This PDF file contains the front matter associated with SPIE Proceedings Volume 9033, including the Title Page, Copyright information, Table of Contents, Invited Panel Discussion, and Conference Committee listing.

Contrast enhancement on cardiac CT provides valuable information about myocardial perfusion and methods have been proposed to assess perfusion with static and dynamic acquisitions. There is a lack of knowledge and consensus on the appropriate approach to ensure 1) sufficient diagnostic accuracy for clinical decisions and 2) low radiation doses for patient safety. This work developed a thorough dynamic CT simulation and several accepted blood flow estimation techniques to evaluate the performance of perfusion assessment across a range of acquisition and estimation scenarios. Cardiac CT acquisitions were simulated for a range of flow states (Flow = 0.5, 1, 2, 3 ml/g/min, cardiac output = 3,5,8 L/min). CT acquisitions were simulated with a validated CT simulator incorporating polyenergetic data acquisition and realistic x-ray flux levels for dynamic acquisitions with a range of scenarios including 1, 2, 3 sec sampling for 30 sec with 25, 70, 140 mAs. Images were generated using conventional image reconstruction with additional image-based beam hardening correction to account for iodine content. Time attenuation curves were extracted for multiple regions around the myocardium and used to estimate flow. In total, 2,700 independent realizations of dynamic sequences were generated and multiple MBF estimation methods were applied to each of these. Evaluation of quantitative kinetic modeling yielded blood flow estimates with an root mean square error (RMSE) of ~0.6 ml/g/min averaged across multiple scenarios. Semi-quantitative modeling and qualitative static imaging resulted in significantly more error (RMSE = ~1.2 and ~1.2 ml/min/g respectively). For quantitative methods, dose reduction through reduced temporal sampling or reduced tube current had comparable impact on the MBF estimate fidelity. On average, half dose acquisitions increased the RMSE of estimates by only 18% suggesting that substantial dose reductions can be employed in the context of quantitative myocardial blood flow estimation. In conclusion, quantitative model-based dynamic cardiac CT perfusion assessment is capable of accurately estimating MBF across a range of cardiac outputs and tissue perfusion states, outperforms comparable static perfusion estimates, and is relatively robust to noise and temporal subsampling.

A new motion estimation and compensation method for cardiac computed tomography (CT) was developed. By combining two motion estimation (ME) approaches the proposed method estimates the local and global cardiac motion and then preforms motion compensated reconstruction. The combined motion estimation method has two parts: one is the local motion estimation, which estimates the coronary artery motion by using coronary artery tree tracking and registration; the other one is the global motion estimation, which estimates the entire cardiac motion estimation by image registration. The final cardiac motion is the linear combination of the coronary artery motion and entire cardiac motion the. We use the backproject-then-warp method proposed by Pack et al. to perform motion compensation reconstruction (MCR). The proposed method was evaluated with 5 patient data and improvements in sharpness of both coronary arteries and heart chamber boundaries were obtained.

We investigated the use of an advanced hybrid iterative reconstruction (IR) technique (iDose4, Philips Health- care) for low dose dynamic myocardial CT perfusion (CTP) imaging. A porcine model was created to mimic coronary stenosis through partial occlusion of the left anterior descending (LAD) artery with a balloon catheter. The severity of LAD occlusion was adjusted with FFR measurements. Dynamic CT images were acquired at end-systole (45% R-R) using a multi-detector CT (MDCT) scanner. Various corrections were applied to the acquired scans to reduce motion and imaging artifacts. Absolute myocardial blood flow (MBF) was computed with a deconvolution-based approach using singular value decomposition (SVD). We compared a high and a low dose radiation protocol corresponding to two different tube-voltage/tube-current combinations (80kV p/100mAs and 120kV p/150mAs). The corresponding radiation doses for these protocols are 7.8mSv and 34.3mSV , respectively. The images were reconstructed using conventional FBP and three noise-reduction strengths of the IR method, iDose. Flow contrast-to-noise ratio, CNR_f, as obtained from MBF maps, was used to quantitatively evaluate the effect of reconstruction on contrast between normal and ischemic myocardial tissue. Preliminary results showed that the use of iDose to reconstruct low dose images provide better or comparable CNR_f to that of high dose images reconstructed with FBP, suggesting significant dose savings. CNR_f was improved with the three used levels of iDose compared to FBP for both protocols. When using the entire 4D dynamic sequence for MBF computation, a 77% dose reduction was achieved, while considering only half the scans (i.e., every other heart cycle) allowed even further dose reduction while maintaining relatively higher CNR_f.

Purpose: To examine the potential for dose reduction in chest CT studies where lesion volume is the primary output (e.g. in therapy-monitoring applications). Methods: We added noise to the raw sinogram data from 15 chest exams with lung lesions to simulate a series of reduced-dose scans for each patient. We reconstructed the reduced-dose data on the clinical workstation and imported the resulting image series into our quantitative imaging database for lesion contouring. One reader contoured the lesions (one per patient) at the clinical reference dose (100%) and 8 simulated fractions of the clinical dose (50, 25, 15, 10, 7, 5, 4, and 3%). Dose fractions were hidden from the reader to reduce bias. We compared clinical and reduced-dose volumes in terms of bias error and variability (4x the standard deviation of the percent differences). Results: Averaging over all lesions, the bias error ranged from -0.6% to 10.6%. Variability ranged from 92% at 3% of clinical dose to 54% at 50% of clinical dose. Averaging over only the smaller lesions (<1cm equivalent diameter), bias error ranged from -9.2% to 14.1% and variability ranged from 125% at 3% dose to 33.9% at 50% dose. Conclusions: The reader’s variability decreased with dose, especially for smaller lesions. However, these preliminary results are limited by potential recall bias, a small patient cohort, and an overly-simplified task. Therapy monitoring often involves checking for new lesions, which may influence the reader’s clinical dose threshold for acceptable performance.

In recent years, iterative algorithms have become popular in diagnostic CT imaging to reduce noise or radiation dose to the patient. The non-linear nature of these algorithms leads to non-linearities in the imaging chain. However, the methods to assess the performance of CT imaging systems were developed assuming the linear process of filtered backprojection (FBP). Those methods may not be suitable any longer when applied to non-linear systems. In order to evaluate the imaging performance, a phantom is typically scanned and the image quality is measured using various indices. For reasons of practicality, cost, and durability, those phantoms often consist of simple water containers with uniform cylinder inserts. However, these phantoms do not represent the rich structure and patterns of real tissue accurately. As a result, the measured image quality or detectability performance for lesions may not reflect the performance on clinical images. The discrepancy between estimated and real performance may be even larger for iterative methods which sometimes produce “plastic-like”, patchy images with homogeneous patterns. Consequently, more realistic phantoms should be used to assess the performance of iterative algorithms. We designed and constructed a biological phantom consisting of porcine organs and tissue that models a human abdomen, including liver lesions. We scanned the phantom on a clinical CT scanner and compared basic image quality indices between filtered backprojection and an iterative reconstruction algorithm.

Iterative image reconstruction methods have been proposed in computed tomography to address two major challenges: one is to reduce radiation dose while maintaining image quality and the other is to reconstruct diagnostic quality images from angularly sparse projection datasets. A variety of regularization models have been introduced in these iterative image reconstruction methods to incorporate the desired image features. To address the sparse view angle image reconstruction problem in four-dimensional cone-beam CT (4DCBCT), Prior Image Constrained Compressed Sensing (PICCS) was proposed. In the past in 4DCBCT, as well as other applications of the PICCS algorithm, the PICCS regularization was formulated using the 1 norm as the means to promote image sparsity. The 1 norm in the objective function is not differentiable and thus may pose challenges in numerical implementations. When the norm deviates from 1.0, the differentiability of the objective function improves, however, the imaging performance may degrade in image reconstruction from sparse datasets. In this paper, we study how the performance of PICCS-4DCBCT changes with norm selection and whether the introduction of a reweighted scheme in relaxed norm PICCS reconstruction helps improve the imaging performance.

The aim of this work is to implement and evaluate a 3D image-based approach for the estimation of scattered radiation in multi-detector CT. Based on a reconstructed CT image volume, the scattered radiation contribution is calculated in 3D fan-beam geometry in the framework of an extended point-scatter kernel (PSK) model of scattered radiation. The PSK model is based on the calculation of elemental scatter contributions propagating the rays from the focal spot to the detector across the object for defined interaction points on a 3D fan beam grid. Each interaction point in 3D leads to an individual elemental 2D scatter distribution on the detector. The sum of all elemental contributions represents the total scatter intensity distribution on the detector. Our proposed extended PSK depends on the scattering angle (defined by the interaction point and the considered detector channel) and the line integral between the interaction point on a 3D fan beam ray and the intersection of the same ray with the detector. The PSK comprises single- and multiple scattering as well as the angular selectivity characteristics of the anti-scatter grid on detector. Our point-scatter kernels were obtained from a low-noise Monte-Carlo simulation of water-equivalent spheres with different radii for a particular CT scanner geometry. The model allows obtaining noise-free scatter intensity distribution estimates with a lower computational load compared to Monte-Carlo methods. In this work, we give a description of the algorithm and the proposed PSK. Furthermore, we compare resulting scatter intensity distributions (obtained for numerical phantoms) to Monte-Carlo results.

X-ray phase-contrast (XPC) imaging methods are well-suited for lung imaging applications due to the weakly absorbing nature of lung tissue and the strong refractive effects associated with tissue-air interfaces. Until recently, XPC lung imaging had only been accomplished at synchrotron facilities. In this work, we investigate the manifestation of speckle in propagation-based XPC images of mouse lungs acquired in situ by use of a benchtop imager. The key contributions of the work are: a) the demonstration that lung speckle can be observed by use of a benchtop XPC imaging system employing a polychromatic tube-source; and b) a systematic experimental investigation of how the texture of the speckle pattern depends on the parameters of the imaging system. Our analyses consists of image texture characterization based on the statistical properties of pixel intensity distributions. Results show how image texture measures of lung regions are strongly dependent on imaging system parameters associated with XPC sensitivity.

Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure that involves sequential grating motion and multiple x-ray exposures to obtain x-ray phase information. Such a data acquisition process breaks the continuous data acquisition into several step-and-shoot data acquisition sequences. Between two neighboring x-ray pulses, the acquisition will have to be stopped for the grating to translate into the next phase stepping position. This setup also requires that the grating not be fixed. If the gratings are to be mounted onto a fast-rotating gantry (such as those used in x-ray CT), this translation of the grating would add another potential source of mechanical instability. To accelerate the data acquisition speed and improve the mechanical stability of of DPCI data acquisitions, a new grating design was developed. In this method, one of the gratings used in DPCI was divided into four-row groups, within each group, grating structures have a designed offset with respect to their neighboring rows. This design allows the acquired data from any adjacent four detector rows to be combined in order to retrieve the needed x-ray differential phase information from a single x-ray exposure. Both numerical simulations and initial phantom experiments have demonstrated that the new interferometer design can enable DPCI image acquisitions without this well-known overhead in data acquisition time.

The demands for a large field-of-view (FOV) and the stringent requirements for a stable acquisition geometry rank among the major obstacles for the translation of grating-based, differential phase-contrast techniques from the laboratory to clinical applications. While for state-of-the-art Full-Field-Digital Mammography (FFDM) FOVs of 24 cm x 30 cm are common practice, the specifications for mechanical stability are naturally derived from the detector pixel size which ranges between 50 and 100 μm. However, in grating-based, phasecontrast imaging, the relative placement of the gratings in the interferometer must be guaranteed to within micro-meter precision. In this work we report on first experimental results on a phase-contrast x-ray imaging system based on the Philips MicroDose L30 mammography unit. With the proposed approach we achieve a FOV of about 65 mm x 175 mm by the use of the slit-scanning technique. The demand for mechanical stability on a micrometer scale was relaxed by the specific interferometer design, i.e., a rigid, actuator-free mount of the phase-grating G₁ with respect to the analyzer-grating G₂ onto a common steel frame. The image acquisition and formation processes are described and first phase-contrast images of a test object are presented. A brief discussion of the shortcomings of the current approach is given, including the level of remaining image artifacts and the relatively inefficient usage of the total available x-ray source output.

In this work, we report on the development of an advanced multi-channel (MC) image reconstruction algorithm for grating-based X-ray phase-contrast computed tomography (GB-XPCT). The MC reconstruction method we have developed operates by concurrently, rather than independently as is done conventionally, reconstructing tomographic images of the three object properties (absorption, small-angle scattering, refractive index). By jointly estimating the object properties by use of an appropriately defined penalized weighted least squares (PWLS) estimator, the 2nd order statistical properties of the object property sinograms, including correlations between them, can be fully exploited to improve the variance vs. resolution tradeoff of the reconstructed images as compared to existing methods. Channel-independent regularization strategies are proposed. To solve the MC reconstruction problem, we developed an advanced algorithm based on the proximal point algorithm and the augmented Lagrangian method. By use of experimental and computer-simulation data, we demonstrate that by exploiting inter-channel noise correlations, the MC reconstruction method can improve image quality in GB-XPCT.

We present a development of the laboratory-based implementation of edge-illumination (EI) x-ray phase contrast imaging (XPCI) that simultaneously enables low-dose and high sensitivity. Lab-based EI-XPCI simplifies the set-up with respect to other methods, as it only requires two optical elements, the large pitch of which relaxes the alignment requirements. Albeit in the past it was erroneously assumed that this would reduce the sensitivity, we demonstrate quantitatively that this is not the case. We discuss a system where the pre-sample mask open fraction is smaller than 50%, and a large fraction of the created beamlets hits the apertures in the detector mask. This ensures that the majority of photons traversing the sample are detected i.e. used for image formation, optimizing dose delivery. We show that the sensitivity depends on the dimension of the part of each beamlet hitting the detector apertures, optimized in the system design. We also show that the aperture pitch does not influence the sensitivity. Compared to previous implementations, we only reduced the beamlet fraction hitting the absorbing septa on the detector mask, not the one falling inside the apertures: the same number of x-rays per second is thus detected, i.e. the dose is reduced, but not at the expense of exposure time. We also present an extension of our phase-retrieval algorithm enabling the extraction of ultra-small-angle scattering by means of only one additional frame, with all three frames acquired within dose limits imposed by e.g. clinical mammography, and easy adaptation to lab-based phase-contrast x-ray microscopy implementations.

Unlike conventional x-ray absorption imaging, x-ray differential phase contrast imaging (DPCI) uses a phase retrieval algorithm to obtain x-ray phase information from a group of x-ray intensity measurements. As a result, the noise performance of DPCI is expected to differ from that of x-ray absorption imaging. Given the total number of x-ray photons used in imaging, lower noise variance in estimated phase contrast images suggests superior dose efficiency, which is one of the most desirable feature in x-ray imaging. When an algorithm is used to retrieve the phase information, it is important to understand what the lowest possible noise variance would be and whether the algorithm used to retrieve the phase information yields the lowest possible noise variance. To address these questions for differential phase contrast imaging, we studied the noise performance of DPC imaging using the powerful Cramér-Rao lower bound (CRLB) in statistical signal estimation method. Results demonstrated that the noise variances in DPCI images obtained by the algorithmic phase retrieval are always higher than the CRLB, which implies a possible sub-optimality of current phase estimation method. The results also call for the need to apply statistical signal estimation theory to DPCI in order to further improve its noise performance and dose efficiency.

In x-ray differential phase contrast imaging (DPCI), a simple sinusoidal model is used to describe the measured signal intensities. When there is no noise added to the measurements, one is able to estimate the three unknown parameters, absorption contrast, phase-contrast, and small angle scatter, directly from the signal model. However, for x-ray detections, noise is always present and the noise level increases with a decrease in x-ray flux. When noise is involved, an experimental ensemble average is needed to make the above parameter estimation method accurate. Unfortunately, an ensemble average requires a large number of measurements acquired under identical experimental conditions. Such repetition procedures would significantly prolong the data acquisition time and require additional radiation dose to the image object. Therefore, it is desirable to develop a reliable parameter estimation method from a group of single x-ray signal intensity measurements. This paper addresses this concern by proposing a statistical signal estimation method. This method maximizes the joint probability of measurements in a complete phase stepping period and is able to estimate the three unknowns in DPCI simultaneously. Our numerical simulations demonstrate that the statistical signal estimation method outperformed the current blind estimation method by minimizing the noise variance of each parameter. As a result, this method has the potential to further improve the dose efficiency of DPCI.

In-line x-ray phase-contrast (XPC) tomosynthesis combines the concepts of tomosynthesis and in-line XPC imaging to utilize the advantages of both for biological imaging applications. Tomosynthesis permits reductions in acquisition times compared with conventional tomography scans while in-line XPC imaging provides high contrast and resolution in images of weakly absorbing materials. In this work, we develop an advanced iterative algorithm as an approach for dealing with the incomplete (and often noisy) data inherent to XPC tomosynthesis. We also investigate the depth resolution properties of XPC tomosynthesis and demonstrate that the z-resolution properties of XPC tomosynthesis is superior to that of conventional absorption-based tomosynthesis. More specifically, we find in-plane structures display strong boundary-enhancement while out-of-plane structures do not. This effect can facilitate the identification of in-plane structures.

With modern CT scanners, detection and classification of coronary artery disease has become a routine applica- tion in cardiac CT. It poses a desirable non–invasive alternative to the invasive coronary angiography, which is the current clinical gold standard. However, the accuracy of cardiac CT depends on the spatial resolution of the imaging system. The limited spatial resolution leads to blooming artifacts, arising from hyper–dense calcification deposits in the arterial walls. This blooming leads to an overestimation of the degree of luminal narrowing and to loss of the morphology of the calcified region. We propose an image–based algorithm, which aims at removing the blooming and estimating the correct CT–value and morphology of the calcification. The method is based on the assumption, that each calcification consists of a compact region which has an almost constant density and attenuation. This knowledge is incorporated into an iterative deconvolution algorithm in image space. We quantitatively assess the accuracy of the proposed algorithm on analytically simulated phantom data. Qualita- tive results of clinical patient data are presented as well. In both cases, the proposed method outperforms the compared algorithms. The initial patient data results are promising. However, an ex vivo study has to be done to confirm the quantitative results of the simulation study with real specimen.

Cardiac C-arm computed tomography (CT) imaging using interventional C-arm systems can be applied in various areas of interventional cardiology ranging from structural heart disease and electrophysiology interventions to valve procedures in hybrid operating rooms. In contrast to conventional CT systems, the reconstruction field of view (FOV) of C-arm systems is limited to a region of interest in cone-beam (along the patient axis) and fan-beam (in the transaxial plane) direction. Hence, highly X-ray opaque objects (e.g. cables from the interventional setup) outside the reconstruction field of view, yield streak artifacts in the reconstruction volume. To decrease the impact of these streaks a cable tracking approach on the 2D projection sequences with subsequent interpolation is applied. The proposed approach uses the fact that the projected position of objects outside the reconstruction volume depends strongly on the projection perspective. By tracking candidate points over multiple projections only objects outside the reconstruction volume are segmented in the projections. The method is quantitatively evaluated based on 30 simulated CT data sets. The 3D root mean square deviation to a reference image could be reduced for all cases by an average of 50 % (min 16 %, max 76 %). Image quality improvement is shown for clinical whole heart data sets acquired on an interventional C-arm system.

In digital radiography imaging, the antiscatter grid is usually employed to obtain clear projected images by absorbing scattered x-ray beams. However, due to the shadow of the stationary antiscatter grid, the grid artifact appears in the obtained x-ray image as stripes if a linear grid is considered. In order to alleviate the grid artifact, band-stop filters (BSFs) can be used and may cause annoying ringing artifact especially for metallic materials. In this paper, in order to reduce the ringing artifact while applying BSFs to alleviate the grid artifact, a spatial prefiltering technique, which is called the gradient-reduction filter, is proposed. The gradient-reduction filter can remove steep edges in the obtained x-ray images especially for metallic materials and can prevent the grid- artifact BSFs from yielding serious ringing artifact. The structure of the proposed filter is similar to that of the Crawford noise-reduction filter. However, the nonlinear function in the proposed filter has decreasing slopes as its input increases contrary to the Crawford filter case. Through extensive experiments for real digital x-ray images, which are obtained from a direct digital radiography detector, we can see the superior ringing-artifact reduction performance comparing to the conventional filtering approaches especially when the metallic materials present in or on the patients.

The ability to customize the incident x-ray fluence in CT via beam-shaping filters or mA modulation is known to improve image quality and/or reduce radiation dose. Previous work has shown that complete control of x-ray fluence (ray-by-ray fluence modulation) would further improve dose efficiency. While complete control of fluence is not currently possible, emerging concepts such as dynamic attenuators and inverse-geometry CT allow nearly complete control to be realized. Optimally using ray-by-ray fluence modulation requires solving a very high-dimensional optimization problem. Most optimization techniques fail or only provide approximate solutions. We present efficient algorithms for minimizing mean or peak variance given a fixed dose limit. The reductions in variance can easily be translated to reduction in dose, if the original variance met image quality requirements. For mean variance, a closed form solution is derived. The peak variance problem is recast as iterated, weighted mean variance minimization, and at each iteration it is possible to bound the distance to the optimal solution. We apply our algorithms in simulations of scans of the thorax and abdomen. Peak variance reductions of 45% and 65% are demonstrated in the abdomen and thorax, respectively, compared to a bowtie filter alone. Mean variance shows smaller gains (about 15%).

Flat field calibration methods are commonly used in computed tomography (CT) to correct for system imperfections. Unfortunately, they cannot be applied in energy-resolving CT when using bow-tie filters owing to spectral distortions imprinted by the filter. This work presents a novel semi-analytical calibration method for photon-counting spectral CT systems, which is applicable with a bow-tie filter in place and efficiently compensates pile-up effects at fourfold increased photon flux compared to a previously published method without degradation of image quality. The achieved reduction of the scan time enabled the first K-edge imaging in-vivo. The method employs a calibration measurement with a set of flat sheets of only a single absorber material and utilizes an analytical model to predict the expected photon counts, taking into account factors such as x-ray spectrum and detector response. From the ratios of the measured x-ray intensities and the corresponding simulated photon counts, a look-up table is generated. By use of this look-up table, measured photon-counts can be corrected yielding data in line with the analytical model. The corrected data show low pixel-to-pixel variations and pile-up effects are mitigated. Consequently, operations like material decomposition based on the same analytical model yield accurate results. The method was validated on a experimental spectral CT system equipped with a bow-tie filter in a phantom experiment and an in-vivo animal study. The level of artifacts in the resulting images is considerably lower than in images generated with a previously published method. First in-vivo K-edge images of a rabbit selectively depict vessel occlusion by an ytterbium-based thermoresponsive polymer.

Nearly all reconstruction methods are controlled through various parameter selections. Traditionally, such parameters are used to specify a particular noise and resolution trade-off in the reconstructed image volumes. The introduction of reconstruction methods that incorporate prior image information has demonstrated dramatic improvements in dose utilization and image quality, but has complicated the selection of reconstruction parameters including those associated with balancing information used from prior images with that of the measurement data. While a noise-resolution tradeoff still exists, other potentially detrimental effects are possible with poor prior image parameter values including the possible introduction of false features and the failure to incorporate sufficient prior information to gain any improvements. Traditional parameter selection methods such as heuristics based on similar imaging scenarios are subject to error and suboptimal solutions while exhaustive searches can involve a large number of time-consuming iterative reconstructions. We propose a novel approach that prospectively determines optimal prior image regularization strength to accurately admit specific anatomical changes without performing full iterative reconstructions. This approach leverages analytical approximations to the implicitly defined prior image-based reconstruction solution and predictive metrics used to estimate imaging performance. The proposed method is investigated in phantom experiments and the shift-variance and data-dependence of optimal prior strength is explored. Optimal regularization based on the predictive approach is shown to agree well with traditional exhaustive reconstruction searches, while yielding substantial reductions in computation time. This suggests great potential of the proposed methodology in allowing for prospective patient-, data-, and change-specific customization of prior-image penalty strength to ensure accurate reconstruction of specific anatomical changes.

In CT imaging, a variety of applications exist where reconstructions are SNR and/or resolution limited. However, if the measured data provide redundant information, composite image data with high SNR can be computed. Generally, these composite image volumes will compromise spectral information and/or spatial resolution and/or temporal resolution. This brings us to the idea of transferring the high SNR of the composite image data to low SNR (but high resolution) ‘source’ image data. It was shown that the SNR of CT image data can be improved using iterative reconstruction [1] .We present a novel iterative reconstruction method enabling optimal dose usage of redundant CT measurements of the same body region. The generalized update equation is formulated in image space without further referring to raw data after initial reconstruction of source and composite image data. The update equation consists of a linear combination of the previous update, a correction term constrained by the source data, and a regularization prior initialized by the composite data. The efficiency of the method is demonstrated for different applications: (i) Spectral imaging: we have analysed material decomposition data from dual energy data of our photon counting prototype scanner: the material images can be significantly improved transferring the good noise statistics of the 20 keV threshold image data to each of the material images. (ii) Multi-phase liver imaging: Reconstructions of multi-phase liver data can be optimized by utilizing the noise statistics of combined data from all measured phases (iii) Helical reconstruction with optimized temporal resolution: splitting up reconstruction of redundant helical acquisition data into a short scan reconstruction with Tam window optimizes the temporal resolution The reconstruction of full helical data is then used to optimize the SNR. (iv) Cardiac imaging: the optimal phase image (‘best phase’) can be improved by transferring all applied over radiation into that image. In all these cases, we show that - at constant patient dose - SNR can efficiently be transferred from the composite data to the source data while maintaining spatial, temporal and contrast resolution properties of the source data.

Statistical iterative image reconstruction has become the subject of strong, active research in X-ray computed tomography (CT), primarily because it may allow a significant reduction in dose imparted to the patient. However, developing an algorithm that converges fast while allowing parallelization so as to obtain a product that can be used routinely in the clinic is challenging. In this work, we present a novel algorithm that combines the strength of two popular methods. A preliminary investigation of this algorithm was performed, and strongly encouraging initial results are reported.

Iterative image reconstruction gains more and more interest in clinical routine, as it promises to reduce image noise, to reduce artifacts, or to improve spatial resolution. Among vendors and researchers, however, there is no consensus of how to best achieve these aims. The general approach is to incorporate a priori knowledge into iterative image reconstruction for example by adding additional constraints to the cost function, which penalize strong variations between neighboring voxels. However this approach to regularization in general poses a resolution noise trade–off because the stronger the regularization, and thus the noise reduction, the stronger the loss of spatial resolution and thus loss of anatomical detail. We propose a method which tries to improve this trade-off. One starts with generating basis images, which emphasize certain desired image properties, like high resolution or low noise. The proposed reconstruction algorithm reconstructs voxel–specific weighting coefficients that are applied to combine the basis images. By combining the desired properties of each basis image one can generate an image with lower noise and maintained high contrast resolution thus improving the resolution noise trade–off.

Polychromatic statistical reconstruction algorithms have very high computational demands due to the difficulty of the optimization problems and the large number of spectrum bins. We want to develop a more practical algorithm that has a simpler optimization problem, a faster numerical solver, and requires only a small amount of prior knowledge. In this paper, a modified optimization problem for polychromatic statistical reconstruction algorithms is proposed. The modified optimization problem utilizes the idea of determining scanned materials based on a first pass FBP reconstruction to fix the ratios between photoelectric and Compton scattering components of all image pixels. The reconstruction of a density image is easy to solve by a separable quadratic surrogate algorithm that is also applicable to the multi-material case. In addition, a spectrum binning method is introduced so that the full spectrum information is not required. The energy bins sizes and attenuations are optimized based on the true spectrum and object. With these approximations, the expected line integral values using only a few energy bins are very closed to the true polychromatic values. Thus both the problem size and computational demand caused by the large number of energy bins that are typically used to model a full spectrum are reduced. Simulation showed that three energy bins using the generalized spectrum binning method could provide an accurate approximation of the polychromatic X-ray signals. The average absolute error of the logarithmic detector signal is less than 0.003 for a 120 kVp spectrum. The proposed modified optimization problem and spectrum binning approach can effectively suppress beam hardening artifacts while providing low noise images.

The short-scan Feldkamp David Kress (FDK) method for C-arm CT reconstruction involves a heuristic raybased weighting scheme to handle data redundancies. This scheme is known to be approximate under general circumstances and it often creates low frequency image artifacts in regions away from the central axial plane. Alternative algorithms, such as the one proposed by Defrise and Clack (DC),1 can handle data redundancy in a theoretically exact manner and thus notably improve image quality. The DC algorithm, however, is computationally more complex than FDK, as it requires a shift-variant 2D filtering of the data instead of a efficient 1D filtering. In this paper, a modification of the original DC algorithm is investigated, which applies the efficient FDK filtering scheme whereever possible and the DC filtering scheme only where it is required. This modification leads to a more efficient implementation of the DC algorithm, in which filtering effort can be reduced by up to about 70%, dependent on the specific geometry set-up. This gain in computation speed makes the DC method even more attractive for use in an interventional environment, where fast and interactive X-ray imaging is a crucial requirement.

Statistical Image Reconstruction (SIR) often involves a balance of two requirements: the first requirement is enforcing a minimal difference between the forward projection of the reconstructed image with the measured projection data and the second requirement enforcing some kind of image smoothness, which depends on the specific selection of regularizer, to reduce the noise in the reconstructed image. The needed delicate balance between these two requirements in the numerical implementations often slow down the reconstruction speed due to either a degradation in convergence rate of the algorithm or a degradation of parallellizability of the numerical implementation algorithms. In this work, a general numerical implementation strategy has been proposed to allow the SIR algorithms to be implemented in two decoupled and alternating steps. The first step using SIR without any regularizer which allows for the use of the well-known ordered subset (OS) strategy to accelerate the image reconstruction. The second step solves a denoising problem without involving the data fidelity term. The alternation of these two decoupled steps enable one to perform SIR with both high convergence rate and high parallellizability. The total variation norm of the image has been used as an example of regularizers to illustrate the proposed numerical implementation strategy. Numerical simulations have been performed to validate the proposed algorithm. The noise-spatial resolution tradeoff curve and convergence speed of the algorithm have been investigated and compared against the conventional gradient descent based implementation strategy.

Model-based iterative reconstruction (MBIR) is an emerging technique for several imaging modalities and appli- cations including medical CT, security CT, PET, and microscopy. Its success derives from an ability to preserve image resolution and perceived diagnostic quality under impressively reduced signal level. MBIR typically uses a cost optimization framework that models system geometry, photon statistics, and prior knowledge of the recon- structed volume. The challenge of tomosynthetic geometries is that the inverse problem becomes more ill-posed due to the limited angles, meaning the volumetric image solution is not uniquely determined by the incom- pletely sampled projection data. Furthermore, low signal level conditions introduce additional challenges due to noise. A fundamental strength of MBIR for limited-views and limited-angle is that it provides a framework for constraining the solution consistent with prior knowledge of expected image characteristics. In this study, we analyze through simulation the capability of MBIR with respect to prior modeling components for limited-views, limited-angle digital breast tomosynthesis (DBT) under low dose conditions. A comparison to ground truth phantoms shows that MBIR with regularization achieves a higher level of fidelity and lower level of blurring and streaking artifacts compared to other state of the art iterative reconstructions, especially for high contrast objects. The benefit of contrast preservation along with less artifacts may lead to detectability improvement of microcalcification for more accurate cancer diagnosis.

We design an iterative image reconstruction (IIR) algorithm for enhancing tissue structure contrast. The algorithm takes advantage of a data fidelity term, which compares the derivative of the DBT projections with the derivative of the estimated projections. This derivative data fidelity is sensitive to the edges of tissue structure projections, and as a consequence minimizing the corresponding the data-error term brings out structure information in the reconstructed volumes. The method has the practical advantages that few iterations are required and that direct region-of-interest (ROI) reconstruction is possible with the proposed derivative data fidelity term. Both of these advantages reduce the computational burden of the IIR algorithm and potentially make it feasible for clinical application. The algorithm is demonstrated on clinical DBT data.

Compressed sensing (CS) [1] is a novel sensing (acquisition) paradigm that applies to discrete-to-discrete system models and asserts exact recovery of a sparse signal from far fewer measurements than the number of unknowns [1- 2]. Successful applications of CS may be found in MRI [3, 4] and optical imaging [5]. Sparse reconstruction methods exploiting CS principles have been investigated for CT [6-8] to reduce radiation dose, and to gain imaging speed and image quality in optical imaging [9]. In this work the objective is to investigate the applicability of compressed sensing principles for a faster brain imaging protocol on a hybrid collimator SPECT system. As a proofof- principle we study the null space of the fan-beam collimator component of our system with regards to a particular imaging object. We illustrate the impact of object sparsity on the null space using pixel and Haar wavelet basis functions to represent a piecewise smooth phantom chosen as our object of interest.

Graphical analysis is employed in the research setting to provide quantitative estimation of PET tracer kinetics from dynamic images at a single bed. Recently, we proposed a multi-bed dynamic acquisition framework enabling clinically feasible whole-body parametric PET imaging by employing post-reconstruction parameter estimation. In addition, by incorporating linear Patlak modeling within the system matrix, we enabled direct 4D reconstruction in order to effectively circumvent noise amplification in dynamic whole-body imaging. However, direct 4D Patlak reconstruction exhibits a relatively slow convergence due to the presence of non-sparse spatial correlations in temporal kinetic analysis. In addition, the standard Patlak model does not account for reversible uptake, thus underestimating the influx rate Ki. We have developed a novel whole-body PET parametric reconstruction framework in the STIR platform, a widely employed open-source reconstruction toolkit, a) enabling accelerated convergence of direct 4D multi-bed reconstruction, by employing a nested algorithm to decouple the temporal parameter estimation from the spatial image update process, and b) enhancing the quantitative performance particularly in regions with reversible uptake, by pursuing a non-linear generalized Patlak 4D nested reconstruction algorithm. A set of published kinetic parameters and the XCAT phantom were employed for the simulation of dynamic multi-bed acquisitions. Quantitative analysis on the K_i images demonstrated considerable acceleration in the convergence of the nested 4D whole-body Patlak algorithm. In addition, our simulated and patient whole-body data in the postreconstruction domain indicated the quantitative benefits of our extended generalized Patlak 4D nested reconstruction for tumor diagnosis and treatment response monitoring.

Scatter in cone-beam computed tomography (CBCT) is a significant problem that degrades image contrast, uniformity and CT number accuracy. One means of estimating and correcting for detected scatter is through an iterative deconvolution process known as scatter kernel superposition (SKS). While the SKS approach is efficient, clinically significant errors on the order 2-4% (20-40 HU) still remain. We have previously shown that the kernel method can be improved by perturbing the kernel parameters based on reference data provided by limited Monte Carlo simulations of a first-pass reconstruction. In this work, we replace the Monte Carlo modeling with a deterministic Boltzmann solver (AcurosCTS) to generate the reference scatter data in a dramatically reduced time. In addition, the algorithm is improved so that instead of adjusting kernel parameters, we directly perturb the SKS scatter estimates. Studies were conducted on simulated data and on a large pelvis phantom scanned on a tabletop system. The new method reduced average reconstruction errors (relative to a reference scan) from 2.5% to 1.8%, and significantly improved visualization of low contrast objects. In total, 24 projections were simulated with an AcurosCTS execution time of 22 sec/projection using an 8-core computer. We have ported AcurosCTS to the GPU, and current run-times are approximately 4 sec/projection using two GPU’s running in parallel.

This paper provides a fast and patient-specific scatter artifact correction method for cone-beam computed tomography (CBCT) used in image-guided interventional procedures. Due to increased irradiated volume of interest in CBCT imaging, scatter radiation has increased dramatically compared to 2D imaging, leading to a degradation of image quality. In this study, we propose a scatter artifact correction strategy using an analytical convolution-based model whose free parameters are estimated using a rough estimation of scatter profiles from the acquired cone-beam projections. It was evaluated using Monte Carlo simulations with both monochromatic and polychromatic X-ray sources. The results demonstrated that the proposed method significantly reduced the scatter-induced shading artifacts and recovered CT numbers.

The performance of a novel designed x-ray CT scanning geometry is investigated. Composed of a specially designed tungsten collimation mask and a high resolution flat panel detector, this scanning geometry provides high efficient data acquisition allowing dose reduction potentially up to 50%. In recent years a special type of scanning geometry has been proposed. A first prototype of this geometry called CTDOR( CT with Dual Optimal Reading) has already been built. Despite many drawbacks, resulting images have shown promising potential of dual reading. The approach of gaining two subsets of data has anew been picked up and come to terms with a novel designed CT scanner for breast imaging. The main idea consists of collimating the X-ray beam through a specially designed shielding mask thereby reducing radiation dose without compromising image quality. This is achieved by hexagonally sampled Radon transform and image reconstruction with the especially suitable OPED (orthogonal polynomial expansion on disk) algorithm. This work now presents the development and evaluation of the novel designed breast CT system. Therefore simulated phantom data were obtained to test the performance of the scanning device and compared to a standard 3rd generation scanner. Retaining advantages such as scatter-correction potential and 3D-capability, the proposed CT system yields high resolution images for breast diagnostics in low energy ranges. Assuming similar sample size, it is expected that the novel designed breast CT system in conjunction with OPED outperforms the standard 3rd generation CT system combined with FBP (filtered back projection).

We previously introduced three different fiducial marker-based correction methods (2D projection shifting, 2D projection warping, and 3D image warping) for patients’ involuntary motion in the lower body during weight-bearing Carm CT scanning. The 3D warping method performed better than 2D methods since it could more accurately take into account the lower body motion in 3D. However, as the 3D warping method applies different rotational and translational movement to the reconstructed image for each projection frame, distance-related weightings were slightly twisted and thus result in overlaying background noise over the entire image. In order to suppress background noise and artifacts (e.g. metallic marker-caused streaks), the 3D warping method has been improved by incorporating bilateral filtering and a Landwebertype iteration in one step. A series of projection images of five healthy volunteers standing at various flexion angles were acquired using a C-arm cone-beam CT system with a flat panel. A horizontal scanning trajectory of the C-arm was calibrated to generate projection matrices. Using the projection matrices, the static reference marker coordinates in 3D were estimated and used for the improved 3D warping method. The improved 3D warping method effectively reduced background noise down below the noise level of 2D methods and also eliminated metal-generated streaks. Thus, improved visibility of soft tissue structures (e.g. fat and muscle) was achieved while maintaining sharp edges at bone-tissue interfaces. Any high resolution weight-bearing cone-beam CT system can apply this method for motion compensation.

Projection images from digital breast tomosynthesis acquisitions can contain a large fraction of scattered x-rays due to the absence of an anti-scatter grid in front of the detector. In order to produce quantitative results, this should be accounted for in reconstruction algorithms. We examine the possible improvement in signal difference to noise ratio (SDNR) for low contrast spherical densities when applying a scatter correction algorithm. Hybrid patient data were created by combining real patient data with attenuation profiles of spherical masses acquired with matching exposure settings. Scatter in these cases was estimated using Monte-Carlo based scatter- ing kernels. All cases were reconstructed using filtered backprojection (FBP) with and without beam hardening correction and two maximum likelihood methods for transmission tomography, with and without quadratic smoothing prior (MAPTR and MLTR). For all methods, images were reconstructed without scatter correction, and with scatter precorrection, and for the iterative methods also with an adjusted update step obtained by including scatter in the physics model. SDNR of the inserted spheres was calculated by subtracting the recon- structions with and without inserted template to measure the signal difference, while noise was measured in the image containing the template. SDNR was significantly improved by 3.5% to 4.5% (p < 0.0001) at iteration 10 for both correction methods applied to the MLTR and MAPTR reconstructions. For MLTR these differences disappeared by iteration 100. For regular FBP SDNR remained the same after correction (p = 0.60) while it dropped slightly for FBP with beam hardening correction (-1.4%, p = 0.028). These results indicate that for the iterative methods, application of a scatter correction algorithm has very little effect on the SDNR, it only causes a slight decrease in convergence speed, which is similar for precorrection and correction incorporated in the update step. The FBP results were unchanged because the scatter being corrected is a low frequency component in the projection images, and this information is mostly ignored in the reconstruction due to the high pass filter.

Digital breast tomosynthesis (DBT) is a technique developed to overcome the limitations of conventional digital mammography by reconstructing slices through the breast from projections acquired at different angles. In developing and optimizing DBT, the x-ray scatter reduction technique remains a significant challenge due to projection geometry and radiation dose limitations. The most common approach for scatter reduction technique is a beam-stop-array (BSA) algorithm while this method has a concern of additional exposure to acquire the scatter distribution. The compressed breast is roughly symmetry and the scatter profiles from projection acquired at axially opposite angle are similar to mirror image from each other. The purpose of this study was to apply the BSA algorithm acquiring only two scans with a beam stop array, which estimates scatter distribution with minimum additional exposure. The results of scatter correction with angular interpolation were comparable to those of scatter correction with all scatter distributions at each angle and exposure increase was less than 13%. This study demonstrated the influence of scatter correction by BSA algorithm with minimum exposure which indicates the practical application in clinical situations.

In cone beam computed tomography (CT), it is common practice to use the Defrise phantom for image quality assessment. The phantom consists of a stack of plastic plates with low frequency spacing. Because the x-ray beam may traverse multiple plates, the spacing between plates can appear blurry in the reconstruction, and hence modulation provides a measure of image quality. This study considers the potential merit of using the Defrise phantom in digital breast tomosynthesis (DBT), a modality with a smaller projection range than CT. To this end, a Defrise phantom was constructed and subsequently imaged with a commercial DBT system. It was demonstrated that modulation is dependent on position and orientation in the reconstruction. Modulation is preserved over a broad range of positions along the chest wall if the input frequency is oriented in the tube travel direction. By contrast, modulation is degraded with increasing distance from the chest wall if the input frequency is oriented in the posteroanterior (PA) direction. A theoretical framework was then developed to model these results. Reconstructions were calculated in an acquisition geometry designed to improve modulation. Unlike current geometries in which the x-ray tube motion is restricted to the plane of the chest wall, we consider a geometry with an additional component of tube motion along the PA direction. In simulations, it is shown that the newly proposed geometry improves modulation at positions distal to the chest wall. In conclusion, this study demonstrates that the Defrise phantom is a tool for optimizing DBT systems.

Current digital breast tomosynthesis (DBT) systems have been shown to have diminished microcalcification (MC) visibility compared to 2D mammography systems. Rotating gantry DBT systems require mechanical motion of the X-ray source which causes motion blurring of the focal spot, thus reducing spatial resolution. We have developed a stationary DBT (s-DBT) technology that uses a carbon nanotube (CNT) based X-ray source array in order to acquire all the projections images without any mechanical motion. It is capable of producing full tomosynthesis datasets with zero motion blur. It has been shown to have significantly higher spatial resolution than continuous motion DBT systems. An s-DBT system also allows for a wider angular span without increasing the acquisition time. A larger angular span covers a larger portion of the Fourier domain, thus decreasing the tissue overlap. In this study, we compare tomosynthesis imaging of MCs, in lumpectomy specimens, between an s-DBT system and a rotating gantry DBT system. Results show that s-DBT produces better MC sharpness and reduced tissue overlap compared to continuous motion DBT systems.

We have recently demonstrated the feasibility of stationary digital chest tomosynthesis (s-DCT) using a dis- tributed carbon nanotube x-ray source array. The technology has the potential to increase the imaging resolution and speed by eliminating source motion. In addition, the flexibility in the spatial configuration of the individual sources allows new tomosynthesis imaging geometries beyond the linear scanning mode used in the conventional systems. In this paper, we report the preliminary results on the effects of the tomosynthesis imaging geometry on the image quality. The study was performed using a bench-top s-DCT system consisting of a CNT x-ray source array and a flat-panel detector. System MTF and ASF are used as quantitative measurement of the in-plane and in-depth resolution. In this study geometries with the x-ray sources arranged in linear, square, rectangular and circular configurations were investigated using comparable imaging doses. Anthropomorphic chest phantom images were acquired and reconstructed for image quality assessment. It is found that wider angular coverage results in better in-depth resolution, while the angular span has little impact on the in-plane resolution in the linear geometry. 2D source array imaging geometry leads to a more isotropic in-plane resolution, and better in-depth resolution compared to 1D linear imaging geometry with comparable angular coverage.

Possible advantages of energy-selective photon counting detectors compared to dual energy CT shall be evaluated in the case of a typical dual energy application: Image-based material decomposition into an iodine and a water material image. Apart from a possibly smaller spectral overlap between the low and the high energy information, a photon counting detector will probably offer more than the two necessary energy bins. In this case additional degrees of freedom are gained that allow minimizing the noise in the material images. We propose an image-based method that determines optimal bin image weighting factors for material decomposition with respect to minimal material image noise. Results indicate that a perfect photon counting detector with four bins outperforms the dual energy CT technique by a noise reduction of 22% in the water image and 43% in the iodine image. Limited detector energy resolution has only a small impact on the achieved noise reduction, the photon counting detector performs still better than the dual energy technique. However, if degrading detector effects like charge sharing and K-escape peaks are taken into account, the performance of our proposed method drops below the one of conventional dual energy CT.

We present a fast, noise-efficient, and accurate estimator for material separation using photon-counting x-ray detectors (PCXDs) with multiple energy bin capability. The proposed targeted least squares estimator (TLSE) improves a previously proposed A-Table method by incorporating dynamic weighting that allows noise to be closer to the Cramér- Rao Lower Bound (CRLB) throughout the operating range. We explore Cartesian and average-energy segmentation of the basis material space for TLSE, and show that iso-average-energy contours require fewer segments compared to Cartesian segmentation to achieve similar performance. We compare the iso-average-energy TLSE to other proposed estimators - including the gold standard maximum likelihood estimator (MLE) and the A-Table1 - in terms of variance, bias and computational efficiency. The variance and bias of this estimator between 0 to 6 cm of aluminum and 0 to 50 cm of water is simulated with Monte Carlo methods. Iso-average energy TLSE achieves an average variance within 2% of CRLB, and mean of absolute error of (3.68 ± 0.06) x 10^-6 cm. Using the same protocol, MLE showed variance-to- CRLB ratio and average bias of 1.0186 ± 0.0002 and (3.10 ± 0.06) x 10^-6 cm, respectively, but was 50 times slower in our simulation. Compared to the A-Table method, TLSE gives a more homogenous variance-to-CRLB profile in the operating region. We show that variance-to-CRLB for TLSE is lower by as much as ~36% than A-Table method in the peripheral region of operation (thin or thick objects). The TLSE is a computationally efficient and fast method for implementing material separation technique in PCXDs, with performance parameters comparable to the MLE.

Photon counting detectors used in spectroscopic CT are often based on small pixels and therefore offer only limited space to include energy discriminators and their associated counters in each pixel cell. For this reason, it is important to make efficient use of the available energy discriminators in order to achieve an optimized material contrast at a radiation dose as low as possible. Unfortunately, the complexity of evaluating every possible combination of energy thresholds, given a fixed number of counters, rapidly increases with the resolution at which this search is performed, and makes brute-force approaches to this problem infeasible. In this work, we introduce methods from machine learning, in particular sparse regression, to perform a feature selection to determine optimal combinations of energy thresholds. We will demonstrate how methods enforcing row-sparsity on a linear regression’s coefficient matrix can be applied to the multiple response problem in spectroscopic CT, i.e. the case in which a single set of energy thresholds is sought to simultaneously retrieve concentrations pertaining to a multitude of materials in an optimal way. These methods are applied to CT images experimentally obtained with a Medipix3RX detector operated in charge summing mode and with a CdTe sensor at a pixel pitch of 110μm. We show that the least absolute shrinkage and selection operator (lasso), generalized to the multiple response case, chooses four out of 20 possible threshold positions that allow discriminating PMMA, iodine and gadolinium in a contrast agent phantom at a higher accuracy than with equally spaced thresholds. Finally, we illustrate why it might be unwise to use a higher number of energy thresholds than absolutely necessary.

Spectral CT with photon-counting detectors has the potential to improve material decomposition and contrastto- noise ratio (CNR) compared to conventional CT. This work compared the noise properties of two general energy-bin acquisition methods: (1) energy bins acquired from the same spectrum noise realization, and (2) energy bins acquired from different spectrum noise realizations. For both types of acquisitions, the detected number of counts per bin was simulated and measured on a bench-top system. The energy-bin noise standard deviation was compared for both acquisition methods. Simulations were performed to compare both methods with respect to noise in material decomposition estimates and the CNR in image-based weighted images. Both the experimental and simulation results indicated increased energy-bin noise when energy measurements were acquired from different spectrum realizations. The noise increased by a factor of 2 for the lowest energy bin, with the noise penalty decreasing with increasing bin energy. The simulation results demonstrated a factor of 1.2 to 2 increase in noise in material decomposition estimates when acquiring from different spectrum realizations. Despite the increased energy-bin noise, energy measurements from different spectrum realizations increased the CNR in image-based-weighted images by 10%, potentially due to noise correlations across bins. Overall, the investigated acquisition methods demonstrated differences in noise standard deviation, affecting material decomposition estimates and CNR.

The energy-selectivity of photon counting detectors provides contrast enhancement and enables new material-identification techniques for clinical Computed Tomography (CT). Patient dose considerations and the resulting requirement of efficient X-ray detection suggest the use of CdTe or CdZnTe as detector material. The finite signal pulse duration of several nanoseconds present in those detectors requires strong reduction of the pixel size to achieve feasible count rates in the high-flux regime of modern CT scanners. Residual pulse pile-up effects in scans with high X-ray fluxes still can limit two key properties of the counting detector, namely count-rate linearity and spectral linearity. We have used our research prototype scanner with CdTe-based counting detector and 225μm small pixels to investigate these effects in CT imaging scenarios at elevated X-ray tube currents. We present measurements of CT images and provide a detailed analysis of contrast stability, image noise and multi-energy performance achieved with different phantom sizes at various X-ray tube settings.

We present investigations into direct, calibration-free recovery of distorted spectral x-ray measurements with the Medipix 2 detector. Spectral x-ray measurements using pixelated photon counting spectral x-ray detectors are subject to significant spectral distortion. For detectors with small pixel size, charge sharing between adjacent electrodes often dominates this distortion. In material decomposition applications, a popular spectral recovery technique employs a calibration phantom with known spectral properties. This works due to the similarity of the attenuation properties of the phantom and the material to be studied. However, this approach may be too simplistic for clinical imaging applications as it assumes the homogeneity (and knowledge) of exactly the properties whose variation accounts entirely for the diagnostic content of the spectral data obtained by the photon counting detector. It may also be difficult to find the right calibration phantom for varying patient size and tissue densities on a case-by-case basis. Thus, it is desirable to develop direct correction strategies, based on the objectively measurable response of the detector. We model analytically the distortion of a spectral signal in a PCSXD by applying Gaussian broadening and a charge-sharing model. The model parameters are fitted to the measured fluorescence of several metals. While we are investigating the methodology using Medipix detectors, it should be applicable to other PCXDs as well.

In x-ray imaging, contrast information content varies with photon energy. It is therefore possible to improve image quality by weighting photons according to energy. We have implemented and evaluated so-called energy weighting on a commercially available spectral photon-counting mammography system. A practical formula for calculating the optimal weight from pixel values was derived. Computer simulations and phantom measurements revealed that the contrast-tonoise ratio was improved by 3%–5%, and automatic image analysis showed that the improvement was detectable in a set of screening mammograms.

Spectral X-ray imaging allows to differentiate between two given tissue types, provided their spectral absorption characteristics differ measurably. In mammography, this method is used clinically to determine a decomposition of the breast into adipose and glandular tissue compartments, from which the glandular tissue fraction and, hence, the volumetric breast density (VBD) can be computed. Another potential application of this technique is the characterization of lesions by spectral mammography. In particular, round lesions are relatively easily detected by experienced radiologists, but are often difficult to characterize. Here, a method is described that aims at discriminating cystic from solid lesions directly on a spectral mammogram, obtained with a calibrated spectral mammography system and using a hypothesis-testing algorithm based on a maximum likelihood approach. The method includes a parametric model describing the lesion shape, compression height variations and breast composition. With the maximum likelihood algorithm, the model parameters are estimated separately under the cyst and solid hypothesis. The resulting ratio of the maximum likelihood values is used for the final tissue characterization. Initial results using simulations and phantom measurements are presented.

We have developed a high resolution amorphous selenium (a-Se) direct detection imager using a large-area compatible back-end fabrication process on top of a CMOS active pixel sensor having 25 micron pixel pitch. Integration of a-Se with CMOS technology requires overcoming CMOS/a-Se interfacial strain, which initiates nucleation of crystalline selenium and results in high detector dark currents. A CMOS-compatible polyimide buffer layer was used to planarize the backplane and provide a low stress and thermally stable surface for a-Se. The buffer layer inhibits crystallization and provides detector stability that is not only a performance factor but also critical for favorable long term cost-benefit considerations in the application of CMOS digital x-ray imagers in medical practice. The detector structure is comprised of a polyimide (PI) buffer layer, the a-Se layer, and a gold (Au) top electrode. The PI layer is applied by spin-coating and is patterned using dry etching to open the backplane bond pads for wire bonding. Thermal evaporation is used to deposit the a-Se and Au layers, and the detector is operated in hole collection mode (i.e. a positive bias on the Au top electrode). High resolution a-Se diagnostic systems typically use 70 to 100 μm pixel pitch and have a pre-sampling modulation transfer function (MTF) that is significantly limited by the pixel aperture. Our results confirm that, for a densely integrated 25 μm pixel pitch CMOS array, the MTF approaches the fundamental material limit, i.e. where the MTF begins to be limited by the a-Se material properties and not the pixel aperture. Preliminary images demonstrating high spatial resolution have been obtained from a frst prototype imager.

In order to predict and improve the performance of pixelated detectors, it is important to understand the optical properties of the basic unit of the scintillating structure in the detector. To measure one of the essential optical properties, reflectance, we have used a device composed of a laser and photodiode array. We have also developed an analytical model of the optical phenomena based on Snell's law and the Fresnel equations to simply analyze measured results and reflectance parameters at the interface. The computed and experimentally measured results typically have good agreement, validating the analytical model and measurements. The optical parameters are used as inputs to GEANT4 [1]. The simulations are then leveraged to optimize an imager design before a prototype is built. The optical reflectance was measured by using relatively inexpensive samples. A sample has scintillator, glue, and septum (reflector) layers, and each sample has a different scintillator surface (polished/rough) and/or reflector [ESR film/aluminum-sputtered (coated) ESR film] condition. A high-refractive-index hemisphere was attached on the top surface of a sample to increase the maximum incidence angle at the scintillator-glue interface from 27° to 52°. The sample including ESR film demonstrated average reflectance approximately 1.3 times higher than that from the sample with aluminum-sputtered ESR film as a reflector, and the polished surface condition showed higher reflectance than the rough-cut surface condition.

The thin-film semiconductor processing methods that enabled creation of inexpensive liquid crystal displays based on amorphous silicon transistors for cell phones and televisions, as well as desktop, laptop and mobile computers, also facilitated the development of devices that have become ubiquitous in medical x-ray imaging environments. These devices, called active matrix flat-panel imagers (AMFPIs), measure the integrated signal generated by incident X rays and offer detection areas as large as ~43×43 cm². In recent years, there has been growing interest in medical x-ray imagers that record information from X ray photons on an individual basis. However, such photon counting devices have generally been based on crystalline silicon, a material not inherently suited to the cost-effective manufacture of monolithic devices of a size comparable to that of AMFPIs. Motivated by these considerations, we have developed an initial set of small area prototype arrays using thin-film processing methods and polycrystalline silicon transistors. These prototypes were developed in the spirit of exploring the possibility of creating large area arrays offering single photon counting capabilities and, to our knowledge, are the first photon counting arrays fabricated using thin film techniques. In this paper, the architecture of the prototype pixels is presented and considerations that influenced the design of the pixel circuits, including amplifier noise, TFT performance variations, and minimum feature size, are discussed.

A novel x-ray fluorescence imaging setup for the in vivo detection of high-Z tracer distributions is investigated for its application in molecular imaging. The setup uses an energy resolved detection method based on a Bragg reflecting analyzer array together with a multiple scatter reducing radial collimator. The aim of this work is to investigate the potential application of this imaging method to in vivo imaging in humans. A proof of principle experiment modeling a partial setup for the detection of gold nano-particles was conducted in order to test the feasibility of the proposed imaging method. Furthermore a Monte Carlo simulation of the complete setup was created in order to quantify the dependence of the image quality on the applied radiation dose and on the geometrical collimator parameters as well as on the analyzer crystal parameters. The Monte Carlo simulation quantifies the signal-to-noise ratio per radiation dose and its dependence on the collimator parameters. Thereby the parameters needed for a dose efficient in vivo imaging of gold nano-particle based tracer distributions are quantified. However also a number of problems are found like the fluorescence emission as well as scatter from the collimator material obscuring the tracer fluorescence and the potentially large scan time.

Gold nanoparticles (GNPs) were demonstrated as X-ray imaging contrast agents and radiosensitizers in mice. However, the translational medical applications of GNPs in to the clinical practice need further detailed information on the biological effects related to the enhanced doses in malignant and healthy cells. The idea of improving radiotherapy with high atomic number materials, especially gold foils, was initiated in our research unit in the 1980s. Recently, experimental and theoretical efforts were made to investigate the potential improvement of imaging and radiotherapy with GNPs. Initially, the present work attempts to validate the dose enhancement effects of GNPs to cancer cells; secondly, it intends to examine the possible side effects on healthy cells when using GNPs as X-ray contrast agent. In this study, three Monte Carlo simulation programs, namely PENELOPE-2011, GEANT4 and EGSnrc were used to simulate the local energy deposition and the resulting dose enhancement of GNPs. Diameters of the GNPs were assumed to be 2 nm, 15 nm, 50 nm, 100 nm and 200 nm. The X-ray energy spectra for irradiation were 60 kVp, 80 kVp, 100 kVp, 150 kVp with a filtering of 2.7 mm Al for projectional radiography, and 8 mm Al for 100 kVp and 150 kVp for computed tomography. Additional peak energy of 200 kVp was simulated for radiotherapy purpose. The information of energy deposition and dose enhancement can help understanding the physical processes of medical imaging and the implication of nanoparticles in radiotherapy.

We use propagation-based phase-contrast X-ray imaging with gas as contrast agent to visualize the microvasculature in small animals like mice and rats. The radiation dose required for absorption X-ray imaging is proportional to the minus fourth power of the structure size to be detected. This makes small vessels impossible to image at reasonable radiation doses using the absorption of conventional iodinated contrast agents. Propagation-based phase contrast gives enhanced contrast for high spatial frequencies by moving the detector away from the sample to let phase variations in the transmitted X-rays develop into intensity variations at the detector. Blood vessels are normally difficult to observe in phase contrast even with iodinated contrast agents as the density difference between blood and most tissues is relatively small. By injecting gas into the blood stream this density difference can be greatly enhanced giving strong phase contrast. One possible gas to use is carbon dioxide, which is a clinically accepted X-ray contrast agent. The gas is injected into the blood stream of patients to temporarily displace the blood in a region and thereby reduce the X-ray absorption in the blood vessels. We have shown that this method can be used to image blood vessels down to 8 μm in diameter in mouse ears. The low dose requirements of this method indicate a potential for live small-animal imaging and longitudinal studies of angiogenesis.

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of morbidity and mortality worldwide and emphysema is one of its main components. The disorder is characterized by irreversible destruction of the alveolar walls and enlargement of distal airspaces. Despite the severe changes in the lung tissue morphology, conventional chest radiographs have only a limited sensitivity for the detection of mild to moderate emphysema. X-ray dark-field is an imaging modality that can significantly increase the visibility of lung tissue on radiographic images. The dark-field signal is generated by coherent, small-angle scattering of x-rays on the air-tissue interfaces in the lung. Therefore, morphological changes in the lung can be clearly visualized on dark-field images. This is demonstrated by a preclinical study with a small-animal emphysema model. To generate a murine model of pulmonary emphysema, a female C57BL/6N mouse was treated with a single orotracheal application of porcine pancreatic elastase (80 U/kg body weight) dissolved in phosphate-buffered saline (PBS). Control mouse received PBS. The mice were imaged using a small-animal dark-field scanner. While conventional x-ray transmission radiography images revealed only subtle indirect signs of the pulmonary disorder, the difference between healthy and emphysematous lungs could be clearly directly visualized on the dark-field images. The dose applied to the animals is compatible with longitudinal studies. The imaging results correlate well with histology. The results of this study reveal the high potential of dark-field radiography for clinical lung imaging.

Compton scattering is a dominant interaction during radiography and computed tomography x-ray imaging. However, the scattered photons are not used for extracting imaging information, but seriously degrade image quality. Here we introduce a new scheme that overcomes most of the problems associated with existing Compton scattering imaging schemes and allows Compton scattered photons to be effectively used for imaging. In our scheme, referred as Compton coincidence volumetric imaging (CCVI), a collimated monoenergetic x-ray beam is directed onto a thin semiconductor detector. A small portion of the photons is Compton scattered by the detector and their energy loss is detected. Some of the scattered photons intersect the imaging object, where they are Compton scattered a second time. The finally scattered photons are recorded by an areal energy resolving detector panel around the object. The two detectors work in coincidence mode. CCVI images the spatial electron density distribution in the imaging object. Similar to PET imaging, the event location can be located within a curve; therefore the imaging reconstruction algorithms are also similar to those of PET. Two statistical iterative imaging reconstruction algorithms are tested. Our study verifies the feasibility of CCVI in imaging acquisition and reconstruction. Various aspects of CCVI are discussed. If successfully implemented, it will offer a great potential for imaging dose reduction compared with x-ray CT. Furthermore, a CCVI modality will have no moving parts, which potentially offers cost reduction and faster imaging speed.

In cancer treatment it is highly desirable to identify and /or classify individual cancer cells in real time. Nowadays, the standard method is PCR which is costly and time-consuming. Here we present a different approach to rapidly classify cell types: we measure the pattern of coherently diffracted extreme ultraviolet radiation (XUV radiation at 38nm wavelength), allowing to distinguish different single breast cancer cell types. The output of our laser driven XUV light source is focused onto a single unstained and unlabeled cancer cell, and the resulting diffraction pattern is measured in reflection geometry. As we will further show, the outer shape of the object can be retrieved from the diffraction pattern with sub-micron resolution. For classification it is often not necessary to retrieve the image, it is only necessary to compare the diffraction patterns which can be regarded as a spatial fingerprint of the specimen. For a proof-of-principle experiment MCF7 and SKBR3 breast cancer cells were pipetted on gold-coated silica slides. From illuminating each single cell and measuring a diffraction pattern we could distinguish between them. Owing to the short bursts of coherent soft x-ray light, one could also image temporal changes of the specimen, i.e. studying changes upon drug application once the desired specimen is found by the classification method. Using a more powerful laser, even classifying circulating tumor cells (CTC) at a high throughput seems possible. This lab-sized equipment will allow fast classification of any kind of cells, bacteria or even viruses in the near future.

Purpose: A new method for accurately portraying the impact of low-dose imaging techniques in C-arm cone-beam CT (CBCT) is presented and validated, allowing identification of minimum-dose protocols suitable to a given imaging task on a patient-specific basis in scenarios that require repeat intraoperative scans. Method: To accurately simulate lower-dose techniques and account for object-dependent noise levels (x-ray quantum noise and detector electronics noise) and correlations (detector blur), noise of the proper magnitude and correlation was injected into the projections from an initial CBCT acquired at the beginning of a procedure. The resulting noisy projections were then reconstructed to yield low-dose preview (LDP) images that accurately depict the image quality at any level of reduced dose in both filtered backprojection and statistical image reconstruction. Validation studies were conducted on a mobile C-arm, with the noise injection method applied to images of an anthropomorphic head phantom and cadaveric torso across a range of lower-dose techniques. Results: Comparison of preview and real CBCT images across a full range of techniques demonstrated accurate noise magnitude (within ~5%) and correlation (matching noise-power spectrum, NPS). Other image quality characteristics (e.g., spatial resolution, contrast, and artifacts associated with beam hardening and scatter) were also realistically presented at all levels of dose and across reconstruction methods, including statistical reconstruction. Conclusion: Generating low-dose preview images for a broad range of protocols gives a useful method to select minimum-dose techniques that accounts for complex factors of imaging task, patient-specific anatomy, and observer preference. The ability to accurately simulate the influence of low-dose acquisition in statistical reconstruction provides an especially valuable means of identifying low-dose limits in a manner that does not rely on a model for the nonlinear reconstruction process or a model of observer performance.

In an environment in which computed tomography (CT) has become an indispensable diagnostic tool employed with great frequency, dose concerns at the population level have become a subject of public attention. In that regard, optimizing radiation dose has become a core problem to the CT community. As a fundamental step to optimize radiation dose, it is crucial to effectively quantify radiation dose for a given CT exam. Such dose estimates need to be patient-specific to reflect individual radiation burden. It further needs to be prospective so that the scanning parameters can be dynamically adjusted before the scan is performed. The purpose of this study was to prospectively estimate organ dose in abdominopelvic CT exams under tube current modulation (TCM). CTDI_vol-normalized-organ dose coefficients ( h_fixed ) for fixed tube current were first estimated using a validated Monte Carlo simulation program and 58 computational phantoms. To account for the effect of TCM scheme, a weighted CTDI_vol was computed for each organ based on the tube current modulation profile. The organ dose was predicted by multiplying the weighted CTDI_vol with the organ dose coefficients ( h_fixed ). To quantify prediction accuracy, each predicted organ dose was compared with organ dose simulated from Monte Carlo program with TCM profile explicitly modeled. The predicted organ dose showed good agreement with simulated organ dose across all organs and modulation strengths. For an average CTDI_vol of a CT exam of 10 mGy, the absolute median error across all organs were 0.64 mGy (-0.21 and 0.97 for 25th and 75^th percentiles, respectively). The percentage differences (normalized by CTDI_vol of the exam) were within 15%. This study developed a quantitative model to predict organ dose under clinical abdominopelvic scans. Such information may aid in the optimization of CT protocols.

Patient specific dose evaluation and reporting is becoming increasingly important for x-ray imaging systems. Even imaging systems with lower patient dose such as CBCT scanners for extremities can benefit from accurate and size-specific dose assessment and reporting. This paper presents CTDI dose measurements performed on a prototype CBCT extremity imaging system across a range of body part sizes (5, 10, 16, and 20 cm effective diameter) and kVp (70, 80, and 90 kVp - with 0.1 mm Cu added filtration). The ratio of the CTDI measurements for the 5, 10, and 20 cm phantoms to the CTDI measurements for the 16 cm phantom were calculated and results were compared to size-specific dose estimates conversion factors (AAPM Report 204), which were evaluated on a conventional CT scanner. Due to the short scan nature of the system (220 degree acquisition angle), the dependence of CTDI values on the initial angular orientation of the phantom with respect to the imager was also evaluated. The study demonstrated that for a 220 degree acquisition sequence, the initial angular position of the conventional CTDI phantom with respect to the scanner does not significantly affect CTDI measurements (varying by less than 2% overall across the range of possible initial angular positions). The size-specific conversion factor was found to be comparable to the Report 204 factors for the large phantom size (20 cm) but lower, by up to 12%, for the 5 cm phantom (i.e., 1.35 for CBCT vs 1.54 for CT). The factors dependence on kVp was minimal, but dependence on kVp was most significant for smaller diameters. These results indicate that specific conversion factors need to be used for CBCT systems with short scans in order to provide more accurate dose reporting across the range of body sizes found in extremity scanners.

Complex interventional and diagnostic x-ray angiographic (XA) procedures may yield patient skin doses exceeding the threshold for radiation induced skin injuries. Skin dose is conventionally determined by converting the incident air kerma free-in-air into entrance surface air kerma, a process that requires the use of backscatter factors. Subsequently, the entrance surface air kerma is converted into skin kerma using mass energy-absorption coefficient ratios tissue-to-air, which for the photon energies used in XA is identical to the skin dose. The purpose of this work was to investigate how the cranial bone affects backscatter factors for the dosimetry of interventional neuroradiology procedures. The PENELOPE Monte Carlo system was used to calculate backscatter factors at the entrance surface of a spherical and a cubic water phantom that includes a cranial bone layer. The simulations were performed for different clinical x-ray spectra, field sizes, and thicknesses of the bone layer. The results show a reduction of up to 15% when a cranial bone layer is included in the simulations, compared with conventional backscatter factors calculated for a homogeneous water phantom. The reduction increases for thicker bone layers, softer incident beam qualities, and larger field sizes, indicating that, due to the increased photoelectric crosssection of cranial bone compared to water, the bone layer acts primarily as an absorber of low-energy photons. For neurointerventional radiology procedures, backscatter factors calculated at the entrance surface of a water phantom containing a cranial bone layer increase the accuracy of the skin dose determination.

Commercially available computed tomography (CT) technologies such as iterative reconstruction (IR) have the potential to enable reduced patient doses while maintaining diagnostic image quality. However, systematically determining safe dose reduction levels for IR algorithms is a challenging task due to their nonlinear nature. Most attempts to evaluate IR algorithms rely on measurements made in uniform phantoms. Such measurements may overstate the dose reduction potential of IR because they don’t account for the complex relationship between anatomical variability and image quality. The purpose of this study was to design anatomically informed textured phantoms for CT performance evaluation. Two phantoms were designed to represent lung and soft-tissue textures. The lung phantom includes intricate vessel-like structures along with embedded nodules (spherical, lobulated, and spiculated). The soft tissue phantom was designed based on a three-dimensional clustered lumpy background with included low-contrast lesions (spherical and anthropomorphic). The phantoms were built using rapid prototyping (3D printing) technology and imaged on a modern multi-slice clinical CT scanner to assess the noise performance of a commercial IR algorithm in the context of uniform and textured backgrounds. Fifty repeated acquisitions were acquired for each background type and noise was assessed by measuring pixel standard deviation, across the ensemble of repeated acquisitions. For pixels in uniform areas, the IR algorithm reduced noise magnitude (STD) by 60% (compared to FBP). However, for edge pixels, the noise magnitude in the IR images ranged from 20% higher to 40% lower compared to FBP. In all FBP images and in IR images of the uniform phantom, noise appeared to be globally non-stationary (i.e., spatially dependent) but locally stationary (within a reasonably small region of interest). In the IR images of the textured phantoms, the noise was globally and locally non-stationary.

With the increased use of CT examinations, the associated radiation dose has become a large concern, especially for pediatrics. Much research has focused on reducing radiation dose through new scanning and reconstruction methods. Computational phantoms provide an effective and efficient means for evaluating image quality, patient-specific dose, and organ-specific dose in CT. We previously developed a set of highly-detailed 4D reference pediatric XCAT phantoms at ages of newborn, 1, 5, 10, and 15 years with organ and tissues masses matched to ICRP Publication 89 values. We now extend this reference set to a series of 64 pediatric phantoms of a variety of ages and height and weight percentiles, representative of the public at large. High resolution PET-CT data was reviewed by a practicing experienced radiologist for anatomic regularity and was then segmented with manual and semi-automatic methods to form a target model. A Multi-Channel Large Deformation Diffeomorphic Metric Mapping (MC-LDDMM) algorithm was used to calculate the transform from the best age matching pediatric reference phantom to the patient target. The transform was used to complete the target, filling in the non-segmented structures and defining models for the cardiac and respiratory motions. The complete phantoms, consisting of thousands of structures, were then manually inspected for anatomical accuracy. 3D CT data was simulated from the phantoms to demonstrate their ability to generate realistic, patient quality imaging data. The population of pediatric phantoms developed in this work provides a vital tool to investigate dose reduction techniques in 3D and 4D pediatric CT.

In radiography there is generally a conflict between the best image quality and the lowest possible patient dose. A proven method of dosimetry is the simulation of radiation transport in virtual human models (i.e. phantoms). However, while the resolution of these voxel models is adequate for most dosimetric purposes, they cannot provide the required organ fine structures necessary for the assessment of the imaging quality. The aim of this work is to develop hybrid/dual-lattice voxel models (called also phantoms) as well as simulation methods by which patient dose and image quality for typical radiographic procedures can be determined. The results will provide a basis to investigate by means of simulations the relationships between patient dose and image quality for various imaging parameters and develop methods for their optimization. A hybrid model, based on NURBS (Non Linear Uniform Rational B-Spline) and PM (Polygon Mesh) surfaces, was constructed from an existing voxel model of a female patient. The organs of the hybrid model can be then scaled and deformed in a non-uniform way i.e. organ by organ; they can be, thus, adapted to patient characteristics without losing their anatomical realism. Furthermore, the left lobe of the lung was substituted by a high resolution lung voxel model, resulting in a dual-lattice geometry model. “Dual lattice” means in this context the combination of voxel models with different resolution. Monte Carlo simulations of radiographic imaging were performed with the code EGS4nrc, modified such as to perform dual lattice transport. Results are presented for a thorax examination.

Breast imaging is an important area of research with many new techniques being investigated to further reduce the morbidity and mortality of breast cancer through early detection. Computerized phantoms can provide an essential tool to quantitatively compare new imaging systems and techniques. Current phantoms, however, lack sufficient realism in depicting the complex 3D anatomy of the breast. In this work, we created one-hundred realistic and detailed 3D computational breast phantoms based on high-resolution CT datasets from normal patients. We also developed a finiteelement application to simulate different compression states of the breast, making the phantoms applicable to multimodality imaging research. The breast phantoms and tools developed in this work were packaged into user-friendly software applications to distribute for breast imaging research.

Previous fabrication of anthropomorphic breast phantoms has demonstrated their viability as a model for 2D (mammography) and 3D (tomosynthesis) breast imaging systems. Further development of these models will be essential for the evaluation of breast x-ray systems. There is also the potential to use them as the ground truth in virtual clinical trials. The first generation of phantoms was segmented from human subject dedicated breast computed tomography data and fabricated into physical models using highresolution 3D printing. Two variations were made. The first was a multi-material model (doublet) printed with two photopolymers to represent glandular and adipose tissues with the greatest physical contrast available, mimicking 75% and 35% glandular tissue. The second model was printed with a single 75% glandular equivalent photopolymer (singlet) to represent glandular tissue, which can be filled independently with an adipose-equivalent material such as oil. For this study, we have focused on improving the latter, the singlet phantom. First, the temporary oil filler has been replaced with a permanent adipose-equivalent urethane-based polymer. This offers more realistic contrast as compared to the multi-material approach at the expense of air bubbles and pockets that form during the filling process. Second, microcalcification clusters have been included in the singlet model via crushed eggshells, which have very similar chemical composition to calcifications in vivo. The results from these new prototypes demonstrate significant improvement over the first generation of anthropomorphic physical phantoms.

An automated method has been developed to insert realistic clusters of simulated microcalcifications (MCs) into computer models of breast anatomy. This algorithm has been developed as part of a virtual clinical trial (VCT) software pipeline, which includes the simulation of breast anatomy, mechanical compression, image acquisition, image processing, display and interpretation. An automated insertion method has value in VCTs involving large numbers of images. The insertion method was designed to support various insertion placement strategies, governed by probability distribution functions (pdf). The pdf can be predicated on histological or biological models of tumor growth, or estimated from the locations of actual calcification clusters. To validate the automated insertion method, a 2-AFC observer study was designed to compare two placement strategies, undirected and directed. The undirected strategy could place a MC cluster anywhere within the phantom volume. The directed strategy placed MC clusters within fibroglandular tissue on the assumption that calcifications originate from epithelial breast tissue. Three radiologists were asked to select between two simulated phantom images, one from each placement strategy. Furthermore, questions were posed to probe the rationale behind the observer’s selection. The radiologists found the resulting cluster placement to be realistic in 92% of cases, validating the automated insertion method. There was a significant preference for the cluster to be positioned on a background of adipose or mixed adipose/fibroglandular tissues. Based upon these results, this automated lesion placement method will be included in our VCT simulation pipeline.

Photon counting detector (PCD) x-ray imaging systems have seen increasing use in the past decade in applications such as low-dose radiography and tomography. A cascaded systems analysis model has been developed to describe the signal and noise transfer characteristics for such systems in a manner that accounts for unique PCD functionality (such as an application of a threshold) and explicitly considers the distribution of quanta through each stage. This model was used to predict the mean signal, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE) of a silicon-strip PCD system, and these predictions were compared to measurements across a range of exposure conditions and thresholds. Further, the model was used to investigate the impact of design parameters such as detector thickness and pulse height amplification as well as unique PCD performance effects such as charge sharing and additive noise with respect to threshold. The development of an analytical model for prediction of such metrics provides a framework for understanding the complex imaging performance characteristics of PCD systems – especially important in the early development of new radiographic and tomographic applications – and a guide to task-based performance optimization.

Neuro-EIGIs require visualization of very small endovascular devices and small vessels. A Microangiographic Fluoroscope (MAF) x-ray detector was developed to improve on the standard flat panel detector’s (FPD’s) ability to visualize small objects during neuro-EIGIs. To compare the performance of FPD and MAF imaging systems, specific imaging tasks related to those encountered during neuro-EIGIs were used to assess contrast to noise ratio (CNR) of different objects. A bar phantom and a stent were placed at a fixed distance from the x-ray focal spot to mimic a clinical imaging geometry and both objects were imaged by each detector system. Imaging was done without anti-scatter grids and using the same conditions for each system including: the same x-ray beam quality, collimator position, source to imager distance (SID), and source to object distance (SOD). For each object, relative contrasts were found for both imaging systems using the peak and trough signals. The relative noise was found using mean background signal and background noise for varying detector exposures. Next, the CNRs were found for these values for each object imaged and for each imaging system used. A relative CNR metric is defined and used to compare detector imaging performance. The MAF utilizes a temporal filter to reduce the overall image noise. The effects of using this filter with the MAF while imaging the clinical object’s CNRs are reported. The relative CNR for the detectors demonstrated that the MAF has superior CNRs for most objects and exposures investigated for this specific imaging task.

Research on CT systems often requires knowledge of intensity as a function of angle in the fan-beam, due to the presence of bowtie filters, for studies such as dose reduction simulation, Monte Carlo dose calculations, or statistical reconstruction algorithms. Since manufacturers consider the x-ray bowtie filter design to be proprietary information, several methods have been proposed to measure the beam intensity profile independently: 1) calculate statistical properties of noise in acquired sinograms (requires access to raw data files, which is also vendor proprietary); 2) measure the waveform of a dosimeter located away from the isocenter (requires dosimeter equipment costing > $10K). We present a novel method that is inexpensive (parts costing ~$100 from any hardware store, using Gafchromic film at ~$3 per measurement), requires no proprietary information, and can be performed in a few minutes. A fixture is built from perforated steel tubing, which forms an aperture that selectively samples the intensity at a particular fan-beam angle in a rotating gantry. Two exposures (1× and 2×) are made and self-developing radiochromic film (Gafchromic XR- Ashland Inc.) is then scanned on an inexpensive PC document scanner. An analysis method is described that linearizes the measurements for relative exposure. The resultant profile is corrected for geometric effects (1/LΛ2 fall-off, gantry dwell time) and background exposure, providing a noninvasive estimate of the CT fan-beam intensity present in an operational CT system. This method will allow researchers to conveniently measure parameters required for modeling the effects of bowtie filters in clinical scanners.

Despite the significant clinical benefits of computed tomography (CT) in providing diagnostic information for a broad range of diseases, concerns have been raised regarding the potential cancer risk induced by CT radiation exposure. In that regard, optimizing CT protocols and minimizing radiation dose have become the core problem for the CT community. To develop strategies to optimize radiation dose, it is crucial to effectively characterize CT image quality. Such image quality estimates need to be prospective to ensure that optimization can be performed before the scan is initiated. The purpose of this study was to establish a phantombased methodology to predict quantum noise in CT images as a first step in our image quality prediction. Quantum noise was measured using a variable-sized phantom under clinical protocols. The mathematical relationship between noise and water-equivalent-diameter (Dw) was further established. The prediction was achieved by ascribing a noise value to a patient according to the patient’s water-equivalent-diameter. The prediction accuracy was evaluated in anthropomorphic phantoms across a broad range of sizes, anatomy, and reconstruction algorithms. The differences between the measured and predicted noise were within 10% for anthropomorphic phantoms across all sizes and anatomy. This study proposed a practically applicable technique to predict noise in CT images. With a prospective estimation of image quality level, the scanning parameters can then by adjusted to ensure optimized imaging performance.

Digital breast tomosynthesis (DBT) generates 3-D reconstructions of the breast by taking X-Ray projections at various angles around the breast. DBT improves cancer detection as it minimizes tissue overlap that is present in traditional 2-D mammography. In this work, two methods of reconstruction, filtered backprojection (FBP) and the Newton-Raphson iterative reconstruction were used to create 3-D reconstructions from phantom images acquired on a breast tomosynthesis system. The task based image analysis method was used to compare the performance of each reconstruction technique. The task simulated a 10mm lesion within the breast containing iodine concentrations between 0.0mg/ml and 8.6mg/ml. The TTF was calculated using the reconstruction of an edge phantom, and the NPS was measured with a structured breast phantom (CIRS 020) over different exposure levels. The detectability index d’ was calculated to assess image quality of the reconstructed phantom images. Image quality was assessed for both conventional, single energy and dual energy subtracted reconstructions. Dose allocation between the high and low energy scans was also examined. Over the full range of dose allocations, the iterative reconstruction yielded a higher detectability index than the FBP for single energy reconstructions. For dual energy subtraction, detectability index was maximized when most of the dose was allocated to the high energy image. With that dose allocation, the performance trend for reconstruction algorithms reversed; FBP performed better than the corresponding iterative reconstruction. However, FBP performance varied very erratically with changing dose allocation. Therefore, iterative reconstruction is preferred for both imaging modalities despite underperforming dual energy FBP, as it provides stable results.

The utility of CT lung nodule volume quantification technique depends on the precision of the quantification. To enable the evaluation of quantification precision, we previously developed a mathematical model that related precision to image resolution and noise properties in uniform backgrounds in terms of an estimability index (e’). The e’ was shown to predict empirical precision across 54 imaging and reconstruction protocols, but with different correlation qualities for FBP and iterative reconstruction (IR) due to the non-linearity of IR impacted by anatomical structure. To better account for the non-linearity of IR, this study aimed to refine the noise characterization of the model in the presence of textured backgrounds. Repeated scans of an anthropomorphic lung phantom were acquired. Subtracted images were used to measure the image quantum noise, which was then used to adjust the noise component of the e’ calculation measured from a uniform region. In addition to the model refinement, the validation of the model was further extended to 2 nodule sizes (5 and 10 mm) and 2 segmentation algorithms. Results showed that the magnitude of IR’s quantum noise was significantly higher in structured backgrounds than in uniform backgrounds (ASiR, 30-50%; MBIR, 100-200%). With the refined model, the correlation between e’ values and empirical precision no longer depended on reconstruction algorithm. In conclusion, the model with refined noise characterization relfected the nonlinearity of iterative reconstruction in structured background, and further showed successful prediction of quantification precision across a variety of nodule sizes, dose levels, slice thickness, reconstruction algorithms, and segmentation software.

The channelized Hotelling observer (CHO) model, wherein internal noise plays an important role to account for the psychophysiological uncertainty in human’s visual perception, has found extensive applications in the assessment of image quality in nuclear medicine, mammography and conventional CT. Recently, we extended its application to investigating the detectability index of differential phase contrast (DPC) CT–an emerging CT technology with the potential of increasing the capability in soft tissue differentiation. We found that the quantitative determination of internal noise in the CHO study of DPC-CT’s detectability index should differ from that in the conventional CT. It is believed that the root cause of such a difference lies in the distinct noise spectra between the DPC-CT and conventional CT. In this paper, we present the preliminary results and investigate the adequate strategies to quantitatively determine the internal noise of CHO model for its application in the assessment of image quality in DPC-CT and its comparison with that of the conventional CT.

We propose a generalized resolution modeling (RM) framework, including extensive task-based optimization, wherein we continualize the conventionally discrete framework of RM vs. no RM, to include varying degrees of RM. The proposed framework has the advantage of providing a trade-off between the enhanced contrast recovery by RM and the reduced inter-voxel correlations in the absence of RM, and to enable improved task performance. The investigated context was that of oncologic lung FDG PET imaging. Given a realistic blurring kernel of FWHM h (‘true PSF’), we performed iterative EM including RM using a wide range of ‘modeled PSF’ kernels with varying widths h. In our simulations, h = 6mm, while h varied from 0 (no RM) to 12mm, thus considering both underestimation and overestimation of the true PSF. Detection task performance was performed using prewhitened (PWMF) and nonprewhitened matched filter (NPWMF) observers. It was demonstrated that an underestimated resolution blur (h = 4mm) enhanced task performance, while slight over-estimation (h = 7mm) also achieved enhanced performance. The latter is ironically attributed to the presence of ringing artifacts. Nonetheless, in the case of the NPWMF, the increasing intervoxel correlations with increasing values of h degrade detection task performance, and underestimation of the true PSF provides the optimal task performance. The proposed framework also achieves significant improvement of reproducibility, which is critical in quantitative imaging tasks such as treatment response monitoring.

The purposes of this work were to find an optimal x-ray voltage for CT imaging and to determine the diagnostic effectiveness of image reconstruction techniques by using the visual grading analysis (VGA). Images of the PH-5 CT abdomen phantom (Kagaku Co, Kyoto) were acquired by the Toshiba Aquillion One 320 slices CT system with various exposures (from 10 to 580 mAs) under different tube peak voltages (80, 100 and 120 kVp). The images were reconstructed by employing the FBP and the AIDR 3D iterative reconstructions with Mild, Standard and Strong FBP blending. Image quality was assessed by measuring noise, contrast to noise ratio and human observer’s VGA scores. The CT dose index CTDIv was obtained from the values displayed on the images. The best fit for the curves of the image quality VGA vs dose CTDIv is a logistic function from the SPSS estimation. A threshold dose Dt is defined as the CTDIv at the just acceptable for diagnostic image quality and a figure of merit (FOM) is defined as the slope of the standardised logistic function. The Dt and FOM were found to be 5.4, 8.1 and 9.1 mGy and 0.47, 0.51 and 0.38 under the tube voltages of 80, 100 and 120 kVp, respectively, from images reconstructed by the FBP technique. The Dt and FOM values were lower from the images reconstructed by the AIDR 3D in comparison with the FBP technique. The optimal xray peak voltage for the imaging of the PH-5 abdomen phantom by the Aquillion One CT system was found to be at 100 kVp. The images reconstructed by the FBP are more diagnostically effective than that by the AIDR 3D but with a higher dose Dt to the patients.

Purpose: Clinical performance studies of an extremity cone-beam CT (CBCT) system indicate excellent bone visualization, but point to the need for improvement of soft-tissue image quality. To this end, a rapid Monte Carlo (MC) scatter correction is proposed, and Penalized Likelihood (PL) reconstruction is evaluated for noise management. Methods: The accelerated MC scatter correction involved fast MC simulation with low number of photons implemented on a GPU (10⁷ photons/sec), followed by Gaussian kernel smoothing in the detector plane and across projection angles. PL reconstructions were investigated for reduction of imaging dose for projections acquired at ~2 mGy. Results: The rapid scatter estimation yielded root-mean-squared-errors of scatter projections of ~15% of peak scatter intensity for 5⋅10⁶ photons/projection (runtime ~0.5 sec/projection) and 25% improvement in fat-muscle contrast in reconstructions of a cadaveric knee. PL reconstruction largely restored soft-tissue visualization at 2 mGy dose to that of 10 mGy FBP image. Conclusion: The combination of rapid (5-10 minutes/scan) MC-based, patient-specific scatter correction and PL reconstruction offers an important means to overcome the current limitations of extremity CBCT in soft-tissue imaging.

We studied the efficiency of transferring task information from each stage of the ultrasonic imaging to another by computing the total task energy available at each stage. We computed the task-energy efficiency for the B-mode data with respect to RF data as an expression to predict performance efficiency. We also measured performance efficiency of B-mode image observers with that of ideal observer of the RF data using both analytical SNR expressinos and Monte-Carlo expreriment over a range of visual tasks related to breast lesion diagnosis. The performance efficiency results are compared with the prediction from task-energy efficiency and information efficiency. It is shown that task-energy efficiency can closely predict the performance efficiency especially for large-area tasks.

In recent years, there have been several findings regarding CT number variations (partial scan artifact or PSA) across time in dynamic myocardial perfusion studies with short scan gated reconstruction. These variations are correlated with the view angle range corresponding to the short scan acquisition for a given cardiac phase, which can vary from one cardiac cycle to another due to the asynchrony between heart rate and gantry rotation speed. In this study, we investigate several potential causes of PSA, including noise, beam hardening and scatter, using numerical simulations. In addition, we investigate partial scan artifact in a single source 64-slice diagnostic CT scanner in vivo data sets, and report its effect on perfusion analysis. Results indicated that among all three factors investigated, scatter can cause obvious partial scan artifact in dynamic myocardial perfusion imaging. Further, scatter is a low frequency phenomenon and is not heavily dependent on the changing contrasts, as both the frequency method and the virtual scan method are effective in reducing partial scan artifact. However, PSA does not necessarily lead to different blood volume maps compared to the full scan, because these maps are usually generated with a curve fitting procedure.

Purpose: The purpose of this study is to determine if diffraction enhanced imaging (DEI) can quantify anisotropy in bone microarchitecture. Background: Osteoporosis is characterized by low bone mass and microarchitectural deterioration of bone. A noninvasive tool for measuring the degree of anisotropy (DA) in bone microarchitecture will help clinicians better assess fracture risk in osteoporotic patients. DEI detects small angular deflections in an x-ray beam, and is only sensitive to angular changes in one plane. If the beam is refracted by multiple anisotropic microstructures (e.g. osteocyte lacunae and pores) in bone, the angular spreading can be measured with DEI and differences in the amount of spreading for different bone orientations is indicative of the DA in bone microarchitecture. Method: An x-ray-tube based DEI system was used to collect an array of DEI reflectivity profiles measured through bovine cortical bone samples with the bones oriented with the bone axis in the plane perpendicular to the propagation of the x-ray beam. Micro-CT images of the bones were obtained using a Scanco uCT40 ex vivo scanner, and the DA of the pore structure was quantified using BoneJ. Results: The maximum and minimum measured reflectivity profile widths through bone varied by a factor of two; this suggests that the microarchitecture is preferentially aligned with the bone axis in a 2-to-1 ratio. The DA for the cortical pores was 0.6, which agrees with DEI’s anisotropy measure. Conclusions: The preliminary findings of this study suggest that DEI is sensitive to anisotropy in bone microarchitecture.

State of the art automatic exposure control modulates the tube current across view angle and Z based on patient anatomy for use in axial full scan reconstructions. Cardiac CT, however, uses a fundamentally different image reconstruction that applies a temporal weighting to reduce motion artifacts. This paper describes a phase based mA modulation that goes beyond axial and ECG modulation; it uses knowledge of the temporal view weighting applied within the reconstruction algorithm to improve dose efficiency in cardiac CT scanning. Using physical phantoms and synthetic noise emulation, we measure how knowledge of sinogram temporal weighting and the prescribed cardiac phase can be used to improve dose efficiency. First, we validated that a synthetic CT noise emulation method produced realistic image noise. Next, we used the CT noise emulation method to simulate mA modulation on scans of a physical anthropomorphic phantom where a motion profile corresponding to a heart rate of 60 beats per minute was used. The CT noise emulation method matched noise to lower dose scans across the image within 1.5% relative error. Using this noise emulation method to simulate modulating the mA while keeping the total dose constant, the image variance was reduced by an average of 11.9% on a scan with 50 msec padding, demonstrating improved dose efficiency. Radiation dose reduction in cardiac CT can be achieved while maintaining the same level of image noise through phase based dose modulation that incorporates knowledge of the cardiac reconstruction algorithm.

Motion estimation is a very important method for improving image quality by compensating the cardiac motion at the best phase reconstructed. We tackle the cardiac motion estimation problem using an image registration approach. We compare the performance of three gradient-based registration methods on clinical data. In addition to simple gradient descent, we test the Nesterov accelerated descent and conjugate gradient algorithms. The results show that accelerated gradient methods provide significant speedup over conventional gradient descent with no loss of image quality.

Endovascular-Image-Guided-Interventional (EIGI) treatment of neuro-vascular conditions such as aneurysms, stenosed arteries, and vessel thrombosis make use of treatment devices such as stents, coils, and balloons which have very small feature sizes, 10's of microns to a few 100's of microns, and hence demand a high resolution imaging system. The current state-of-the-art flat panel detector (FPD) has about a 200-um pixel size with the Nyquist of 2.5 lp/mm. For higher-resolution imaging a charge-coupled device (CCD) based Micro-Angio - Fluoroscope (MAF-CCD) with a pixel size of 35um (Nyquist of 11 lp/mm) was developed and previously reported. Although the detector addresses the high resolution needs, the Field-Of-View (FOV) is limited to 3.5 cm x 3.5 cm, which is much smaller than current FPDs. During the use of the MAF-CCD for delicate parts of the intervention, it may be desirable to have real-time monitoring outside the MAF FOV with a low dose, and lower, but acceptable, quality image. To address this need, a novel imaging technique for biplane imaging systems has been developed, using an MAFCCD in the frontal plane and a dose-reduced standard large FOV imager in the lateral plane. The dose reduction is achieved by using a combination of ROI fluoroscopy and spatially different temporal filtering, a technique that has been previously presented. In order to evaluate this technique, a simulation using images acquired during an actual EIGI treatment on a patient, followed by an actual implementation on phantoms is presented.

When performing Computed Tomographic (CT) image reconstruction on digital subtraction angiography (DSA) projections, loss of vessel contrast has been observed behind highly attenuating anatomy, such as dental implants and large contrast filled aneurysms. Because this typically occurs only in a limited range of projection angles, the observed contrast time course can potentially be altered. In this work, we have developed a model for acquiring DSA projections that models both the polychromatic nature of the x-ray spectrum and the x-ray scattering interactions to investigate this problem. In our simulation framework, scatter and beam hardening contributions to vessel dropout can be analyzed separately. We constructed digital phantoms with large clearly defined regions containing iodine contrast, bone, soft issue, titanium (dental implants) or combinations of these materials. As the regions containing the materials were large and rectangular, when the phantoms were forward projected, the projections contained uniform regions of interest (ROI) and enabled accurate vessel dropout analysis. Two phantom models were used, one to model the case of a vessel behind a large contrast filled aneurysm and the other to model a vessel behind a dental implant. Cases in which both beam hardening and scatter were turned off, only scatter was turned on, only beam hardening was turned on, and both scatter and beam hardening were turned on, were simulated for both phantom models. The analysis of this data showed that the contrast degradation is primarily due to scatter. When analyzing the aneurysm case, 90.25% of the vessel contrast was lost in the polychromatic scatter image, however only 50.5% of the vessel contrast was lost in the beam hardening only image. When analyzing the teeth case, 44.2% of the vessel contrast was lost in the polychromatic scatter image and only 26.2% of the vessel contrast was lost in the beam hardening only image.

Proper sizing of interventional devices to match coronary vessel dimensions improves procedural efficiency and therapeutic outcomes. We have developed a novel method using inverse geometry x-ray fluoroscopy to automatically determine vessel dimensions without the need for magnification calibration or optimal views. To validate this method in vivo, we compared results to intravascular ultrasound (IVUS) and coronary computed tomography angiography (CCTA) in a healthy porcine model. Coronary angiography was performed using Scanning-Beam Digital X-ray (SBDX), an inverse geometry fluoroscopy system that performs multiplane digital x-ray tomosynthesis in real time. From a single frame, 3D reconstruction of the arteries was performed by localizing the depth of vessel lumen edges. The 3D model was used to directly calculate length and to determine the best imaging plane to use for diameter measurements, where outof- plane blur was minimized and the known pixel spacing was used to obtain absolute vessel diameter. End-diastolic length and diameter measurements were compared to measurements from CCTA and IVUS, respectively. For vessel segment lengths measuring 6 mm to 73 mm by CCTA, the SBDX length error was -0.49 ± 1.76 mm (SBDX - CCTA, mean ± 1 SD). For vessel diameters measuring 2.1 mm to 3.6 mm by IVUS, the SBDX diameter error was 0.07 ± 0.27 mm (SBDX - minimum IVUS diameter, mean ± 1 SD). The in vivo agreement between SBDX-based vessel sizing and gold standard techniques supports the feasibility of calibration-free coronary vessel sizing using inverse geometry x-ray fluoroscopy.

Material basis decomposition in the sinogram domain requires accurate knowledge of the forward model in spectral CT. Misspecifications over a certain limit will result in biased estimates and make quantum limited quantitative CT difficult. We present a method whereby users can determine the degree of allowed misspecification error in a spectral CT forward model, and still have quantification errors that are quantum limited.

Recently, the double contrast technique in a gastrointestinal examination and the transbronchial lung biopsy in an examination for the respiratory system [1-3] have made a remarkable progress. Especially in the transbronchial lung biopsy, better quality of x-ray fluoroscopic images is requested because this examination is performed under a guidance of x-ray fluoroscopic images. On the other hand, various image processing methods [4] for x-ray fluoroscopic images have been developed as an x-ray system with a flat panel detector [5-7] is widely used. A recursive filtering is an effective method to reduce a random noise in x-ray fluoroscopic images. However it has a limitation for its effectiveness of a noise reduction in case of a moving object exists in x-ray fluoroscopic images because the recursive filtering is a noise reduction method by adding last few images. After recursive filtering a residual signal was produced if a moving object existed in x-ray images, and this residual signal disturbed a smooth procedure of the examinations. To improve this situation, new noise reduction method has been developed. The Adaptive Noise Reduction [ANR] is the brand-new noise reduction technique which can be reduced only a noise regardless of the moving object in x-ray fluoroscopic images. Therefore the ANR is a very suitable noise reduction method for the transbronchial lung biopsy under a guidance of x-ray fluoroscopic images because the residual signal caused of the moving object in x-ray fluoroscopic images is never produced after the ANR. In this paper, we will explain an advantage of the ANR by comparing of a performance between the ANR images and the conventional recursive filtering images.

Magnetic Resonance Imaging is a very powerful techniques for soft tissue diagnosis. At the present, the clinical evaluation is mainly conducted exploiting the amplitude of the recorded MR image which, in some specific cases, is modified by using contrast enhancements. Nevertheless, spin-lattice (T1) and spin-spin (T2) relaxation times can play an important role in many pathology diagnosis, such as cancer, Alzheimer or Parkinson diseases. Different algorithms for relaxation time estimation have been proposed in literature. In particular, the two most adopted approaches are based on Least Squares (LS) and on Maximum Likelihood (ML) techniques. As the amplitude noise is not zero mean, the first one produces a biased estimator, while the ML is unbiased but at the cost of high computational effort. Recently the attention has been focused on the estimation in the complex, instead of the amplitude, domain. The advantage of working with real and imaginary decomposition of the available data is mainly the possibility of achieving higher quality estimations. Moreover, the zero mean complex noise makes the Least Square estimation unbiased, achieving low computational times. First results of complex domain relaxation times estimation on real datasets are presented. In particular, a patient with an occipital lesion has been imaged on a 3.0T scanner. Globally, the evaluation of relaxation times allow us to establish a more precise topography of biologically active foci, also with respect to contrast enhanced images.

The 2D scatter-to-primary (SPR) ratios and 3D voxelized difference volumes were characterized for a cone beam breast CT scanner capable of arbitrary (non-traditional) 3D trajectories. The CT system uses a 30x30cm² flat panel imager with 197 micron pixellation and a rotating tungsten anode x-ray source with 0.3mm focal spot, with an SID of 70cm. Data were acquired for two cylindrical phantoms (12.5cm and 15cm diameter) filled with three different combinations of water and methanol yielding a range of uniform densities. Projections were acquired with two acquisition trajectories: 1) simple-circular azimuthal orbit with fixed tilt; and 2) saddle orbit following a ±15° sinusoidal trajectory around the object. Projection data were acquired in 2x2 binned mode. Projections were scatter corrected using a beam stop array method, and the 2D SPR was measured on the projections. The scatter corrected and uncorrected data were then reconstructed individually using an iterative ordered subsets convex algorithm, and the 3D difference volumes were calculated as the absolute difference between the two. Results indicate that the 2D SPR is ~7-15% higher on projections with greatest tilt for the saddle orbit, due to the longer x-ray path length through the volume, compared to the 0° tilt projections. Additionally, the 2D SPR increases with object diameter as well as density. The 3D voxelized difference volumes are an estimate of the scatter contribution to the reconstructed attenuation coefficients on a voxel level. They help visualize minor deficiencies and artifacts in the volumes due to correction methods.

Perfusion imaging is an essential method for stroke diagnostics. One of the most important factors for a successful therapy is to get the diagnosis as fast as possible. Therefore our approach aims at perfusion imaging (PI) with a cone beam C-arm system providing perfusion information directly in the interventional suite. For PI the imaging system has to provide excellent soft tissue contrast resolution in order to allow the detection of small attenuation enhancement due to contrast agent in the capillary vessels. The limited dynamic range of flat panel detectors as well as the sparse sampling of the slow rotating C-arm in combination with standard reconstruction methods results in limited soft tissue contrast. We choose a penalized maximum-likelihood reconstruction method to get suitable results. To minimize the computational load, the 4D reconstruction task is reduced to several static 3D reconstructions. We also include an ordered subset technique with transitioning to a small number of subsets, which adds sharpness to the image with less iterations while also suppressing the noise. Instead of the standard multiplicative EM correction, we apply a Newton-based optimization to further accelerate the reconstruction algorithm. The latter optimization reduces the computation time by up to 70%. Further acceleration is provided by a multi-GPU implementation of the forward and backward projection, which fulfills the demands of cone beam geometry. In this preliminary study we evaluate this procedure on clinical data. Perfusion maps are computed and compared with reference images from magnetic resonance scans. We found a high correlation between both images.

Image reconstruction and motion model estimation in four dimensional cone-beam CT (4D-CBCT) are conventionally handled as two sequential steps. Due to the limited number of projections at each phase, the image quality of 4D-CBCT is degraded by view aliasing artifacts, and the accuracy of subsequent motion modeling is decreased by the inferior 4DCBCT. The objective of this work is to enhance both the image quality of 4D-CBCT and the accuracy of motion model estimation with a novel strategy enabling simultaneous motion estimation and image reconstruction (SMEIR). The proposed SMEIR algorithm consists of two alternating steps: 1) model-based iterative image reconstruction to obtain a motion-compensated primary CBCT (m-pCBCT) and 2) motion model estimation to obtain an optimal set of deformation vector fields (DVFs) between the m-pCBCT and other 4D-CBCT phases. The motion-compensated image reconstruction is based on the simultaneous algebraic reconstruction (SART) technique coupled with total variation minimization. During the forward- and back-projection of SART, measured projections from an entire set of 4D-CBCT are used for reconstruction of the m-pCBCT by utilizing the updated DVF. The DVF is estimated by matching the forward projection of the deformed m-pCBCT and measured projections of other phases of 4D-CBCT. The performance of the SMEIR algorithm is quantitatively evaluated on a 4D NCAT phantom. The SMEIR algorithm improves image reconstruction accuracy of 4D-CBCT and tumor motion trajectory estimation accuracy as compared to conventional sequential 4D-CBCT reconstruction and motion estimation.

In cone-beam CT the range of projection views measured for each given image voxel is spatially variant. In the corners of the image volume there is less projection data available to be used by the image reconstruction algorithm, due to data truncation in the z direction (i.e. along the scanner axis). Given the desire to increase the fraction of the voxels which may be reconstructed from a given scan there is a desire to incorporate some extrapolated data into the image reconstruction procedure. In this work one approach is described which consists of a two-pass procedure where the first pass image reconstruction is performed over a larger extent in the z direction, a non-linear transform is applied to the initial reconstruction and a forward projection is applied in order to estimate the extrapolated image data. Initial results are presented which compare the method to zeroth order extrapolation and demonstrate that improvement in the reconstruction of the corner regions with a simple numerical phantom and with anatomical phantom data from a prototype wide coverage CT system.

Scatter is a significant source of image artifacts in wide-cone CT. Scatter management includes both scatter rejection and scatter correction. The common scatter rejection approach is to use an anti-scatter grid (ASG). Conventional CT scanners (with detector coverage not exceeding 40mm along the patient axis) typically employ one-dimensional (1D) ASGs. Such grids are quite effective for small cone angles. For larger cone angles, however, simply increasing the aspect ratio of a 1D ASG is not sufficient. In addition, a 1D ASG offers no scatter rejection along the patient axis. To ensure adequate image quality in wide-cone CT, a two-dimensional (2D) ASG is needed. In this work, we measured the amount of scatter and the degree of image artifacts typically attributable to scatter for four prototype 2D ASG designs, and we compared those to a 1D ASG. The scatter was measured in terms of the scatter-toprimary ratio (SPR). The cupping and ghosting artifacts were assessed through quantitative metrics. For the 2D ASGs, when compared to the 1D ASG, the SPR decreased by up to 66% and 75% for 35cm water and 48cm polyethylene, respectively, phantoms, at 80-160mm apertures (referenced to isocenter), as measured by the pinhole method. As measured by the two-aperture method, the SPR reduction was 59%-68% at isocenter for the 35cm water phantom at 160mm aperture. The cupping artifact was decreased by up to ~80%. The ghosting artifact was reduced as well. The results of the evaluation clearly demonstrate the advantage of using a 2D ASG for wide-cone CT.

C-arm cone-beam computed tomography offers CT-like 3D imaging capabilities, but with the additional advantage of being appropriate for interventional suites. Due to the limitations of the data acquisition system, projections are oft acquired in a short scan angular range, resulting in significant artifacts, if conventional analytic formulas are applied. Furthermore, the presence of high-density objects, like metal parts, induces streak-like artifacts, which can obscure relevant anatomy. We present a new algorithm to reduce such artifacts and enhance the quality of reconstructed 3D volume. We make use of the variance of estimated voxel values over all projections to decrease the ground artifact level. The proposed algorithm is less sensitive to data truncation, and does not require explicit estimation of missing data. The number of required images is very low (up to 56 projections), which have several benefits, like significant reduction of patient dose and shortening of the acquisition time. The performance of the proposed method is demonstrated based on simulations and phantom data.

In this study, we systematically investigated the noise correlation properties among detector bins of CBCT projection data by analyzing repeated projection measurements. The measurements were performed on a TrueBeam on-board CBCT imaging system with a 4030CB flat panel detector. An anthropomorphic male pelvis phantom was used to acquire 500 repeated projection data at six different dose levels from 0.1 mAs to 1.6 mAs per projection at three fixed angles. To minimize the influence of the lag effect, lag correction was performed on the consecutively acquired projection data. The noise correlation coefficient between detector bin pairs was calculated from the corrected projection data. The noise correlation among CBCT projection data was then incorporated into the covariance matrix of the penalized weighted least-squares (PWLS) criterion for noise reduction of low-dose CBCT. The analyses of the repeated measurements show that noise correlation coefficients are non-zero between the nearest neighboring bins of CBCT projection data. The average noise correlation coefficients for the first- and second- order neighbors are 0.20 and 0.06, respectively. The noise correlation coefficients are independent of the dose level. Reconstruction of the pelvis phantom shows that the PWLS criterion with consideration of noise correlation results in a lower noise level as compared to the PWLS criterion without considering the noise correlation at the matched resolution.

Cone beam CT (CBCT) technique provides true three-dimensional (3D) images of a breast; however, metal clips and needles used for surgical planning can cause artifacts, which may extend to many adjacent slices, in the reconstructed images obtained by the Feldkamp-Davis-Kress (FDK) filtered backprojection method,. In this paper, a sinogram based method to remove the metal clips in the projection image data is described and discussed for improving the quality of reconstructed breast images. First, the original projection data was reconstructed using the FDK algorithm to obtain a volumetric image with metal clips and artifacts. Second, the volumetric image was segmented by using the threshold method to obtain a 3D map of metal objects. Third, a forward projection algorithm is applied to the metal object map to obtain projection map of metal objects. Finally, the original projection images and projection map of metal objects are reorganized into sinograms for correction in the angular space on a pixel-by-pixel basis. Cone beam CT images of a mastectomy breast specimen are used to demonstrate the feasibility of using this technique for removal of metal object artifacts. Preliminary results have demonstrated that metal objects artifacts in 3D images were reduced and the image quality were improved.