An x-ray energy spectrum plays an essential role in computed tomography (CT) imaging and related tasks. Because of the high photon flux of clinical CT scanners, most of the spectrum estimation methods are indirect and usually suffer from various limitations. In this study, we aim to provide a segmentation-free, indirect transmission measurement–based energy spectrum estimation method using dual-energy material decomposition. The general principle of this method is to minimize the quadratic error between the polychromatic forward projection and the raw projection to calibrate a set of unknown weights, which are used to express the unknown spectrum together with a set of model spectra. The polychromatic forward projection is performed using material-specific images, which are obtained using dual-energy material decomposition. The algorithm was evaluated using numerical simulations, experimental phantom data, and realistic patient data. The results show that the estimated spectrum matches the reference spectrum quite well and the method is robust. Extensive studies suggest that the method provides an accurate estimate of the CT spectrum without dedicated physical phantom and prolonged workflow. This paper may be attractive for CT dose calculation, artifacts reduction, polychromatic image reconstruction, and other spectrum-involved CT applications.
Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy (∼1% to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy (∼76% average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.
Thanks to the current advances in nanoscience, molecular biochemistry, and x-ray detector technology, x-ray fluorescence computed tomography (XFCT) has been considered for molecular imaging of probes containing high atomic number elements, such as gold nanoparticles. The commonly used XFCT imaging performed with K-shell x rays appears to have insufficient imaging sensitivity to detect the low gold concentrations observed in small animal studies. Low energy fluorescence L-shell x rays have exhibited higher signal-to-background ratio and appeared as a promising XFCT mode with greatly enhanced sensitivity. The aim of this work was to experimentally demonstrate the feasibility of L-shell XFCT imaging and to assess its achievable sensitivity. We built an experimental L-shell XFCT imaging system consisting of a miniature x-ray tube and two spectrometers, a silicon drift detector (SDD), and a CdTe detector placed at ±120 deg with respect to the excitation beam. We imaged a 28-mm-diameter water phantom with 4-mm-diameter Eppendorf tubes containing gold solutions with concentrations of 0.06 to 0.1% Au. While all Au vials were detectable in the SDD L-shell XFCT image, none of the vials were visible in the CdTe L-shell XFCT image. The detectability limit of the presented L-shell XFCT SDD imaging setup was 0.007% Au, a concentration observed in small animal studies.
X-ray induced photoacoustic tomography, also called X-ray acoustic computer tomography (XACT) is investigated in
this paper. Short pulsed (μs-range) X-ray beams from a medical linear accelerator were used to generate ultrasound. The ultrasound signals were collected with an ultrasound transducer (500 KHz central frequency) positioned around an
object. The transducer, driven by a computer-controlled step motor to scan around the object, detected the resulting
acoustic signals in the imaging plane at each scanning position. A pulse preamplifier, with a bandwidth of 20 KHz–2
MHz at −3 dB, and switchable gains of 40 and 60 dB, received the signals from the transducer and delivered the
amplified signals to a secondary amplifier. The secondary amplifier had bandwidth of 20 KHz–30 MHz at −3 dB, and a
gain range of 10–60 dB. Signals were recorded and averaged 128 times by an oscilloscope. A sampling rate of 100 MHz
was used to record 2500 data points at each view angle. One set of data incorporated 200 positions as the receiver moved
360°. The x-ray generated acoustic image was then reconstructed with the filtered back projection algorithm. The twodimensional
XACT images of the lead rod embedded in chicken breast tissue were found to be in good agreement with
the shape of the object. This new modality may be useful for a number of applications, such as providing the location of
a fiducial, or monitoring x-ray dose distribution during radiation therapy.
Monte Carlo simulation is considered the most reliable method for modeling photon migration in heterogeneous media. However, its widespread use is hindered by the high computational cost. The purpose of this work is to report on our implementation of a simple MapReduce method for performing fault-tolerant Monte Carlo computations in a massively-parallel cloud computing environment. We ported the MC321 Monte Carlo package to Hadoop, an open-source MapReduce framework. In this implementation, Map tasks compute photon histories in parallel while a Reduce task scores photon absorption. The distributed implementation was evaluated on a commercial compute cloud. The simulation time was found to be linearly dependent on the number of photons and inversely proportional to the number of nodes. For a cluster size of 240 nodes, the simulation of 100 billion photon histories took 22 min, a 1258 × speed-up compared to the single-threaded Monte Carlo program. The overall computational throughput was 85,178 photon histories per node per second, with a latency of 100 s. The distributed simulation produced the same output as the original implementation and was resilient to hardware failure: the correctness of the simulation was unaffected by the shutdown of 50% of the nodes.
The presence of metals in patient causes streaking artifacts in X-ray CT and has long been recognized as a problem that
limits various applications of CT imaging. Accurate localization of metals in CT images is a critical step for metal
artifacts reduction in CT imaging and many practical applications of CT images. The purpose of this work is to develop a
method of auto-determination of the shape and location of metallic object(s) in the image space. The proposed method is
based on the fact that when a metal object is present in a patient, a CT image can be divided into two prominent
components: high density metal and low density normal tissues. This prior knowledge is incorporated into an objective
function as the regularization term whose role is to encourage the solution to take a form of two intensity levels. The
function is minimized by using a Gauss-Seidel iterative algorithm. A computer simulation study and four experimental
studies are performed to evaluate the proposed approach. Both simulation and experimental studies show that the
presented algorithm works well even in the presence of complicated shaped metal objects. For a hexagonally shaped
metal embedded in a water phantom, for example, it is found that the accuracy of metal reconstruction is within submillimeter.
The algorithm is of practical importance for imaging patients with implanted metals.
Computed tomography (CT) streak artifacts caused by metal implants have long been recognized as a problem that limits
various applications of CT imaging. An effective and robust algorithm is highly desirable to minimize metal artifacts and
achieve clinically acceptable CT images. In this work, the raw projection data is viewed as "incomplete" in the presence
of metal shadows. Shape and location of metal objects are automatically identified and used as prior knowledge for
accurate segmentation of metal shadows in projection domain. An iterative algorithm based on constrained optimization
is then used for the image reconstruction. This algorithm minimizes a quadratic penalized smoothness measure function
of the image, subject to the constraint that the estimated projection data is within a specified tolerance of the available
metal-shadow-excluded projection data, with image non-negativity enforced. The constrained minimization problem is
optimized through the combination of projection onto convex sets (POCS) and steepest gradient descent of the
smoothness measure objective. Digital phantom study shows that the proposed constrained optimization algorithm has
superior performance in reducing metal artifacts, suppressing noise and improving soft-tissue visibility. Some
comparisons are performed with the filtered-back-projection (FBP), FDK, POCS and constrained optimization with
total-variation (TV) objective. Although the algorithm is presented in the context of metal artifacts, it can be generated to
image reconstruction from incomplete projections caused by limited angular range or low angular sampling rate in both
2D and 3D cases.
Cone-beam CT (CBCT) is being increasingly used in modern radiation therapy. However, as compared to
conventional CT, the degraded image quality of CBCT hampers its applications in radiation therapy. Due to the
large volume of x-ray illumination, scatter is considered as one of the fundamental limitations of CBCT image
quality. Many scatter correction algorithms have been proposed in the literature, while drawbacks still exist. In
this work, we propose a correction algorithm which is particularly useful in radiation therapy. Since the same
patient is scanned repetitively during one radiation treatment course, we measure the scatter distribution in
one scan, and use the measured scatter distribution to estimate and correct scatter in the following scans. A
partially blocked CBCT is used in the scatter measurement scan. The x-ray beam blocker has a strip pattern,
such that the whole-field scatter distribution can be estimated from the detected signals in the shadow region and
the patient rigid transformation can be determined from the reconstructed image using the illuminated detector
projection data. From the derived patient transformation, the measured scatter is then modified accordingly and
used for scatter correction in the following regular CBCT scans. The proposed method has been evaluated using
Monte Carlo simulations and physical experiments on an anthropomorphic chest phantom. The results show
a significant suppression of scatter artifacts using the proposed method. Using the reconstruction in a narrow
collimator geometry as a reference, the comparison also shows that the proposed method reduces reconstruction
error from 13.2% to 3.8%. The proposed method is attractive in applications where a high CBCT image quality
is critical, for example, dose calculation in adaptive radiation therapy.
There are growing interests in using cone-beam computed tomography (CBCT) for patient treatment position setup and
dose evaluation in radiation therapy. The repeated use of CBCT during the course of a treatment has raised concerns of
extra radiation dose delivered to patients. One way to reduce radiation dose delivered to patients during CBCT
procedure is to acquire CT projection data with a lower mAs level. However, the image quality of the projection image
and the reconstructed CBCT image will degrade due to excessive quantum noise as a result of low mAs protocol. In this
work, we first studied the noise properties of CBCT projection data from repeated scan and then improved low-dose
CBCT image quality by restoring CBCT projection images based on an improved noise model of CBCT projection data.
Analysis of repeated measurements show that noise is correlated among nearest neighbors in projection data, i.e.,
covariance matrix of projection data noise is non-diagonal. The covariance matrix of noise provides the knowledge of
second-order statistics of noise, which may lead to more accurate estimation for statistical image reconstruction and
restoration algorithm. We constructed the penalized weighted least-squares (PWLS) objective function by incorporating
the noise correlation of CBCT projection data. The optimal solution of the line integrals is then estimated by minimizing
the PWLS objective function. A quality assurance phantom was used to evaluate the presented algorithm for noise
reduction in low-dose CBCT.
Recently, we proposed a scatter correction method for x-ray imaging using primary modulation. A primary
modulator with spatially variant attenuating materials is inserted between the x-ray source and the object to
make the scatter and part of the primary distributions strongly separate in the Fourier domain. Linear filtering
and demodulation techniques suffice to extract and correct the scatter for this modified system. The method has
been verified by computer simulations and preliminary experimental results on a simple object. In this work, we
improve performance by using a new primary modulator with a higher modulation frequency and by refining the
algorithm. The improved method is evaluated experimentally using a pelvis phantom. The imaging parameters
are chosen to match the Varian Acuity CT simulator, where scatter correction has been shown to be challenging
due to complicated artifact patterns. The results using our approach are compared with those without scatter
correction, and with scatter estimated and corrected using a slit measurement as a pre-scan. The comparison
shows that the primary modulation method greatly reduces the scatter artifacts and improves image contrast.
Using only one single scan, this method achieves CT HU accuracy comparable to that obtained using a slit measurement as a pre-scan.
In this paper, we propose a novel technique for blind image restoration and resolution enhancement based on radial basis function (RBF) neural network. The RBF network gives a solution of the regularization problem often seen in function estimation with certain standard smoothness functional used as stabilizers. A RBF network model is designed to represent the observed image. In this model, the number and distribution of the centers (which are set to the pixels of the observed image) are fixed. In addition, network output is set to the observed image pixel gray scale value. The RBF plays a role of point spread function. The technique can also be applied to image resolution enhancement by generating an interpolated image from the low resolution version. Experimental results show that the learning algorithm can effectively estimate the model parameters and the established neural network model has a high fidelity in representing an image. It is believed that the proposed neural network model provides a valuable tool for image restoration and resolution enhancement and holds promises to improve the quality and efficiency of image processing.