The penalized weighted least-squares (PWLS) image reconstruction with the widely used edge-preserving nonlocal means (NLM) penalty has shown the potential to significantly improve the image quality for low dose CT (LDCT). Considering the nonlocal weights have significant effects for the smoothness and resolution of the reconstruction, much effort has been made to improve their accuracy. A high quality image of normal dose with less noise and artifacts is sometimes used for the weight’s calculation to further improvement. However, registration should be employed first when misalignment between the low-dose and normal-dose scans cannot be ignored. It will bring an extra work and the effect of registration error on the proposed method are uncertain. The paper aims to propose a new NLM prior model based on normal-dose CT (NDCT) without registration, by predicting nonlocal weights with selecting most similar patch samples from FDCT database. The patch samples are determined by evaluating the similarity between patches from NDCT and the target patch of LDCT. After building up the normal dose based NLM penalty, the PWLS object function is iteratively minimized for reconstruction. Preliminary reconstruction with LDCT data has shown its potential in the structure detail preservation.
Cone beam X-ray luminescence computed tomography (CB-XLCT) has recently been proposed as a new imaging modality for biological imaging application. Compared with other XLCT systems such as pencil beam XLCT and narrow beam XLCT, CB-XLCT can achieve fast imaging, where the speed is essential to small animal <i>in vivo</i> imaging studies. However, due to the high degree of light scattering in biological tissues, the CB-XLCT reconstruction is an ill-posed problem, which can result in poor image quality such as low spatial resolution. As a hybrid CT/optical imaging technique, the image quality is conjected to be improved substantially with the structural guidance from the anatomical images of the CT component. For that purpose, in this paper, a direct prior regularization method is proposed by introducing anatomical information directly into the CB-XLCT reconstruction. The primary advantage of the proposed method is that it does not require segmentation of targets in the anatomical images. Phantom experiments with different edge-to-edge distance (EED) were performed to realize the proposed approach's feasibility. Phantom experiments results indicate that the proposed direct regularization method can separate two luminescent targets with an EED of 0 mm. Compared with no-prior reconstruction methods such as ART and adaptive Tikhonov methods, the proposed method can significantly improve the imaging resolution of CB-XLCT.
Cone beam X-ray luminescence computed tomography (CB-XLCT) has recently been proposed as a new molecular imaging modality for various biomedical applications. It utilizes X-ray excitable nanophosphors to produce visible or near-infrared (NIR) luminescence and combines the high sensitivity of optical imaging with the high spatial resolution of X-ray imaging. With the development of the nanophosphors and reconstruction methods, dynamic XLCT imaging, which can reflect the dynamic course of absorption, distribution, and elimination of the nanophosphors in vivo, has demonstrated its initial prospect in biological and biochemical studies. However, challenges remain in resolving nanophosphors (drug) distributions inside the imaging object due to the high light scattering and complex dynamics of nanophosphor’s delivery. Considering that target with different functions may have different kinetic behaviors, in this paper we present a method to resolve targets with different kinetics by utilizing principal component analysis (PCA). The metabolic processes of nanophosphors (Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+</sup>) of two targets were simulated and imaged using a CB-XLCT system, with two targets located at different edge-to-edge distances of 0.12 cm. Simulation and experiment studies validate the performance of the proposed algorithm. The results suggest that two adjacent targets of different kinetic behaviors can be extracted and illustrated by the proposed method, at an edge-to-edge distance of 0.12 cm.
As an emerging hybrid imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) has been proposed based on the development of X-ray excitable nanoparticles. Fast three-dimensional (3-D) CB-XLCT imaging has attracted significant attention for the application of XLCT in fast dynamic imaging study. Currently, due to the short data collection time, single-view CB-XLCT imaging achieves fast resolving the three-dimensional (3-D) distribution of X-ray-excitable nanoparticles. However, owing to only one angle projection data is used in the reconstruction, the single-view CB-XLCT inverse problem is inherently ill-conditioned, which makes image reconstruction highly susceptible to the effects of noise and numerical errors. To solve the ill-posed inverse problem, using the sparseness of the X-ray-excitable nanoparticles distribution as the prior, a new reconstruction approach based on total variance is proposed in this study. To evaluate the performance of the proposed approach, a phantom experiment was performed based on a CB-XLCT imaging system. The experiments indicate that the reconstruction from single-view XCLT can provide satisfactory results based on the proposed approach. In conclusion, with the reconstruction approach based on total variance, we implement a fast XLCT reconstruction of high quality with only one angle projection data used, which would be helpful for fast dynamic imaging study. In future, we will focus on how to applying the proposed TV-based reconstruction method and CB-XLCT imaging system to image fast biological distributions of the X-ray excitable nanophosphors in vivo.
Markov random field (MRF) model-based penalty is widely used in statistical iterative reconstruction (SIR) of low dose CT (LDCT) reconstruction for noise suppression and edge-preserving. In this strategy, normal dose CT scans are usually used as a priori information to further improve the LDCT quality. However, repeated CT scans are needed and registration or segmentation is usually applied first when misalignment between the low-dose and normal-dose scans exists. The study aims to propose a new MRF prior model of SIR based on the NDCT database without registration. In the proposed model, MRF weights are predicted using optimal similar patch samples from the NDCT database. The patch samples are determined by evaluating the similarity with Euclidean distance between patches from NDCT and the target patch of LDCT. The proposed prior term is incorporated into the SIR cost function, which is to be minimized for LDCT reconstruction. The proposed method is tested on an artificial LDCT data based on a high-dose patient data. Preliminary result has proved its potential performance in edge and structure detail preservation.
With the advances of x-ray excitable nanophosphors, x-ray luminescence computed tomography (XLCT) has become a promising hybrid imaging modality. In particular, a cone-beam XLCT (CB-XLCT) system has demonstrated its potential in in vivo imaging with the advantage of fast imaging speed over other XLCT systems. Currently, the imaging models of most XLCT systems assume that nanophosphors emit light based on the intensity distribution of x-ray within the object, not completely reflecting the nature of the x-ray excitation process. To improve the imaging quality of CB-XLCT, an imaging model that adopts an excitation model of nanophosphors based on x-ray absorption dosage is proposed in this study. To solve the ill-posed inverse problem, a reconstruction algorithm that combines the adaptive Tikhonov regularization method with the imaging model is implemented for CB-XLCT reconstruction. Numerical simulations and phantom experiments indicate that compared with the traditional forward model based on x-ray intensity, the proposed dose-based model could improve the image quality of CB-XLCT significantly in terms of target shape, localization accuracy, and image contrast. In addition, the proposed model behaves better in distinguishing closer targets, demonstrating its advantage in improving spatial resolution.
Large samples of raw low-dose CT (LDCT) projections on lungs are needed for evaluating or designing novel and
effective reconstruction algorithms suitable for lung LDCT imaging. However, there exists radiation risk when getting
them from clinical CT scanning. To avoid the problem, a new strategy for producing large samples of lung LDCT
projections with computer simulations is proposed in this paper. In the simulation, clinical images from the publicly
available medical image database-the Lung Image Database Consortium(LIDC) and Image Database Resource Initiative
(IDRI) database (LIDC/IDRI) are used as the projected object to form the noise-free sinogram. Then by adding a Poisson
distributed quantum noise plus Gaussian distributed electronic noise to the projected transmission data calculated from
the noise-free sinogram, different noise levels of LDCT projections are obtained. At last the LDCT projections are used
for evaluating two reconstruction strategies. One is the conventional filtered back projection (FBP) algorithm and the
other is FBP reconstruction from the filtered sinogram with penalized weighted least square criterion (PWLS-FBP).
Images reconstructed with the LDCT simulations have shown that the PWLS-FBP algorithm performs better than the
FBP algorithm in reducing streaking artifacts and preserving resolution. Preliminary results indicate that the feasibility of
the proposed lung LDCT simulation strategy for helping to determine advanced reconstruction algorithms.
X-ray luminescence computed tomography (XLCT) opens new possibilities to perform molecular imaging with x-ray. It is a dual modality imaging technique based on the principle that some nanophosphors can emit near-infrared (NIR) light when excited by x-rays. The x-ray scattering effect is a great issue in both CT and XLCT reconstruction. It has been shown that if the scattering effect compensated, the reconstruction average relative error can be reduced from 40% to 12% in the in the pencil beam XLCT. However, the scattering effect in the cone beam XLCT has not been proved. To verify and reduce the scattering effect, we proposed scattering-compensated cone beam x-ray luminescence computed tomography using an added leading to prevent the spare x-ray outside the irradiated phantom in order to decrease the scattering effect. Phantom experiments of two tubes filled with Y<sub>2</sub>O<sub>3</sub>:Eu<sup>3+</sup> indicated that the proposed method could reduce the scattering by a degree of 30% and can reduce the location error from 1.8mm to 1.2mm. Hence, the proposed method was feasible to the general case and actual experiments and it is easy to implement.
X-ray scatter poses a significant limitation to image quality in cone-beam CT (CBCT), as well as beam hardening, resulting in image artifacts, contrast reduction, and lack of CT number accuracy. Meanwhile the x-ray radiation dose is also non-ignorable. Considerable scatter or beam hardening correction methods have been developed, independently, and rarely combined with low-dose CT reconstruction. In this paper, we combine scatter suppression with beam hardening correction for sparse-view CT reconstruction to improve CT image quality and reduce CT radiation. Firstly, scatter was measured, estimated, and removed using measurement-based methods, assuming that signal in the lead blocker shadow is only attributable to x-ray scatter. Secondly, beam hardening was modeled by estimating an equivalent attenuation coefficient at the effective energy, which was integrated into the forward projector of the algebraic reconstruction technique (ART). Finally, the compressed sensing (CS) iterative reconstruction is carried out for sparse-view CT reconstruction to reduce the CT radiation. Preliminary Monte Carlo simulated experiments indicate that with only about 25% of conventional dose, our method reduces the magnitude of cupping artifact by a factor of 6.1, increases the contrast by a factor of 1.4 and the CNR by a factor of 15. The proposed method could provide good reconstructed image from a few view projections, with effective suppression of artifacts caused by scatter and beam hardening, as well as reducing the radiation dose. With this proposed framework and modeling, it may provide a new way for low-dose CT imaging.
Evaluating spatial resolution is an essential work for cone-beam computed tomography (CBCT) manufacturers, prototype designers or equipment users. To investigate the cross-sectional spatial resolution for different transaxial slices with CBCT, the slanted edge technique with a 3D slanted edge phantom are proposed and implemented on a prototype cone-beam micro-CT. Three transaxial slices with different cone angles are under investigation. An over-sampled edge response function (ERF) is firstly generated from the intensity of the slightly tiled air to plastic edge in each row of the transaxial reconstruction image. Then the oversampled ESF is binned and smoothed. The derivative of the binned and smoothed ERF gives the line spread function (LSF). At last the presampled modulation transfer function (MTF) is calculated by taking the modulus of the Fourier transform of the LSF. The spatial resolution is quantified with the spatial frequencies at 10% MTF level and full-width-half-maximum (FWHM) value. The spatial frequencies at 10% of MTFs are 3.1±0.08mm<sup>-1</sup>, 3.0±0.05mm<sup>-1</sup>, and 3.2±0.04mm<sup>-1</sup> for the three transaxial slices at cone angles of 3.8°, 0°, and -3.8° respectively. The corresponding FWHMs are 252.8μm, 261.7μm and 253.6μm. Results indicate that cross-sectional spatial resolution has no much differences when transaxial slices being 3.8° away from z=0 plane for the prototype conebeam micro-CT.
Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant’s modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.