A generalized simultaneous algebraic reconstruction technique (GSART) for dual-energy X-ray computed tomography.

BACKGROUND: Dual-energy computed tomography (DECT) is a widely used and actively researched imaging modality that can estimate the physical properties of an object more accurately than single-energy CT (SECT). Recently, iterative reconstruction methods called one-step methods have received attention among various approaches since they can resolve the intermingled limitations of the conventional methods. However, the one-step methods typically have expensive computational costs, and their material decomposition performance is largely affected by the accuracy in the spectral coefficients estimation. OBJECTIVE: In this study, we aim to develop an efficient one-step algorithm that can effectively decompose into the basis material maps and is less sensitive to the accuracy of the spectral coefficients. METHODS: By use of a new loss function that employs the non-linear forward model and the weighted squared errors, we propose a one-step reconstruction algorithm named generalized simultaneous algebraic reconstruction technique (GSART). The proposed algorithm was compared with the image-domain material decomposition and other existing one-step reconstruction algorithm. RESULTS: In both simulation and experimental studies, we demonstrated that the proposed algorithm effectively reduced the beam-hardening artifacts thereby increasing the accuracy in the material decomposition. CONCLUSIONS: The proposed one-step reconstruction for material decomposition in dual-energy CT outperformed the image-domain approach and the existing one-step algorithm. We believe that the proposed method is a practically very useful addition to the material-selective image reconstruction field.

Clinical implementation of a wide-field electron arc technique with a scatterer for widespread Kaposi's sarcoma in the distal extremities.

A novel wide-field electron arc technique with a scatterer is implemented for widespread Kaposi's sarcoma (KS) in the distal extremities. Monte Carlo beam modeling for electron arc beams was established to achieve <2% deviation from the measurements, and used for dose calculation. MC-based electron arc plan was performed using CT images of a foot and leg mimicking phantom and compared with in-vivo measurement data. We enrolled one patient with recurrent KS on the lower extremities who had been treated with photon radiation therapy. The 4- and 6-MeV electron arc plans were created, and then compared to two photon plans: two opposite photon beam and volumetric modulated arc with bolus. Compared to the two photon techniques, the electron arc plans resulted in superior dose saving to normal organs beneath the skin region, although it shows inferior coverage and homogeneity for PTV. The electron arc treatment technique with scatterer was successfully implemented for the treatment of widespread KS in the distal extremities with lower radiation exposure to the normal organs beyond the skin lesions, which could be a treatment option for recurrent skin cancer in the extremities.

Backprojection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis.

While an accurate image reconstruction of digital breast tomosynthesis (DBT) is fundamentally impossible due to its limited data, the DBT is increasingly used in clinics for its rich image information at a relatively low dose. One of the dominant image artifacts in DBT that hinders a faithful diagnosis is high-density object artifact in conjunction with a limited angle problem. In this paper, we developed a very efficient method for reconstructing DBT images with much reduced high-density object artifacts. The method is based on backprojection filtration reconstruction algorithm, voting strategy, and image blending. Data derivatives were backprojected with appropriate weights to reduce ripple artifacts by use of the voting strategy. We generated another differentiated backprojection volume, where the edges of high-density objects are replaced by the background. After Hilbert transform, we blended the two images to reduce undershoot artifacts. Physical phantoms were scanned and we compared conventional filtered backprojection, filtered backprojection with weighted backprojection, and our proposed method. Ripple artifacts were dramatically suppressed and undershoot artifacts were also greatly suppressed in the proposed method.

A Feasibility Study of Low-Dose Single-Scan Dual-Energy Cone-Beam CT in Many-View Under-Sampling Framework.

A single-scan dual-energy low-dose cone-beam CT (CBCT) imaging technique that exploits a multi-slit filter is proposed in this paper. The multi-slit filter installed between the x-ray source and the scanned object is reciprocated during a scan. The x-ray beams through the slits would generate relatively low-energy x-ray projection data, while the filtered beams would make high-energy projection data. An iterative image reconstruction algorithm that uses an adaptive-steepest-descent method to minimize image total-variation under the constraint of data fidelity was applied to reconstructing the image from the low-energy projection data. Since the high-energy projection data suffer from a substantially high noise level due to the beam filtration, we have developed a new algorithm that exploits the joint sparsity between the low- and high-energy CT images for image reconstruction of the high-energy CT image. The proposed image reconstruction algorithm uses a gradient magnitude image (GMI) of the low-energy CT image by regularizing the difference of GMIs of the low- and high-energy CT images to be minimized. The feasibility of the proposed technique has been demonstrated by the use of various phantoms in the experimental CBCT setup. Furthermore, based on the proposed dual-energy imaging, a material differentiation was performed and its potential utility has been shown. The proposed imaging technique produced promising results for its potential application to a low-dose single-scan dual-energy CBCT.