Abstract
Four-Dimensional Cone-Beam Computed Tomography (4D-CBCT) imaging is important in upper abdomen tumor radiation treatment as it can obtain the tumor motion information immediately before treatment. However, the number of projections at a single phase for 4D-CBCT imaging is very small, and the CT images reconstructed by using conventional algorithms will be contaminated by view aliasing artifacts and noises. To address this problem, we propose a framework to jointly estimate and compensate the inter-phase motion for 4D-CBCT image reconstruction. Specifically, by introducing the intensity-based optical flow (OF) constraint into the reconstruction framework, the model can deal with the inter-frame displacements and improve the image quality simultaneously. The primal-dual algorithm method was used to optimize the cost function of the proposed model. Experiments on physical phantoms and patient data show that the proposed approach can effectively reduce the noise and artifacts in 4D–CBCT images and achieve the inter-phase motion vector fields (MVFs).
Highlights
Lung cancer is one of the most common malignant tumors, and its morbidity and mortality are constantly increasing
In order to evaluate the performance of the algorithm, we implemented the FDK algorithm and ART-total variation (TV) algorithm, and compared the reconstruction respect to the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR)
The cone-beam CT (CBCT) imaging system amounted on the tumor radiotherapy system exists this problem, and exhibits relatively slow gantry rotation, which results in the limited number of projection data at each respiratory phase
Summary
Lung cancer is one of the most common malignant tumors, and its morbidity and mortality are constantly increasing. About 60% of the cancer patients need radiation therapy [1, 2]. As the cone-beam CT (CBCT) can provide the location of the tumor in the patient's body, three dimensional CBCT guided conformal radiotherapy is mostly used in clinical tumor radiotherapy [3,4,5]. In chest CBCT volume imaging, lung tumors significantly displaced with respiratory motion, and blur in CBCT image caused by respiratory motion seriously affects the target localization, and thereby reduces the precision of radiotherapy and buries hidden dangers for tumor recurrence. In order to manage the respiratory motion, the emergence of four dimensional cone-beam computed tomography (4D-CBCT) has enabled on-board verification of target location and motion range while reducing motion artifacts [6]. For the 4D-CBCT imaging, CB projections are usually sorted into 8-10 subsets, which are used to reconstruct 3D-CBCT volumes representing different phases of the respiratory cycle
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