Dose Reduction Via Development of a Novel Image Reconstruction Method for Few-View Computed Tomography, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Abolfazl (Supervisor) ; Ay, Mohammad Reza (Supervisor)
Abstract
Sparse-view computed tomography (CT) is recently proposed as a promising method to speed up data acquisition and alleviate the issue of CT high-dose delivery to patients. However, traditional reconstruction algorithms are time-consuming and suffer from image degradation when faced with sparse-view data. To address this problem, we propose two new frameworks based on deep learning (DL) that can quickly produce high-quality CT images from sparsely sampled projections and is able for clinical use. Our first DL-based proposed model is based on the convolution, and residual neural networks in a parallel manner, named the parallel residual neural network (PARS-Net). Besides, our proposed PARS-Net...
Cataloging briefDose Reduction Via Development of a Novel Image Reconstruction Method for Few-View Computed Tomography, Ph.D. Dissertation Sharif University of Technology ; Hosseini, Abolfazl (Supervisor) ; Ay, Mohammad Reza (Supervisor)
Abstract
Sparse-view computed tomography (CT) is recently proposed as a promising method to speed up data acquisition and alleviate the issue of CT high-dose delivery to patients. However, traditional reconstruction algorithms are time-consuming and suffer from image degradation when faced with sparse-view data. To address this problem, we propose two new frameworks based on deep learning (DL) that can quickly produce high-quality CT images from sparsely sampled projections and is able for clinical use. Our first DL-based proposed model is based on the convolution, and residual neural networks in a parallel manner, named the parallel residual neural network (PARS-Net). Besides, our proposed PARS-Net...
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