Zobrazeno 1 - 10
of 486
pro vyhledávání: '"Sidky, Emil Y."'
Autor:
Sidky, Emil Y., Wu, Xiangyi, Duan, Xiaoyu, Huang, Hailing, Zhao, Wei, Zhang, Leo Y., Phillips, John Paul, Zhang, Zheng, Chen, Buxin, Xia, Dan, Reiser, Ingrid S., Pan, Xiaochuan
An optimization-based image reconstruction algorithm is developed for contrast enhanced digital breast tomosynthesis (DBT) using dual-energy scanning. The algorithm minimizes directional total variation (TV) with a data discrepancy and non-negativity
Externí odkaz:
http://arxiv.org/abs/2406.07306
Deep neural networks used for reconstructing sparse-view CT data are typically trained by minimizing a pixel-wise mean-squared error or similar loss function over a set of training images. However, networks trained with such pixel-wise losses are pro
Externí odkaz:
http://arxiv.org/abs/2402.10010
Autor:
Terzioglu, Fatma, Sidky, Emil Y., Phillips, Jp, Reiser, Ingrid, Bal, Guillaume, Pan, Xiaochuan
Purpose: This study proposes a systematic method for determining the optimal x-ray tube settings/energy windows and fluence for minimal noise and maximum CNR in material density images obtained from DECT scans by fixing the subject size and the total
Externí odkaz:
http://arxiv.org/abs/2308.00212
Autor:
Zhang, Zheng, Epel, Boris, Chen, Buxin, Xia, Dan, Sidky, Emil Y., Qiao, Zhiwei, Halpern, Howard, Pan, Xiaochuan
Objective: We investigate and develop optimization-based algorithms for accurate reconstruction of four-dimensional (4D)-spectral-spatial (SS) images directly from data collected over limited angular ranges (LARs) in continuous-wave (CW) electron par
Externí odkaz:
http://arxiv.org/abs/2304.00209
An alternating direction method of multipliers (ADMM) framework is developed for nonsmooth biconvex optimization for inverse problems in imaging. In particular, the simultaneous estimation of activity and attenuation (SAA) problem in time-of-flight p
Externí odkaz:
http://arxiv.org/abs/2303.17042
Autor:
Pan, Xiaochuan, Sidky, Emil Y.
Quantitative image reconstruction in dual-energy computed tomography (CT) remains a topic of active research. We read with interest ``DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT imaging,'' which
Externí odkaz:
http://arxiv.org/abs/2212.08953
Autor:
Sidky, Emil Y., Pan, Xiaochuan
This Special Report summarizes the 2022 AAPM Grand Challenge on Deep-Learning spectral Computed Tomography (DL-spectral CT) image reconstruction. The purpose of the challenge is to develop the most accurate image reconstruction algorithm for solving
Externí odkaz:
http://arxiv.org/abs/2212.06718
The generalized minimal residual (GMRES) algorithm is applied to image reconstruction using linear computed tomography (CT) models. The GMRES algorithm iteratively solves square, non-symmetric linear systems and it has practical application to CT whe
Externí odkaz:
http://arxiv.org/abs/2201.07408
Autor:
Sidky, Emil Y., Pan, Xiaochuan
Purpose: The purpose of the challenge is to find the deep-learning technique for sparse-view CT image reconstruction that can yield the minimum RMSE under ideal conditions, thereby addressing the question of whether or not deep learning can solve inv
Externí odkaz:
http://arxiv.org/abs/2109.09640
In dual-energy computed tomography (DECT), low- and high- kVp data are collected often over a full-angular range (FAR) of $360^\circ$. While there exists strong interest in DECT with low- and high-kVp data acquired over limited-angular ranges (LARs),
Externí odkaz:
http://arxiv.org/abs/2108.07113