954 citations · 2k across the 32 of their papers we have counts for
26 papers · 1 filter
Optimizing dual-energy CT technique for iodine-based contrast-to-noise ratio
Fatma Terzioglu, Emil Y. Sidky, Jp Phillips +3
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 im…
4D-image reconstruction directly from limited-angular-range data in continuous-wave electron paramagnetic resonance imaging
Zheng Zhang, Boris Epel, Buxin Chen +5
Objective: We investigate and develop optimization-based algorithms for accurate reconstruction of four-dimensional (4D)-spectral-spatial (SS) images directly from data collected o…
Report on the AAPM deep-learning spectral CT Grand Challenge
Emil Y. Sidky, Xiaochuan Pan
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…
Comments on "DIRECT-Net: A unified mutual-domain material decomposition network for quantitative dual-energy CT Imaging'', Med. Phys. 2022, Vol. 49, pgs. 917-934
Xiaochuan Pan, Emil Y. Sidky
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 d…
Iterative image reconstruction for CT with unmatched projection matrices using the generalized minimal residual algorithm
Emil Y. Sidky, Per Christian Hansen, Jakob S. Jørgensen +1
The generalized minimal residual (GMRES) algorithm is applied to image reconstruction using linear computed tomography (CT) models. The GMRES algorithm iteratively solves square, n…
Report on the AAPM deep-learning sparse-view CT (DL-sparse-view CT) Grand Challenge
Emil Y. Sidky, Xiaochuan Pan
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…