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Towards deep iterative-reconstruction algorithms for computed tomography (CT) applications
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Paper Abstract

We introduce a new approach for designing deep learning algorithms for computed tomography applications. Rather than training generically-structured neural network architectures to equivalently perform imaging tasks, we show how to leverage classical iterative-reconstruction algorithms such as Newton-Raphson and expectation- maximization (EM) to bootstrap network performance to a good initialization-point, with a well-understood baseline of performance. Specifically, we demonstrate a natural and systematic way to design these networks for both transmission-mode x-ray computed tomography (XRCT) and emission-mode single-photon computed tomography (SPECT), highlighting that our method is capable of preserving many of the nice properties, such as convergence and understandability, that is featured in classical approaches. The key contribution of this work is a formulation of the reconstruction task that enables data-driven improvements in image clarity and artifact reduction without sacrificing understandability. In this early work, we evaluate our method on a number of synthetic phantoms, highlighting some of the benefits and difficulties of this machine-learning approach.

Paper Details

Date Published: 1 March 2019
PDF: 11 pages
Proc. SPIE 10948, Medical Imaging 2019: Physics of Medical Imaging, 1094856 (1 March 2019); doi: 10.1117/12.2513005
Show Author Affiliations
Abhejit Rajagopal, Univ. of California, Santa Barbara (United States)
Toyon Research Corp. (United States)
Noah Stier, Toyon Research Corp. (United States)
Joyoni Dey, Louisiana State Univ. (United States)
Michael A. King, Univ. of Massachusetts Medical School (United States)
Shivkumar Chandrasekaran, Univ. of California, Santa Barbara (United States)


Published in SPIE Proceedings Vol. 10948:
Medical Imaging 2019: Physics of Medical Imaging
Taly Gilat Schmidt; Guang-Hong Chen; Hilde Bosmans, Editor(s)

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