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Proceedings Paper

A hierarchical approach to deep learning and its application to tomographic reconstruction
Author(s): Lin Fu; Bruno De Man
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Paper Abstract

Deep learning (DL) has been successfully applied to many image analysis and image enhancement tasks, but applying DL to inverse problems such as tomographic reconstruction remains challenging due to its high dimensionality and non-local spatial relationship. This paper introduces a hierarchical network architecture that enables purely DL-based tomographic reconstruction for full-size computed tomography (CT) datasets. The proposed method recursively decomposes the reconstruction problem into hierarchical sub problems that can each be solved by a neural network. Overall, the hierarchical approach requires exponentially fewer parameters than a generic network would, and in theory, is of lower computational order of complexity than analytical filtered-backprojection (FBP) reconstruction. As an example, we built a hierarchical network to reconstruct 2D CT images directly from sinograms without relying on conventional analytical or iterative reconstruction components. The hierarchical approach is extensible to three dimensions and to other applications such as emission and magnetic resonance reconstruction. Such DL-based reconstruction opens the door to an entirely new type of reconstruction, which could potentially lead to a better tradeoff between image quality and computational complexity.

Paper Details

Date Published: 28 May 2019
PDF: 5 pages
Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107202 (28 May 2019); doi: 10.1117/12.2534615
Show Author Affiliations
Lin Fu, GE Global Research (United States)
Bruno De Man, GE Global Research (United States)

Published in SPIE Proceedings Vol. 11072:
15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine
Samuel Matej; Scott D. Metzler, Editor(s)

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