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15 February 2021 DeepInterior: new pathway to address the interior tomographic reconstruction problem in CT via direct backprojecting divergent beam projection data
Chengzhu Zhang, Yinsheng Li, Ke Li, Guang-Hong Chen
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Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115951Q (2021) https://doi.org/10.1117/12.2581368
Event: SPIE Medical Imaging, 2021, Online Only
Abstract
The interior tomographic reconstruction problem concerns reconstruction of a local region of interest (ROI) from projection data that are conformally collimated to only illuminate the target ROI. In the past decade or so, it has been proven that a stable solution exists to address the interior tomographic reconstruction problem provided that a) the Tuy data sufficiency condition is satisfied and b) image values of some pixels inside the ROI are known(not all pixels to void triviality), although there seems to be no analytical reconstruction method available to reconstruct the image. In this work, we present a new pathway to address the interior tomographic reconstruction problem, referred to as DeepInterior. The new scheme consists of two steps: 1) Direct backprojection of the acquired fully truncated divergent beam projection data to form a backprojection image B0 without the conventional differentiation operations and 2) the true image is then reconstructed from the blurred image B0 using a trained deep neural network architecture. We demonstrated that the trained DeepInterior is shift-invariant and can accurately reconstruct ROIs at arbitrary locations.
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Chengzhu Zhang, Yinsheng Li, Ke Li, and Guang-Hong Chen "DeepInterior: new pathway to address the interior tomographic reconstruction problem in CT via direct backprojecting divergent beam projection data", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115951Q (15 February 2021); https://doi.org/10.1117/12.2581368
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KEYWORDS
Tomography
Data acquisition
Collimation
Neural networks
Visualization
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