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28 May 2019 Reconstructing interior transmission tomographic images with an offset-detector using a deep-neural-network
Hoyeon Lee, Hyeongseok Kim, Seungryong Cho
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Proceedings Volume 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine; 1107230 (2019) https://doi.org/10.1117/12.2534888
Event: Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 2019, Philadelphia, United States
Abstract
Interior tomography that acquires truncated data of a specific interior region-of-interest (ROI) is an attractive option to low-dose imaging. However, image reconstruction from such measurement does not yield an accurate solution because of data insufficiency. There have been developed a host of approaches to getting an approximate useful solution including various weighting methods, iterative reconstruction methods, and methods with prior knowledge. In this study, we use a deep-neural-network, which has shown its potentials in various fields including medical imaging, to reconstruct interior tomographic images. We assumed an offset-detector geometry which has wide applications in cone-beam CT (CBCT) imaging for its extended field-of-view (FOV) in this work. We trained a network to synthesize ‘ramp-filtered’ data within the detector active area so that the corresponding ROI reconstruction would be truncation-artifact-free in the filteredbackprojection (FBP) reconstruction framework. We have compared the results with post- and pre-convolution weighting methods and shown outperformance of the neural network approach.
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Hoyeon Lee, Hyeongseok Kim, and Seungryong Cho "Reconstructing interior transmission tomographic images with an offset-detector using a deep-neural-network", Proc. SPIE 11072, 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, 1107230 (28 May 2019); https://doi.org/10.1117/12.2534888
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KEYWORDS
Tomography
Neural networks
Computed tomography
Medical imaging
Image restoration
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