DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Hoyeon | ko |
dc.contributor.author | Kim, Hyeongseok | ko |
dc.contributor.author | Cho, Seungryong | ko |
dc.date.accessioned | 2019-11-13T07:20:21Z | - |
dc.date.available | 2019-11-13T07:20:21Z | - |
dc.date.created | 2019-11-12 | - |
dc.date.created | 2019-11-12 | - |
dc.date.issued | 2019-06-05 | - |
dc.identifier.citation | 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 | - |
dc.identifier.uri | http://hdl.handle.net/10203/268394 | - |
dc.description.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 'amp-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. | - |
dc.language | English | - |
dc.publisher | SPIE | - |
dc.title | Reconstructing interior transmission tomographic images with an offset-detector using a deep-neural-network | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Fully3D 2019 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Philadelphia, PA | - |
dc.identifier.doi | 10.1117/12.2534888 | - |
dc.contributor.localauthor | Cho, Seungryong | - |
dc.contributor.nonIdAuthor | Kim, Hyeongseok | - |
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