Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Artifact suppression for breast specimen imaging in micro CBCT using deep learning

Abstract Background Cone-beam computed tomography (CBCT) has been introduced for breast-specimen imaging to identify a free resection margin of abnormal tissues in breast conservation. As well-known, typical micro CT consumes long acquisition and computation times. One simple solution to reduce the...

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Bibliographic Details
Main Authors: Sorapong Aootaphao, Puttisak Puttawibul, Pairash Thajchayapong, Saowapak S. Thongvigitmanee
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Imaging
Subjects:
Cone-beam CT
Iterative reconstruction
Scattering radiation
Metal artifact
Truncation artifact
Sparse-view sinogram
Online Access:https://doi.org/10.1186/s12880-024-01216-5

Internet

https://doi.org/10.1186/s12880-024-01216-5

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