Degradation-Aware Deep Learning Framework for Sparse-View CT Reconstruction.

ABSTRACT: Sparse-view CT reconstruction is a fundamental task in computed tomography to overcome undesired artifacts and recover the details of textual structure in degraded CT images. Recently, many deep learning-based networks have achieved desirable performances compared to iterative reconstruction algorithms. However, the performance of these methods may severely deteriorate when the degradation strength of the test image is not consistent with that of the training dataset. In addition, these methods do not pay enough attention to the characteristics of different degradation levels, so solely extending the training dataset with multiple degraded images is also not effective. Although training plentiful models in terms of each degradation level can mitigate this problem, extensive parameter stora

SUBMITTER: Sun C 

PROVIDER: S-EPMC8704775 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature