期刊
INVERSE PROBLEMS
卷 33, 期 12, 页码 -出版社
IOP Publishing Ltd
DOI: 10.1088/1361-6420/aa9581
关键词
tomography; deep learning; gradient descent; regularization
资金
- Swedish Foundation of Strategic Research [AM13-0049, ID14-0055]
- Elekta
- Swedish Foundation for Strategic Research (SSF) [AM13-0049, ID14-0055] Funding Source: Swedish Foundation for Strategic Research (SSF)
We propose a partially learned approach for the solution of ill-posed inverse problems with not necessarily linear forward operators. The method builds on ideas from classical regularisation theory and recent advances in deep learning to perform learning while making use of prior information about the inverse problem encoded in the forward operator, noise model and a regularising functional. The method results in a gradient-like iterative scheme, where the 'gradient' component is learned using a convolutional network that includes the gradients of the data discrepancy and regulariser as input in each iteration. We present results of such a partially learned gradient scheme on a non-linear tomographic inversion problem with simulated data from both the Sheep-Logan phantom as well as a head CT. The outcome is compared against filtered backprojection and total variation reconstruction and the proposed method provides a 5.4 dB PSNR improvement over the total variation reconstruction while being significantly faster, giving reconstructions of 512 x 512 pixel images in about 0.4 s using a single graphics processing unit (GPU).
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