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Patient-specific hyperparameter learning for optimization-based CT image reconstruction
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DOI：10.1088/1361-6560/ac0f9a.png)
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摘要
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We propose a hyperparameter learning framework that learns patient-specific hyperparameters for optimization-based image reconstruction problems for x-ray CT applications. The framework consists of two functional modules: (1) a hyperparameter learning module parameterized by a convolutional neural network, (2) an image reconstruction module that takes as inputs both the noisy sinogram and the hyperparameters from (1) and generates the reconstructed images. As a proof-of-concept study, in this work we focus on a subclass of optimization-based image reconstruction problems with exactly computable solutions so that the whole network can be trained end-to-end in an efficient manner. Unlike existing hyperparameter learning methods, our proposed framework generates patient-specific hyperparameters from the sinogram of the same patient. Numerical studies demonstrate the effectiveness of our proposed approach compared to bi-level optimization.
Keyword:
bi-level optimization
dynamic programming
hyperparameter learning
low dose CT
sinogram smoothing
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
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