Patient-specific hyperparameter learning for optimization-based CT image reconstruction.

ABSTRACT: We propose a hyperparameter learning framework that learnspatient-specifichyperparameters 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 hyperpar

SUBMITTER: Xu J 

PROVIDER: S-EPMC8584383 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature