Abstract
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.
Original language | English (US) |
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Article number | 19NT01 |
Journal | Physics in medicine and biology |
Volume | 66 |
Issue number | 19 |
DOIs | |
State | Published - Oct 7 2021 |
Keywords
- bi-level optimization
- dynamic programming
- hyperparameter learning
- low dose CT
- sinogram smoothing
ASJC Scopus subject areas
- Radiological and Ultrasound Technology
- Radiology Nuclear Medicine and imaging