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

Jingyan Xu, Frederic Noo

Research output: Contribution to journalArticlepeer-review


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 languageEnglish (US)
Article number19NT01
JournalPhysics in medicine and biology
Issue number19
StatePublished - Oct 7 2021


  • 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


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