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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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MEMORY-EFFICIENT DEEP END-TO-END POSTERIOR NETWORK (DEEPEN) FOR INVERSE PROBLEMS.

Jyothi Rikhab Chand1, Mathews Jacob1

  • 1Department of Electrical and Computer Engineering, University of Iowa, IA, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

We developed a memory-efficient deep learning method for Magnetic Resonance (MR) image reconstruction. This approach enables learning the posterior distribution, improving image recovery and providing uncertainty maps.

Keywords:
Energy modelMAP estimateParallel MRI reconstructionUncertainty estimate

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Area of Science:

  • Medical Imaging
  • Computational Neuroscience
  • Machine Learning

Background:

  • End-to-End (E2E) unrolled optimization frameworks are promising for Magnetic Resonance (MR) image recovery.
  • These deterministic methods face challenges with high memory usage during training and lack posterior distribution sampling capabilities.

Purpose of the Study:

  • To introduce a memory-efficient approach for E2E learning of the posterior distribution in MR image reconstruction.
  • To enable uncertainty quantification alongside image recovery.

Main Methods:

  • A novel framework combining a data-consistency likelihood term and a CNN-parameterized prior energy model.
  • E2E learning of CNN weights via maximum likelihood optimization.
  • Maximum A Posteriori (MAP) optimization for image recovery from undersampled MR data.

Main Results:

  • The proposed method achieves comparable performance to memory-intensive E2E unrolled algorithms.
  • It outperforms existing memory-efficient counterparts in MR image reconstruction.
  • The framework successfully generates uncertainty maps derived from posterior distribution sampling.

Conclusions:

  • This memory-efficient E2E learning framework advances MR image reconstruction.
  • It offers a viable solution for high-dimensional (3D+) MR imaging.
  • The ability to sample the posterior distribution provides valuable uncertainty information.