Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

Iris Huijben, Bastiaan S. Veeling, Ruud J.G. van Sloun

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)
30 Downloads (Pure)

Abstract

Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines.
Original languageEnglish
Title of host publication2020 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2020 - Proceedings
Pages8906-8910
Number of pages5
ISBN (Electronic)9781509066315
DOIs
Publication statusPublished - May 2020
Event2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020) - Virtual, Barcelona, Spain
Duration: 4 May 20208 May 2020
https://2020.ieeeicassp.org/

Conference

Conference2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2020)
Abbreviated titleICASSP 2020
Country/TerritorySpain
CityBarcelona
Period4/05/208/05/20
Internet address

Keywords

  • Compressed sensing
  • magnetic resonance imaging
  • model-based deep learning

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