SS-JIRCS: Self-Supervised Joint Image Reconstruction and Coil Sensitivity Calibration in Parallel MRI without Ground Truth

Weijie Gan, Yuyang Hu, Cihat Eldeniz, Jiaming Liu, Yasheng Chen, Hongyu An, Ulugbek S. Kamilov

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

3 Scopus citations

Abstract

Parallel magnetic resonance imaging (MRI) is a widely-used technique that accelerates data collection by making use of the spatial encoding provided by multiple receiver coils. A key issue in parallel MRI is the estimation of coil sensitivity maps (CSMs) that are used for reconstructing a single high-quality image. This paper addresses this issue by developing SS-JIRCS, a new self-supervised model-based deep-learning (DL) method for image reconstruction that is equipped with automated CSM calibration. Our deep network consists of three types of modules: data-consistency, regularization, and CSM calibration. Unlike traditional supervised DL methods, these modules are directly trained on undersampled and noisy k-space data rather than on fully sampled high-quality ground truth. We present empirical results on simulated data that show the potential of the proposed method for achieving better performance than several baseline methods.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4031-4039
Number of pages9
ISBN (Electronic)9781665401913
DOIs
StatePublished - 2021
Event18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021 - Virtual, Online, Canada
Duration: Oct 11 2021Oct 17 2021

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
Volume2021-October
ISSN (Print)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2021
Country/TerritoryCanada
CityVirtual, Online
Period10/11/2110/17/21

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