A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers

Ketan Fatania, Carolin M. Pirkl, Marion I. Menzel, Peter Hall, Mohammad Golbabaee

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

3 Citations (SciVal)
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Abstract

Current spatiotemporal deep learning approaches to Magnetic Resonance Fingerprinting (MRF) build artefact-removal models customised to a particular k-space subsampling pattern which is used for fast (compressed) acquisition. This may not be useful when the acquisition process is unknown during training of the deep learning model and/or changes during testing time. This paper proposes an iterative deep learning plug-and-play reconstruction approach to MRF which is adaptive to the forward acquisition process. Spatiotemporal image priors are learned by an image denoiser i.e. a Convolutional Neural Network (CNN), trained to remove generic white gaussian noise (not a particular subsampling artefact) from data. This CNN denoiser is then used as a data-driven shrinkage operator within the iterative reconstruction algorithm. This algorithm with the same denoiser model is then tested on two simulated acquisition processes with distinct subsampling patterns. The results show consistent dealiasing performance against both acquisition schemes and accurate mapping of tissues' quantitative bio-properties. Software available: https://github.com/ketanfatania/QMRI-PnP-Recon-POC

Original languageEnglish
Title of host publicationISBI 2022 - Proceedings
Subtitle of host publication2022 IEEE International Symposium on Biomedical Imaging
Place of PublicationU. S. A.
PublisherIEEE
ISBN (Electronic)9781665429238
DOIs
Publication statusE-pub ahead of print - 26 Apr 2022
Event19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India
Duration: 28 Mar 202231 Mar 2022

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2022-March
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference19th IEEE International Symposium on Biomedical Imaging, ISBI 2022
Country/TerritoryIndia
CityKolkata
Period28/03/2231/03/22

Bibliographical note

Funding Information:
Carolin M. Pirkl and Marion I. Menzel receive funding from the European Union s Horizon 2020 research and innovation programme, grant agreement No. 952172.


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Funding

Carolin M. Pirkl and Marion I. Menzel receive funding from the European Union s Horizon 2020 research and innovation programme, grant agreement No. 952172. Carolin M. Pirkl and Marion I. Menzel receive funding from the European Union’s Horizon 2020 research and innovation programme, grant agreement No. 952172.

Keywords

  • Compressed Sensing
  • Deep Learning
  • Inverse Problems
  • Iterative Image Reconstruction
  • Magnetic Resonance Fingerprinting
  • Plug-and-Play
  • Quantitative MRI

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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