Projects per year
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 language | English |
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Title of host publication | ISBI 2022 - Proceedings |
Subtitle of host publication | 2022 IEEE International Symposium on Biomedical Imaging |
Place of Publication | U. S. A. |
Publisher | IEEE |
ISBN (Electronic) | 9781665429238 |
DOIs | |
Publication status | E-pub ahead of print - 26 Apr 2022 |
Event | 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 - Kolkata, India Duration: 28 Mar 2022 → 31 Mar 2022 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2022-March |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 |
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Country/Territory | India |
City | Kolkata |
Period | 28/03/22 → 31/03/22 |
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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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0
Campbell, N. (PI), Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Cosker, D. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Petrini, K. (CoI), Proulx, M. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/11/20 → 31/10/25
Project: Research council
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA)
Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Fincham Haines, T. (CoI), Hall, P. (CoI), Kim, K. I. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Richardt, C. (CoI), Salo, A. (CoI), Seminati, E. (CoI), Tabor, A. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/09/15 → 28/02/21
Project: Research council