Academic Journal
A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers
| العنوان: | A Plug-and-Play Approach To Multiparametric Quantitative MRI:Image Reconstruction Using Pre-Trained Deep Denoisers |
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| المؤلفون: | Fatania, Ketan, Pirkl, Carolin M., Menzel, Marion I., Hall, Peter, Golbabaee, Mohammad |
| المصدر: | Fatania , K , Pirkl , C M , Menzel , M I , Hall , P & Golbabaee , M 2022 , A Plug-and-Play Approach To Multiparametric Quantitative MRI : Image Reconstruction Using Pre-Trained Deep Denoisers . in ISBI 2022 - Proceedings : 2022 IEEE International Symposium on Biomedical Imaging . , 9761603 , Proceedings - International Symposium on Biomedical Imaging , vol. 2022-March , IEEE , U. S. A. , 19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 , Kolkata , India , 28/03/22 . https://doi.org/10.1109/ISBI52829.2022.9761603 |
| بيانات النشر: | IEEE |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Compressed Sensing, Deep Learning, Inverse Problems, Iterative Image Reconstruction, Magnetic Resonance Fingerprinting, Plug-and-Play, Quantitative MRI, /dk/atira/pure/subjectarea/asjc/2200/2204, name=Biomedical Engineering, /dk/atira/pure/subjectarea/asjc/2700/2741, name=Radiology Nuclear Medicine and imaging |
| الوصف: | 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 |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| ردمك: | 978-1-66542-923-8 1-66542-923-2 |
| Relation: | urn:ISBN:9781665429238 |
| DOI: | 10.1109/ISBI52829.2022.9761603 |
| الاتاحة: | https://researchportal.bath.ac.uk/en/publications/0888941d-c7be-423f-83a8-2be789567c97 https://doi.org/10.1109/ISBI52829.2022.9761603 https://purehost.bath.ac.uk/ws/files/239059724/Plug_and_Play_QMRI_MRF_Reconstruction_2022.pdf http://www.scopus.com/inward/record.url?scp=85129577594&partnerID=8YFLogxK https://arxiv.org/abs/2202.05269 |
| Rights: | info:eu-repo/semantics/openAccess |
| رقم الانضمام: | edsbas.712C5F15 |
| قاعدة البيانات: | BASE |
| ردمك: | 9781665429238 1665429232 |
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| DOI: | 10.1109/ISBI52829.2022.9761603 |