Abstract
Quantitative dynamic MRI acquisitions have the potential to diagnose diffuse diseases in conjunction with functional abnormalities. However, their resolutions are limited due to the long acquisition time. Such datasets are multi-dimensional, exhibiting interactions between ≥ 4 dimensions, which cannot be easily identified using sparsity or low-rank matrix methods. Hence, low-rank tensors are a natural fit to model such data. But in the presence of multitude of different tissue types in the field-of-view, it is difficult to find an appropriate value of tensor rank, which avoids under-or over-regularization. In this work, we propose a locally low-rank tensor regularization approach to enable high-resolution quantitative dynamic MRI. We show this approach successfully enables dynamic Ti mapping at high spatio-temporal resolutions.
Original language | English (US) |
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Title of host publication | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781538612514 |
DOIs | |
State | Published - Mar 9 2018 |
Event | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao Duration: Dec 10 2017 → Dec 13 2017 |
Publication series
Name | 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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Volume | 2017-December |
Conference
Conference | 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 |
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City | Curacao |
Period | 12/10/17 → 12/13/17 |
Bibliographical note
Funding Information:This work was partially supported by NIH R00HL111410, NIH P41EB015894, NSF CCF-1651825 and NSF IIS-1704074
Publisher Copyright:
© 2017 IEEE.