Abstract
The main focus of this work is a novel framework for the joint reconstruction and segmentation of parallel MRI (PMRI) brain data. We introduce an image domain deep network for calibrationless recovery of undersampled PMRI data. The proposed approach is the deep-learning (DL) based generalization of local low-rank based approaches for uncalibrated PMRI recovery including CLEAR [1]. Since the image domain approach exploits additional annihilation relations compared to k-space based approaches, we expect it to offer improved performance. To minimize segmentation errors resulting from undersampling artifacts, we combined the proposed scheme with a segmentation network and trained it in an end-to-end fashion. In addition to reducing segmentation errors, this approach also offers improved reconstruction performance by reducing overfitting; the reconstructed images exhibit reduced blurring and sharper edges than independently trained reconstruction network.
Original language | English |
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Title of host publication | 2021 IEEE 18th International Symposium on Biomedical Imaging, ISBI 2021 |
Publisher | IEEE Computer Society |
Pages | 1095-1098 |
Number of pages | 4 |
ISBN (Electronic) | 9781665412469 |
DOIs | |
State | Published - Apr 13 2021 |
Externally published | Yes |
Event | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 - Nice, France Duration: Apr 13 2021 → Apr 16 2021 |
Publication series
Name | Proceedings - International Symposium on Biomedical Imaging |
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Volume | 2021-April |
ISSN (Print) | 1945-7928 |
ISSN (Electronic) | 1945-8452 |
Conference
Conference | 18th IEEE International Symposium on Biomedical Imaging, ISBI 2021 |
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Country/Territory | France |
City | Nice |
Period | 04/13/21 → 04/16/21 |
Funding
This work is supported by NIH R01EB019961-01A1 and NIH R01AG067078-01A1.
Keywords
- CNN
- Calibrationless
- Parallel MRI