Accepted manuscript
Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR
ArXiv.org
02/01/2021
DOI: 10.48550/arXiv.2102.01172
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 [6]. 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.
Details
- Title: Subtitle
- Reconstruction and Segmentation of Parallel MR Data using Image Domain DEEP-SLR
- Creators
- Aniket PramanikMathews Jacob
- Resource Type
- Accepted manuscript
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arXiv.2102.01172
- ISSN
- 2331-8422
- Language
- English
- Date posted
- 02/01/2021
- Academic Unit
- Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Radiology; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984071706102771
Metrics
9 Record Views