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Learning Data Consistency and its Application to Dynamic MR Imaging
- Source :
- IEEE Transactions on Medical Imaging. 40:3140-3153
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- Magnetic resonance (MR) image reconstruction from undersampled k-space data can be formulated as a minimization problem involving data consistency and image prior. Existing deep learning (DL)-based methods for MR reconstruction employ deep networks to exploit the prior information and integrate the prior knowledge into the reconstruction under the explicit constraint of data consistency, without considering the real distribution of the noise. In this work, we propose a new DL-based approach termed Learned DC that implicitly learns the data consistency with deep networks, corresponding to the actual probability distribution of system noise. The data consistency term and the prior knowledge are both embedded in the weights of the networks, which provides an utterly implicit manner of learning reconstruction model. We evaluated the proposed approach with highly undersampled dynamic data, including the dynamic cardiac cine data with up to 24-fold acceleration and dynamic rectum data with the acceleration factor equal to the number of phases. Experimental results demonstrate the superior performance of the Learned DC both quantitatively and qualitatively than the state-of-the-art.
- Subjects :
- Data consistency
Radiological and Ultrasound Technology
business.industry
Computer science
Dynamic data
Deep learning
Supervised learning
Heart
Iterative reconstruction
Magnetic Resonance Imaging
Computer Science Applications
Data modeling
Consistency (statistics)
Image Processing, Computer-Assisted
Probability distribution
Artificial intelligence
Electrical and Electronic Engineering
business
Algorithm
Algorithms
Software
Probability
Subjects
Details
- ISSN :
- 1558254X and 02780062
- Volume :
- 40
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Medical Imaging
- Accession number :
- edsair.doi.dedup.....fd4b24cbb8bc6cd63a8683f13d980af5