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Joint Image Reconstruction and Super-Resolution for Accelerated Magnetic Resonance Imaging.
- Source :
-
Bioengineering (Basel) . Sep2023, Vol. 10 Issue 9, p1107. 18p. - Publication Year :
- 2023
-
Abstract
- Magnetic resonance (MR) image reconstruction and super-resolution are two prominent techniques to restore high-quality images from undersampled or low-resolution k-space data to accelerate MR imaging. Combining undersampled and low-resolution acquisition can further improve the acceleration factor. Existing methods often treat the techniques of image reconstruction and super-resolution separately or combine them sequentially for image recovery, which can result in error propagation and suboptimal results. In this work, we propose a novel framework for joint image reconstruction and super-resolution, aiming to efficiently image recovery and enable fast imaging. Specifically, we designed a framework with a reconstruction module and a super-resolution module to formulate multi-task learning. The reconstruction module utilizes a model-based optimization approach, ensuring data fidelity with the acquired k-space data. Moreover, a deep spatial feature transform is employed to enhance the information transition between the two modules, facilitating better integration of image reconstruction and super-resolution. Experimental evaluations on two datasets demonstrate that our proposed method can provide superior performance both quantitatively and qualitatively. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23065354
- Volume :
- 10
- Issue :
- 9
- Database :
- Academic Search Index
- Journal :
- Bioengineering (Basel)
- Publication Type :
- Academic Journal
- Accession number :
- 172415498
- Full Text :
- https://doi.org/10.3390/bioengineering10091107