Affiliation:
1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
2. University of Chinese Academy of Sciences, Beijing 101408, China
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.
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province
Reference57 articles.
1. Generalized autocalibrating partially parallel acquisitions (GRAPPA);Griswold;Magn. Reson. Med. Off. J. Int. Soc. Magn. Reson. Med.,2002
2. SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space;Lustig;Magn. Reson. Med.,2010
3. Compressed-sensing MRI with random encoding;Haldar;IEEE Trans. Med Imaging,2010
4. Chen, C., and Huang, J. (2012, January 3–6). Compressive sensing MRI with wavelet tree sparsity. Proceedings of the Twenty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2012), Lake Tahoe, NV, USA.
5. Sparse representation-based MRI super-resolution reconstruction;Wang;Measurement,2014