LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small dataset
This addresses the problem of high-quality MRI reconstruction from small datasets for medical imaging applications, representing an incremental improvement over existing deep learning methods.
The paper tackles dynamic MRI reconstruction with limited data by proposing LANTERN, which integrates compressed sensing and deep learning to exploit spatiotemporal redundancy, achieving better reconstruction accuracy than state-of-the-art methods across various acceleration factors and undersampling patterns as measured by PSNR, SSIM, and HFEN metrics.
This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (i) The spatial and temporal domains are sparsely constrained by using adaptively trained CNN. (ii) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods. (iii) Compared to existing deep learning reconstruction methods, our reconstruction accuracy is better when the amount of data is limited. Our model is able to fully exploit the redundancy in spatial and temporal of dynamic MR images. We performed quantitative and qualitative analysis of cardiac datasets at different acceleration factors (2x-11x) and different undersampling modes. In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.