pISTA-SENSE-ResNet for Parallel MRI Reconstruction
This work addresses the problem of slow MRI acquisition times for clinical diagnosis, representing an incremental improvement in deep learning-based reconstruction methods.
The paper tackles the challenge of achieving high-quality and fast reconstruction for parallel MRI by proposing a deep learning network based on sparse iterative reconstruction and residual structures, showing less error and greater stability across acceleration factors compared to existing methods.
Magnetic resonance imaging has been widely applied in clinical diagnosis, however, is limited by its long data acquisition time. Although imaging can be accelerated by sparse sampling and parallel imaging, achieving promising reconstruction images with a fast reconstruction speed remains a challenge. Recently, deep learning approaches have attracted a lot of attention for its encouraging reconstruction results but without a proper interpretability. In this letter, to enable high-quality image reconstruction for the parallel magnetic resonance imaging, we design the network structure from the perspective of sparse iterative reconstruction and enhance it with the residual structure. The experimental results of a public knee dataset show that compared with the optimization-based method and the latest deep learning parallel imaging methods, the proposed network has less error in reconstruction and is more stable under different acceleration factors.