A Deep Error Correction Network for Compressed Sensing MRI
This work addresses the need for higher diagnostic quality in MRI reconstructions for medical imaging applications, but it is incremental as it builds upon existing CS-MRI algorithms.
The paper tackles the problem of structural errors in compressed sensing MRI reconstruction by proposing a deep error correction network (DECN) that uses existing algorithms as a template and learns a CNN to adjust for reconstruction errors, resulting in considerable improvement over existing inversion algorithms.
Compressed sensing for magnetic resonance imaging (CS-MRI) exploits image sparsity properties to reconstruct MRI from very few Fourier k-space measurements. The goal is to minimize any structural errors in the reconstruction that could have a negative impact on its diagnostic quality. To this end, we propose a deep error correction network (DECN) for CS-MRI. The DECN model consists of three parts, which we refer to as modules: a guide, or template, module, an error correction module, and a data fidelity module. Existing CS-MRI algorithms can serve as the template module for guiding the reconstruction. Using this template as a guide, the error correction module learns a convolutional neural network (CNN) to map the k-space data in a way that adjusts for the reconstruction error of the template image. Our experimental results show the proposed DECN CS-MRI reconstruction framework can considerably improve upon existing inversion algorithms by supplementing with an error-correcting CNN.