J-MoDL: Joint Model-Based Deep Learning for Optimized Sampling and Reconstruction
This work addresses scan time reduction in MRI for medical imaging applications, representing an incremental improvement by integrating sampling optimization into existing model-based deep learning frameworks.
The paper tackles the problem of improving MRI image quality from undersampled data by jointly optimizing the sampling pattern and reconstruction network parameters, resulting in significant performance gains for deep learning reconstruction algorithms.
Modern MRI schemes, which rely on compressed sensing or deep learning algorithms to recover MRI data from undersampled multichannel Fourier measurements, are widely used to reduce scan time. The image quality of these approaches is heavily dependent on the sampling pattern. We introduce a continuous strategy to jointly optimize the sampling pattern and network parameters. We use a multichannel forward model, consisting of a non-uniform Fourier transform with continuously defined sampling locations, to realize the data consistency block within a model-based deep learning image reconstruction scheme. This approach facilitates the joint and continuous optimization of the sampling pattern and the CNN parameters to improve image quality. We observe that the joint optimization of the sampling patterns and the reconstruction module significantly improves the performance of most deep learning reconstruction algorithms. The source code of the proposed joint learning framework is available at https://github.com/hkaggarwal/J-MoDL.