Magnetic Resonance Imaging (MRI) is a non-invasive imaging modality routinely used in the clinic to obtain tomographic images of the human body. While MRI can provide excellent tissue contrast, its speed is slow compared to X-ray CT. For many MRI applications, e.g., dynamic contrast-enhanced (DCE) MRI and quantitative MRI (e.g., T1, T2 mapping), imaging speed is necessary to get accurate measurements. To accelerate the imaging speed for MRI, people have been using phased-array coils and compressed sensing to acquire fewer data points and rely on optimization algorithms to recover images from the undersampled data. This approach is particularly successful in quantitative MRI, where a fixed physical model can be exploited in the image reconstruction process. When combined with advanced algorithms, the model-based reconstruction strategy can achieve a high degree of undersampling while producing comparable image quality. The accelerated MRI technique has been the focus of MRI research, and there have been numerous clinical uses.

In this proposed collection, researchers are invited to submit their work on fast MRI. These can be new data acquisition techniques, new image reconstruction algorithms, or the application of such techniques. Materials related to, but not limited to, areas such as neural imaging, which investigates the structure and functioning of the brain; quantitative MRI, which uses physical parameters to characterize disease; and technical studies and developments that explore new ways to accelerate MRI are expected.