Progres of deep learning based quantitative MRI reconstruction methods

Abstract

： Magnetic resonance imaging (MRI) is a commonly-used non-invasive medical imaging method. Due to richable soft tissue-related contrast features of human body， medical imaging diagnosis is beneficial from more internal structurescontextual image in clinic， including its organs， bones， muscles， and blood vessels. However， two sorts of bottlenecks of MRI like slow scanning speed and labor-intensive scanning operation are required to be resolved beyond the constraints of hardware and existing techniques. Current MRI has been facilitating in terms of the emerging deep learning based medical imaging technique. To optimize its acquisition time， parallel imaging and compressed sensing combined with the use of multi-array coils， conventional MRI mainly depends on the hardware improvements and new compactable sequence design.First， literature review of deep learning-based MR reconstruction methods are analyzed， including such deep learning issues originated from 1) integration of parallel imaging and compressed sensing of MRI and 2) acceleration of multi-