Accelerating 3D MULTIPLEX MRI Reconstruction with Deep Learning
This addresses a domain-specific problem for medical imaging researchers and clinicians by enabling faster and more efficient 3D MRI reconstruction, though it appears incremental as it applies existing deep learning techniques to a new data type.
The authors tackled the problem of long scan times and large data volumes in 3D high-resolution MULTIPLEX MRI reconstruction by proposing a deep learning framework for undersampled data, which showed good performance in image quality and reconstruction time.
Multi-contrast MRI images provide complementary contrast information about the characteristics of anatomical structures and are commonly used in clinical practice. Recently, a multi-flip-angle (FA) and multi-echo GRE method (MULTIPLEX MRI) has been developed to simultaneously acquire multiple parametric images with just one single scan. However, it poses two challenges for MULTIPLEX to be used in the 3D high-resolution setting: a relatively long scan time and the huge amount of 3D multi-contrast data for reconstruction. Currently, no DL based method has been proposed for 3D MULTIPLEX data reconstruction. We propose a deep learning framework for undersampled 3D MRI data reconstruction and apply it to MULTIPLEX MRI. The proposed deep learning method shows good performance in image quality and reconstruction time.