Kawin Setsompop

TL;DR: It is concluded that making appropriate corrections for physiological noise is likely more important for SMS-EPI than for regular EPI acquisitions, with cardiac pulsatility contributing more to noise in regular E PI data but low-frequency heart rate variability contribute more to SMS- EPI.

TL;DR: A new image reconstruction method based on low-rank and subspace modeling for improved SMS-MRF, which has a potential to allow for a 3× speedup with minimal reconstruction error, resulting in less than 5 sec imaging time per slice.

TL;DR: The proposed algorithm (SB-L21) offers 2x, 32x, and 66x faster reconstruction with lower RMSE averaged across all contrasts and slices compared to FCSA-MT, M-FOCUSS, and SparseMRI, respectively.

TL;DR: This work proposes Nonlinear Dipole Inversion for high-quality Quantitative Susceptibility Mapping (QSM) without regularization tuning, while matching the image quality of state-of-the-art reconstruction techniques, and synergistically combines this physics-model with a Variational Network to leverage the power of deep learning in the VaNDI algorithm.

TL;DR: 3D-EPTI as discussed by the authors is a novel approach, termed 3D Echo Planar Time-resolved Imaging (3DEPTI), which significantly increases the acceleration capacity of MRI sampling, and provides high acquisition efficiency for multi-parametric MRI.