eRAKI: Fast Robust Artificial neural networks for K-space Interpolation (RAKI) with Coil Combination and Joint Reconstruction

Autor:	Yu, Heng, Dong, Zijing, Arefeen, Yamin, Liao, Congyu, Setsompop, Kawin, Bilgic, Berkin
Rok vydání:	2021
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	RAKI can perform database-free MRI reconstruction by training models using only auto-calibration signal (ACS) from each specific scan. As it trains a separate model for each individual coil, learning and inference with RAKI can be computationally prohibitive, particularly for large 3D datasets. In this abstract, we accelerate RAKI more than 200 times by directly learning a coil-combined target and further improve the reconstruction performance using joint reconstruction across multiple echoes together with an elliptical-CAIPI sampling approach. We further deploy these improvements in quantitative imaging and rapidly obtain T2 and T2* parameter maps from a fast EPTI scan. Comment: accepted by ISMRM2021 as an oral abstract
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2108.04218 Zobrazit plný text záznamu View this record from Arxiv