Research Area:  Machine Learning

Abstract:

Spatio-temporal MRI methods offer rapid whole-brain multi-parametric mapping, yet they are often hindered by prolonged reconstruction times or prohibitively burdensome hardware requirements. The aim of this project is to reduce reconstruction time using deep learning.This study focuses on accelerating the reconstruction of volumetric multi-axis spiral projection MRF, aiming for whole-brain T1 and T2 mapping, while ensuring a streamlined approach compatible with clinical requirements. To optimize reconstruction time, the traditional method is first revamped with a memory-efficient GPU implementation. Deep Learning Initialized Compressed Sensing (Deli-CS) is then introduced, which initiates iterative reconstruction with a DL-generated seed point, reducing the number of iterations needed for convergence.

Keywords:  

Author(s) Name:  S. Sophie Schauman, Siddharth S. Iyer, Christopher M. Sandino, Mahmut Yurt, Xiaozhi Cao, Congyu Liao, Natthanan Ruengchaijatuporn, Itthi Chatnuntawech, Elizabeth Tong & Kawin Setsompop

Publisher name:  Springer

DOI:  10.1007/s10334-024-01222-2

Volume Information:  Volume 38, pages 221–237, (2025)

Paper Link:   https://link.springer.com/article/10.1007/s10334-024-01222-2