MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction

MRF-Mixer: A Simulation-Based Deep Learning Framework for Accelerated and Accurate Magnetic Resonance Fingerprinting Reconstruction

MRF-Mixer is a novel deep learning method for magnetic resonance fingerprinting (MRF) reconstruction, offering 200× faster processing (0.35 s on CPU and 0.3 ms on GPU) and 40% higher accuracy (lower MAE) than dictionary matching. It develops a simulation-driven approach using complex-valued multi-la...

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Bibliographic Details
Main Authors: Tianyi Ding, Yang Gao, Zhuang Xiong, Feng Liu, Martijn A. Cloos, Hongfu Sun
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Information
Subjects:
deep learning
MR fingerprinting
self-supervised
MRF-Mixer
Online Access:https://www.mdpi.com/2078-2489/16/3/218
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