Deep Magnetic Resonance Fingerprinting Based on local and global vision transformer

Magnetic resonance fingerprinting (MRF) can achieve simultaneous imaging of multiple tissue parameters. However, the size of the tissue fingerprint dictionary used in MRF grows exponentially as the number of tissue parameters increases, which may result in prohibitively large dictionaries that require extensive computational resources. Existing CNN-based methods obtain parameter reconstruction patch-wisely, using only local information and resulting in limited reconstruction speed. In this paper, we propose a novel end-to-end local and global vision transformer(LG-VIT) for MRF parameter reconstruction. The proposed method enables significantly fast and accurate end-to-end parameter reconstruction while avoiding the high computational cost of high-dimensional data.