Multi Complex-valued Spatio-temporal Fusion Networks for Robust MRF Reconstruction

Motivation: Motivated by the prevalent use of basic deep learning architectures in MRF image reconstruction, and their heavy reliance on conventional dictionary matching methods for ground truth or paired in vivo acquisitions, we sought to innovate. Goal(s): Our specific intent was to determine whether novel architectures could surpass traditional ones in MRF reconstruction. Approach: To this end, we introduced the MRF-Mixer, blending complex-valued MLP with U-Net, and the more advanced MRF-TransMixer, integrating complex-valued MLP, Transformer, and U-Net. Results: Leveraging our purely simulated training dataset, we methodically assessed their performance, endeavoring to highlight advancements with potential to transform MRF reconstruction in practice. Impact: The MRF-Mixer and MRF-TransMixer offer enhanced MRF image reconstruction, potentially boosting diagnostic accuracy for clinicians. This advancement could lead to safer imaging for patients and motivate researchers to further explore SOTA network architecture applications in MRF.