Deep learning-assisted preclinical MR fingerprinting for sub-millimeter T₁ and T₂ mapping of entire macaque brain

Preclinical MR fingerprinting (MRF) suffers from long acquisition time for organ-level coverage due to demanding image resolution and limited undersampling capacity. This study aims to develop a deep learning-assisted fast MRF framework for sub-millimeter T₁ and T₂ mapping of entire macaque brain on a preclinical 9.4 T MR system.

Three dimensional MRF images were reconstructed by singular value decomposition (SVD) compressed reconstruction. T₁ and T₂ mapping for each axial slice exploited a self-attention assisted residual U-Net to suppress aliasing-induced quantification errors, and the transmit-field (B₁ ⁺ ) measurements for robustness against B₁ ⁺ inhomogeneity. Supervised network training used MRF images simulated via virtual parametric maps and a desired undersampling scheme. This strategy bypassed the difficulties of acquiring fully sampled preclinical MRF data to guide network training. The proposed fast MRF framework was tested on experimental data acquired from ex vivo and in vivo macaque brains.

The trained network showed reasonable adaptability to experimental MRF images, enabling robust delineation of various T₁ and T₂ distributions in the brain tissues. Further, the proposed MRF framework outperformed several existing fast MRF methods in handling the aliasing artifacts and capturing detailed cerebral structures in the mapping results. Parametric mapping of entire macaque brain at nominal resolution of 0.35 $\times$ 0.35 $\times$ 1 mm³ can be realized via a 20-min 3D MRF scan, which was sixfold faster than the baseline protocol.

Introducing deep learning to MRF framework paves the way for efficient organ-level high-resolution quantitative MRI in preclinical applications.