Generalizable 7T T1-map Synthesis from 1.5T and 3T T1 MRI with an Efficient Transformer Model
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Purpose: Ultra-high-field 7T MRI offers improved resolution and contrast over
standard clinical field strengths (1.5T, 3T). However, 7T scanners are costly,
scarce, and introduce additional challenges such as susceptibility artifacts.
We propose an efficient transformer-based model (7T-Restormer) to synthesize
7T-quality T1-maps from routine 1.5T or 3T T1-weighted (T1W) images. Methods:
Our model was validated on 35 1.5T and 108 3T T1w MRI paired with corresponding
7T T1 maps of patients with confirmed MS. A total of 141 patient cases (32,128
slices) were randomly divided into 105 (25; 80) training cases (19,204 slices),
19 (5; 14) validation cases (3,476 slices), and 17 (5; 14) test cases (3,145
slices) where (X; Y) denotes the patients with 1.5T and 3T T1W scans,
respectively. The synthetic 7T T1 maps were compared against the ResViT and
ResShift models. Results: The 7T-Restormer model achieved a PSNR of 26.0 +/-
4.6 dB, SSIM of 0.861 +/- 0.072, and NMSE of 0.019 +/- 0.011 for 1.5T inputs,
and 25.9 +/- 4.9 dB, and 0.866 +/- 0.077 for 3T inputs, respectively. Using
10.5 M parameters, our model reduced NMSE by 64 % relative to 56.7M parameter
ResShift (0.019 vs 0.052, p = <.001 and by 41 % relative to 70.4M parameter
ResViT (0.019 vs 0.032, p = <.001) at 1.5T, with similar advantages at 3T
(0.021 vs 0.060 and 0.033; p < .001). Training with a mixed 1.5 T + 3 T corpus
was superior to single-field strategies. Restricting the model to 1.5T
increased the 1.5T NMSE from 0.019 to 0.021 (p = 1.1E-3) while training solely
on 3T resulted in lower performance on input 1.5T T1W MRI. Conclusion: We
propose a novel method for predicting quantitative 7T MP2RAGE maps from 1.5T
and 3T T1W scans with higher quality than existing state-of-the-art methods.
Our approach makes the benefits of 7T MRI more accessible to standard clinical
workflows.
