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7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived fromdiffusion-weighted imaging (DWI) demonstrate improved image quality and spatialresolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging(MRI) currently suffers from limited clinical unavailability, higher cost, andincreased susceptibility to artifacts. To address these issues, we propose ahybrid CNN-transformer model to synthesize high-resolution 7T ADC maps frommulti-modal 3T MRI. The Vision CNN-Transformer (VCT), composed of both VisionTransformer (ViT) blocks and convolutional layers, is proposed to producehigh-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w)MRI. ViT blocks enabled global image context while convolutional layersefficiently captured fine detail. The VCT model was validated on the publiclyavailable Human Connectome Project Young Adult dataset, comprising 3T T1w, 3TDWI, and 7T DWI brain scans. The Diffusion Imaging in the Python library wasused to compute ADC maps from the DWI scans. A total of 171 patient cases wererandomly divided: 130 training cases, 20 validation cases, and 21 test cases.The synthetic ADC maps were evaluated by comparing their similarity to theground truth volumes with the following metrics: peak signal-to-noise ratio(PSNR), structural similarity index measure (SSIM), and mean squared error(MSE). The results are as follows: PSNR: 27.0+-0.9 dB, SSIM: 0.945+-0.010, andMSE: 2.0+-0.4E-3. Our predicted images demonstrate better spatial resolutionand contrast compared to 3T MRI and prediction results made by ResViT andpix2pix. These high-quality synthetic 7T MR images could be beneficial fordisease diagnosis and intervention, especially when 7T MRI scanners areunavailable.

High-resolution 3T to 7T MRI Synthesis with a Hybrid CNN-Transformer Model