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Generative AI for rapid diffusion MRI with improved image quality, reliability, and generalizability.

Amir Sadikov1,2, Xinlei Pan3, Hannah Choi1

  • 1Radiology and Biomedical Imaging, University of California, San Francisco, CA, United States.

Imaging Neuroscience (Cambridge, Mass.)
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Summary
This summary is machine-generated.

Generative AI, using a Swin UNEt Transformers (SWIN) model, enables rapid diffusion MRI (dMRI) with high fidelity and reproducibility. This advanced AI approach significantly improves denoising accuracy and reliability for various clinical applications.

Keywords:
MR-diffusion tensor imagingMR-diffusion weighted imagingbrain/brain stemneural networkssupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Diffusion MRI (dMRI) is crucial for neuroimaging but often requires long scan times and is susceptible to noise.
  • Current denoising methods face limitations in accuracy, reproducibility, and generalizability across different scanners and patient populations.
  • Generative AI offers potential for improving dMRI quality and efficiency.

Purpose of the Study:

  • To develop and validate a generative AI model for rapid, high-fidelity diffusion MRI.
  • To enhance the accuracy, reproducibility, and generalizability of dMRI denoising and super-resolution.
  • To demonstrate the model's robustness and fine-tuning capabilities on diverse clinical datasets.

Main Methods:

  • A Swin UNEt Transformers (SWIN) model was trained on Human Connectome Project (HCP) data for generalized dMRI denoising, conditioned on T1 scans.
  • Qualitative super-resolution was demonstrated using artificially downsampled HCP data.
  • The SWIN model was fine-tuned on out-of-domain datasets including pediatric neurodevelopmental disorders, traumatic brain injury, and intracerebral hemorrhage cohorts.

Main Results:

  • The SWIN model achieved state-of-the-art performance in accuracy and test-retest reliability for rapid diffusion tensor imaging (DTI) with only 90 seconds of scan time.
  • Significant improvements were observed in the reliability of intracellular volume fraction and free water fraction measurements.
  • The model demonstrated robustness across different scanner models, imaging protocols, sites, and patient populations, outperforming self-supervised methods.

Conclusions:

  • Generative AI, specifically the SWIN model, enables rapid dMRI with unprecedented accuracy and reliability.
  • The approach is highly generalizable and can be effectively fine-tuned for diverse clinical applications, including challenging patient cohorts.
  • SWIN denoising enhances biophysical modeling fidelity by removing noise and improving measurements at microscopic spatial scales.