Enhanced Portable Ultra Low-Field Diffusion Tensor Imaging with Bayesian Artifact Correction and Deep Learning-Based Super-Resolution

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• A novel nine-direction, single-shell ULF DTI sequence was developed for portable MRI.method 95%
• A Bayesian bias field correction algorithm with angular dependence was created to correct artifacts in ULF DTI scans.method 95%
• A convolutional neural network-based superresolution algorithm (DiffSR) was developed that generalizes across DTI datasets without retraining.method 95%
• The developed algorithms can recover microstructural and volumetric white matter information at ULF, as demonstrated through synthetic downsampling and real ULF/high-field DTI comparisons.result 90%
• DiffSR can be directly applied to white matter-based Alzheimer's disease classification in synthetically degraded scans.result 90%
• Applying DiffSR to synthetically degraded scans improves agreement between DTI metrics compared to un-degraded scans in Alzheimer's disease classification.result 85%
• The reliability of portable MRI is still under significant scrutiny for broader clinical diagnoses.limitation 80%
• The system may still struggle with severe motion artifacts in real-world use.limitation 75%

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