Diff-DTI: Fast Diffusion Tensor Imaging Using A Feature-Enhanced Joint Diffusion Model

Magnetic resonance diffusion tensor imaging (DTI) is a unique non-invasive technique for measuring in vivo water molecule diffusion, reflecting tissue microstructure. However, acquiring high-quality DTI typically requires numerous diffusion-weighted images (DWIs) in multiple directions, resulting in long scan times that restrict its use in clinical and research settings. To address this limitation, we propose Diff-DTI, a fast DTI processing framework based on a feature-enhanced joint diffusion model, to reduce the number of DWIs needed for tensor fitting. Diff-DTI models the joint probability distribution of DWIs and DTI maps, supporting guided generation during inference. The incorporated feature enhancement fusion module further enhances image precision and details generated by the diffusion model. Experiments were performed on three public DWI datasets. Results demonstrate that Diff-DTI achieves up to 10-fold acceleration (using 6 DWIs) while maintaining relatively low normalized mean square error (NMSE) for DTI maps (2. 89% for FA, 0. 89% for MD, 0. 95% for AD, and 0. 98% for RD). Even using Diff-DTI with only 3 DWIs, the NMSEs of the generated DTI maps showed a gradual decrease, with 3. 51% for FA, 0. 89% for MD, 1. 13% for AD, and 1. 10% for RD. We conclude that Diff-DTI can significantly reduce the number of acquired DWIs and the scan time, without compromising image quality too much.

Authors

• Lang Zhang Li Auto inc.
• Jinling He
• Wang Li
• Dong Liang
• Yanjie Zhu

Keywords

• Diffusion tensor imaging
• Diffusion models
• Imaging
• Tensors
• Training
• Fitting
• Transformers
• Image reconstruction
• Signal to noise ratio
• Noise reduction
• Diffusion Model
• Fast Imaging
• Mean Square Error
• Image Quality
• Scan Time
• Feature Enhancement
• Tissue Microstructure
• Normalized Mean Square Error
• Normalized Mean Square
• Convolutional Neural Network
• White Matter
• Traumatic Brain Injury
• Diffusion Process
• Multilayer Perceptron
• White Matter Tracts
• Peak Signal-to-noise Ratio
• Multi-scale Features
• Quantitative Metrics
• Stochastic Differential Equations
• Human Connectome Project Dataset
• Human Connectome Project
• B0 Image
• Forward Process
• Superior Longitudinal Fasciculus
• Uniform Direction
• Major White Matter Tracts
• Key Vector
• Transformer Encoder
• Multivariate Datasets
• Diffusion-weighed images
• deep learning
• joint diffusion model
• feature enhancement and fusion
• Humans
• Brain
• Algorithms
• Image Interpretation, Computer-Assisted
• Image Processing, Computer-Assisted