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

arXiv: https://arxiv.org/abs/2405.15830 · HTML: https://mlsanity.com/paper/2405.15830/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 21 of its 74 references that are in the map.
- First posted on arXiv 24 May 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 21 of 21)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022)
- [Improved Techniques for Training Score-Based Generative Models](https://mlsanity.com/paper/2006.09011/) (2020)
- [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021)
- [Score-based diffusion models for accelerated MRI](https://mlsanity.com/paper/2110.05243/) (2021)
- [A Survey on Generative Diffusion Model](https://mlsanity.com/paper/2209.02646/) (2022)
- [Diffusion Models for Medical Anomaly Detection](https://mlsanity.com/paper/2203.04306/) (2022)
- [Refusion: Enabling Large-Size Realistic Image Restoration with Latent-Space Diffusion Models](https://mlsanity.com/paper/2304.08291/) (2023)
- [Conversion Between CT and MRI Images Using Diffusion and Score-Matching Models](https://mlsanity.com/paper/2209.12104/) (2022)
- [Adaptive Latent Diffusion Model for 3D Medical Image to Image Translation: Multi-modal Magnetic Resonance Imaging Study](https://mlsanity.com/paper/2311.00265/) (2023)
- [SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation](https://mlsanity.com/paper/2212.08228/) (2022)
- [High-Frequency Space Diffusion Models for Accelerated MRI](https://mlsanity.com/paper/2208.05481/) (2022)
- [SuperDTI: Ultrafast diffusion tensor imaging and fiber tractography with deep learning](https://mlsanity.com/paper/2002.01031/) (2020)
- [A microstructure estimation Transformer inspired by sparse representation for diffusion MRI](https://mlsanity.com/paper/2205.06450/) (2022)
- [Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction](https://mlsanity.com/paper/1911.01458/) (2019)
- [DoseDiff: Distance-aware Diffusion Model for Dose Prediction in Radiotherapy](https://mlsanity.com/paper/2306.16324/) (2023)
- [Diffusion Tensor Estimation with Transformer Neural Networks](https://mlsanity.com/paper/2201.05701/) (2022)
- [A Two-Stage Generative Model with CycleGAN and Joint Diffusion for MRI-based Brain Tumor Detection](https://mlsanity.com/paper/2311.03074/) (2023)

## Built on by (most cited first, 2 of 2)

- [MS-CustomNet: Controllable Multi-Subject Customization with Hierarchical Relational Semantics](https://mlsanity.com/paper/2603.21136/) (2026)
- [TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs](https://mlsanity.com/paper/2606.25545/) (2026)

## Abstract (arXiv)

Magnetic resonance diffusion tensor imaging (DTI) is a critical tool for neural disease diagnosis. However, long scan time greatly hinders the widespread clinical use of DTI. To accelerate image acquisition, a feature-enhanced joint diffusion model (Diff-DTI) is proposed to obtain accurate DTI parameter maps from a limited number of diffusion-weighted images (DWIs). Diff-DTI introduces a joint diffusion model that directly learns the joint probability distribution of DWIs with DTI parametric maps for conditional generation. Additionally, a feature enhancement fusion mechanism (FEFM) is designed and incorporated into the generative process of Diff-DTI to preserve fine structures in the generated DTI maps. A comprehensive evaluation of the performance of Diff-DTI was conducted on the Human Connectome Project dataset. The results demonstrate that Diff-DTI outperforms existing state-of-the-art fast DTI imaging methods in terms of visual quality and quantitative metrics. Furthermore, Diff-DTI has shown the ability to produce high-fidelity DTI maps with only three DWIs, thus overcoming the requirement of a minimum of six DWIs for DTI.