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Time-dependent diffusion magnetic resonance imaging: principles, acquisition sequences, main models, current
Jiahao Wang1, Fang Lin1, Yongzhou Xu2
1Department of Radiology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China; Intelligent Medical Imaging of Jiangxi Key Laboratory, Nanchang, China.
European Journal of Radiology
|May 10, 2026
Summary
Time-dependent diffusion MRI (TDDMRI) maps tissue microstructure by analyzing water diffusion changes over time. This advanced MRI technique shows promise for diagnosing and characterizing various diseases, especially tumors.
Area of Science:
- Biomedical Imaging
- Biophysics
- Medical Physics
Background:
- Time-dependent diffusion magnetic resonance imaging (TDDMRI) is a powerful tool for assessing biological tissue microstructure.
- It leverages the time-dependent nature of restricted water diffusion to noninvasively infer tissue properties.
- Its utility is increasingly recognized in various disease research applications.
Purpose of the Study:
- To provide a comprehensive review of TDDMRI.
- To examine the physical principles, diffusion encoding sequences, and biophysical models of TDDMRI.
- To systematically explore TDDMRI's clinical applications in diseases and compare its diagnostic value to conventional methods.
Main Methods:
- Review of TDDMRI physical principles and diffusion encoding sequences.
- Analysis of widely adopted biophysical models for TDDMRI data interpretation.
- Systematic examination of clinical applications across various tumor types and skeletal muscle diseases.
Main Results:
- TDDMRI offers superior diagnostic value for malignant tumor detection and characterization compared to conventional imaging.
- The technique provides detailed insights into tissue microstructure, aiding disease diagnosis.
- Demonstrated efficacy in intracranial, cervical, thoracic, abdominopelvic tumors, and skeletal muscle diseases.
Conclusions:
- TDDMRI is a valuable technique for noninvasive tissue microstructure analysis and disease diagnosis.
- It exhibits significant advantages in detecting and characterizing malignant tumors.
- Further development is needed to address current limitations and enhance clinical translation.
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