# Diffusion weighted imaging in musculoskeletal system: where are we now?

## TL;DR

This paper reviews how diffusion-weighted imaging helps diagnose musculoskeletal disorders by tracking water movement in tissues.

## Contribution

The paper provides a comprehensive review of current DWI and DTI applications in musculoskeletal imaging.

## Key findings

- DWI uses ADC and FA metrics to assess tissue integrity and pathology.
- DTI and DKI improve evaluation of complex tissue structures like cartilage and ligaments.
- DWI aids in differentiating benign and malignant soft tissue tumors and monitoring treatment responses.

## Abstract

Diffusion-weighted imaging (DWI) is an advanced MRI technique that harnesses the movement of water molecules within tissues to assess and characterize a wide range of musculoskeletal disorders. By differentiating between isotropic and anisotropic diffusion, DWI provides critical insights into tissue integrity and pathology, proving instrumental in diagnosing conditions. Its sensitivity to changes in tissue microstructure is quantified through metrics like the apparent diffusion coefficient (ADC) and fractional anisotropy (FA). Advanced methodologies, including diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI), further enhance DWI's ability to evaluate complex tissue architectures, offering vital information on muscle, ligament, tendon, and cartilage health. DWI also excels in the assessment of soft tissue tumours, infections, and joint pathologies, enabling accurate differentiation between benign and malignant lesions and facilitating early detection of conditions like osteomyelitis. Additionally, DWI plays a crucial role in monitoring treatment responses, with ADC changes correlating to tumour necrosis and recurrence. Despite its advantages, DWI faces limitations, such as technical artefacts and challenges in interpretation that can impact diagnostic accuracy. This review explores the diverse applications of DWI and DTI in musculoskeletal imaging, highlighting their potential to improve diagnostic precision and clinical outcomes while addressing ongoing challenges in the field.