Beyond ADC: Advanced Diffusion-Weighted MRI Techniques and Their Clinical Applications in Neurology

Diffusion-weighted imaging (DWI) has become a cornerstone of neuroimaging by enabling non-invasive assessment of tissue microstructure through the measurement of water molecule motion. The apparent diffusion coefficient (ADC), derived from conventional mono-exponential modeling, has demonstrated significant clinical utility, particularly in acute ischemic stroke and tumor evaluation. However, its limited specificity and inability to capture the complex, non-Gaussian behavior of water diffusion in biological tissues have driven the development of advanced diffusion MRI techniques.

This review provides a comprehensive overview of diffusion MRI methods beyond ADC, including diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), and multi-compartment models such as neurite orientation dispersion and density imaging (NODDI). These techniques offer improved characterization of tissue microstructure by incorporating directional diffusion, non-Gaussian signal behavior, and compartmental modeling. Advances in acquisition strategies, including multi-shell imaging and high b-value diffusion, along with artificial intelligence-based reconstruction and analysis, have further enhanced the capabilities and clinical feasibility of diffusion MRI.

The clinical applications of advanced diffusion MRI in neurology are discussed, including its role in acute stroke, brain tumors, demyelinating diseases, neurodegenerative disorders, epilepsy, and developmental conditions. Emerging techniques such as whole-brain IVIM, ultra-high b-value imaging, and diffusion fingerprinting are also highlighted. Despite these advancements, challenges related to standardization, reproducibility, and biomarker validation remain significant barriers to widespread clinical adoption.

Future directions emphasize the need for harmonized acquisition protocols, multicenter validation, integration with complementary imaging modalities, and the incorporation of artificial intelligence to improve robustness and efficiency. In conclusion, advanced diffusion MRI holds substantial promise as a quantitative imaging biomarker, with the potential to significantly enhance the diagnosis, monitoring, and understanding of neurological diseases.