Intravoxel Incoherent Motion Magnetic Resonance Imaging

Intravoxel Incoherent Motion (IVIM) magnetic resonance imaging is a specialised diffusion‐weighted technique that disentangles true molecular diffusion from microvascular perfusion by fitting a biexponential signal decay model. By acquiring multiple diffusion weightings (b‐values), IVIM quantifies the diffusion coefficient (D), reflecting water mobility in tissue, alongside the pseudo‐diffusion coefficient (D*), associated with capillary blood flow, and the perfusion fraction (f), denoting the proportion of signal arising from microcirculation. This approach provides non‐invasive insights into tissue microstructure and vascularity without the need for exogenous contrast agents. Clinically, IVIM has been explored for characterising tumours, assessing cerebral microvasculature pulsatility, evaluating treatment response in oncology, differentiating liver malignancies, and measuring bone marrow changes in metabolic bone disease. Technical challenges include low signal‐to‐noise ratio, sensitivity to echo‐time effects, and the complexity of robust parameter estimation. Recent advances leverage constrained or segmented fitting methods, extended models that correct for T2 dependence, and physics‐informed deep learning architectures to enhance accuracy and reproducibility. The global significance of IVIM lies in its capacity to deliver quantitative biomarkers of diffusion and perfusion across neurological, oncological and musculoskeletal applications, thereby informing diagnosis, prognostication and therapeutic monitoring.

Research from Nature Portfolio

Recent studies have demonstrated the clinical utility of IVIM‐DWI in hepatic oncology. One investigation evaluated IVIM parameters in patients with hepatocellular carcinoma versus intrahepatic cholangiocarcinoma, revealing that the pseudo‐diffusion coefficient provided the highest diagnostic performance. The perfusion fraction showed no significant difference between these cancer types, whereas lower diffusion coefficients were characteristic of hepatocellular carcinoma. By optimising cut‐off values for diffusion and pseudo‐diffusion metrics, this work achieved high sensitivity and specificity in lesion differentiation, underscoring the value of IVIM as a non‐invasive tool for tumour characterisation.

Intravoxel Incoherent Motion Magnetic Resonance Imaging publication trend

The graph below shows the total number of articles in intravoxel incoherent motion magnetic resonance imaging across all publications each year (not limited to Nature Index journals).

Technical terms

Diffusion coefficient (D): Quantifies the rate of water molecule diffusion within tissue microstructure.

Pseudo-diffusion coefficient (D*): Reflects incoherent microvascular flow, modelling perfusion‐related signal decay.

Perfusion fraction (f): Represents the proportion of MR signal attributable to capillary blood flow within a voxel.

B-value: A parameter controlling the strength of diffusion weighting in the MR sequence, with higher values increasing sensitivity to diffusion effects.

Signal-to-noise ratio (SNR): The measure of signal strength relative to background noise, critical for reliable parameter estimation.

Biexponential model: A fitting approach that separates fast (perfusion) and slow (diffusion) signal decay components in IVIM MRI.

References

1. Incorporating spatial information in deep learning parameter estimation with application to the intravoxel incoherent motion model in diffusion-weighted MRI. Medical Image Analysis (2024).
2. Evaluation of Whole Brain Intravoxel Incoherent Motion (IVIM) Imaging. Diagnostics (2024).
3. IDEAL-IQ combined with intravoxel incoherent motion diffusion-weighted imaging for quantitative diagnosis of osteoporosis. BMC Medical Imaging (2024).
4. Dependence of Brain Intravoxel Incoherent Motion Perfusion Parameters on the Cardiac Cycle. PLOS ONE (2013).
5. Histogram Analysis of Intravoxel Incoherent Motion for Differentiating Recurrent Tumor from Treatment Effect in Patients with Glioblastoma: Initial Clinical Experience. American Journal of Neuroradiology (2013).
6. Extended T2-IVIM model for correction of TE dependence of pseudo-diffusion volume fraction in clinical diffusion-weighted magnetic resonance imaging. Physics in Medicine and Biology (2016).
7. Improved unsupervised physics‐informed deep learning for intravoxel incoherent motion modeling and evaluation in pancreatic cancer patients. Magnetic Resonance in Medicine (2021).
8. Intravoxel incoherent motion diffusion-weighted imaging to differentiate hepatocellular carcinoma from intrahepatic cholangiocarcinoma. Scientific Reports (2020).