Advances in acquisition and post-processing optimization of IVIM MRI for brain imaging: A systematic review

Diffusion-weighted MRI is widely used to probe brain microstructure, but its signal reflects both diffusion and perfusion effects. Intravoxel Incoherent Motion (IVIM) MRI enables non-contrast separation of these components, offering potential clinical value in neuroimaging. However, clinical translation remains limited due to variability in acquisition and post-processing methods. This systematic review evaluates optimization strategies aimed at improving the accuracy, reproducibility, and clinical utility of IVIM parameters in brain. Registered in PROSPERO and conducted according to PRISMA guidelines, a systematic search across five databases was performed. Original peer-reviewed studies focusing on optimization of IVIM acquisition or post-processing in human brain imaging were included, while reviews and studies lacking methodological detail were excluded. Study quality was assessed using a customized QUADAS-2 tool. Due to methodological heterogeneity, an effect direction plot was applied instead of meta-analysis. Out of 1,668 identified records, 14 studies were included. Acquisition strategies such as optimized b-value sampling, cardiac gating, and advanced sequences reduced parameter variability by up to 40%. Post-processing methods, including Bayesian fitting, deep learning–based models, and advanced denoising, improved parameter accuracy by up to 99% and precision by up to 95%. Effect direction analysis demonstrated significant positive effects on accuracy and clinical utility (p < 0.001) and repeatability (p < 0.05), while scan-time reduction showed no significant benefit (p > 0.05). No study reported gold-standard validation. Optimization of IVIM acquisition and post-processing enhances parameter robustness and reproducibility in brain MRI, though protocol heterogeneity remains a major obstacle to standardization and clinical adoption.