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Updated: Aug 26, 2026
Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
Published on: November 8, 2012
Evaluation of acquisition strategies in IVIM-DKI brain imaging: Effects on image quality, parameter stability, and
Abhijith S1, Saikiran Pendem1, Rajagopal Kadavigere2
1Department of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, India.
Background:
Intravoxelincoherent motion (IVIM) and diffusion kurtosis imaging (DKI) provide complementary information on cerebral microstructure and microvascular perfusion without contrast agents. However, clinical translation of IVIM-DKI imaging is limited by variability in b-value selection and signal averaging, leading to inconsistent parameter estimates, and by limited validation against contrast-enhanced perfusion MRI. This study aimed to identify a robust IVIM-DKI acquisition strategy and evaluate its relationship with DSC (Dynamic Susceptibility Contrast)-derived perfusion metrics.
Methods:
This prospective study included 29 adults undergoing brain MRI on a 3T scanner (uMR 780). IVIM-DKI was acquired using four protocols (A1-A4) with varying b-value density and averaging strategies, including high b-value sampling (A1), optimized low b-value-weighted acquisition with targeted averaging (A2), and reduced sampling schemes (A3-A4). DSC perfusion imaging was subsequently performed. Parametric maps were generated using vendor-provided tools. Standardized gray matter (GM) and white matter (WM) ROIs were placed by two blinded radiologists. Image quality (SNR, CNR), stability (CV), and GM-WM contrast were compared using Friedman tests. Spearman correlation and regression-based analysis were used to assess IVIM-DSC relationships.
Results:
A2 demonstrated higher SNR and CNR and lower CV for most IVIM-derived perfusion parameters (p < 0.05), while certain diffusion and kurtosis metrics showed superior performance with alternative acquisitions (MK with A1 and MD with A3). A2 also achieved the highest GM-WM ratios for f (2.25), rBF (2.89), and D* (1.53), indicating improved tissue contrast. IVIM perfusion parameters showed moderate correlations with DSC-derived perfusion metrics, with f demonstrating the strongest association with rCBF (ρ = 0.67) and D* showing the highest correlation with rCBV (ρ = 0.51). Equation-based analysis demonstrated a strong relationship between rCBV and c_CBV (r = 0.77, p < 0.001), whereas no meaningful association was observed between MTT and c_MTT (r = 0.03, p = 0.84).
Conclusion:
An optimized low b-value-weighted acquisition with targeted averaging (A2) provided the strongest overall balance between image quality, parameter stability, tissue contrast, and correspondence with DSC-derived perfusion measures. IVIM perfusion metrics demonstrate physiologically meaningful but non-equivalent relationships with DSC, supporting their complementary role in perfusion assessment.
Plain Language Summary:
Brain MRI scans can provide information about brain tissue structure and blood flow, but finding the best scan settings remains a challenge. This study compared several acquistion strategies for IVIM-DKI MRI in adults to identify which approach produced the most reliable image measurements and how these measurements related to a standard blood flow imaging technique. This study found that one optimized scanning method provided the best balance of image quality, measurement stability, tissue contrast, and agreement with blood flow measurements. This matters because more reliable brain imaging may help clinicians better assess brain health without relying solely on contrast agents.
