Keywords: Simulation/Validation, Quantitative Imaging
Motivation: There are large variations in perfusion-related quantifications using the IVIM model in disease diagnosis and therapeutic response evaluation.
Goal(s): This study aims to improve the reliability of applying IVIM model in hypoxic level classification by considering the model parameter collinearity.
Approach: We introduced a Bayesian inference method to estimate IVIM parameter probability distribution, followed by a linear discrimination analysis. This analysis produces a robust metric for distinguishing hypoxic and non-hypoxic tumor tissues.
Results: A reliable metrics, as a linear combination of two IVIM parameters (Dt and Fp), accurately reflects tumor tissue hypoxia levels using established outcomes as training dataset and a reference.
Impact: We addressed the challenge of uncertainty in IVIM parameter fitting arising from the strong collinearity inherent in the IVIM biexponential model. Additionally, we introduced a robust metric, H-index, for distinguishing between hypoxic and non-hypoxic tumors, referencing previous histopathologically confirmed data.
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