Next to parenchymal diffusion and microvascular pseudo-diffusion, a third diffusion component is present in cerebral intravoxel incoherent motion (IVIM) imaging, representing interstitial fluid. Fitting the three-component IVIM model using conventional fitting methods strongly suffers from image noise. Therefore, we explored the applicability of a physics-informed neural network (PI-NN) fitting approach, previously shown to be more robust to noise. Using test-retest data from sixteen patients with cerebrovascular disease, we found higher repeatability of all IVIM parameters using PI-NN. Furthermore, simulations showed that PI-NN provided more accurate IVIM parameters. Hence, using PI-NN is promising to obtain tissue markers of cerebrovascular disease.
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