TY - JOUR
T1 - Improving microstructural integrity, interstitial fluid, and blood microcirculation images from multi-b-value diffusion MRI using physics-informed neural networks in cerebrovascular disease
AU - Voorter, Paulien H.M.
AU - Backes, Walter H.
AU - Gurney-Champion, Oliver J.
AU - Wong, Sau May
AU - Staals, Julie
AU - van Oostenbrugge, Robert J.
AU - van der Thiel, Merel M.
AU - Jansen, Jacobus F.A.
AU - Drenthen, Gerhard S.
PY - 2023/10
Y1 - 2023/10
N2 - Purpose: To obtain better microstructural integrity, interstitial fluid, and microvascular images from multi-b-value diffusion MRI data by using a physics-informed neural network (PINN) fitting approach. Methods: Test–retest whole-brain inversion recovery diffusion-weighted images with multiple b-values (IVIM: intravoxel incoherent motion) were acquired on separate days for 16 patients with cerebrovascular disease on a 3.0T MRI system. The performance of the PINN three-component IVIM (3C-IVIM) model fitting approach was compared with conventional fitting approaches (i.e., non-negative least squares and two-step least squares) in terms of (1) parameter map quality, (2) test–retest repeatability, and (3) voxel-wise accuracy. Using the in vivo data, the parameter map quality was assessed by the parameter contrast-to-noise ratio (PCNR) between normal-appearing white matter and white matter hyperintensities, and test–retest repeatability was expressed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The voxel-wise accuracy of the 3C-IVIM parameters was determined by 10,000 computer simulations mimicking our in vivo data. Differences in PCNR and CV values obtained with the PINN approach versus conventional fitting approaches were assessed using paired Wilcoxon signed-rank tests. Results: The PINN-derived 3C-IVIM parameter maps were of higher quality and more repeatable than those of conventional fitting approaches, while also achieving higher voxel-wise accuracy. Conclusion: Physics-informed neural networks enable robust voxel-wise estimation of three diffusion components from the diffusion-weighted signal. The repeatable and high-quality biological parameter maps generated with PINNs allow for visual evaluation of pathophysiological processes in cerebrovascular disease.
AB - Purpose: To obtain better microstructural integrity, interstitial fluid, and microvascular images from multi-b-value diffusion MRI data by using a physics-informed neural network (PINN) fitting approach. Methods: Test–retest whole-brain inversion recovery diffusion-weighted images with multiple b-values (IVIM: intravoxel incoherent motion) were acquired on separate days for 16 patients with cerebrovascular disease on a 3.0T MRI system. The performance of the PINN three-component IVIM (3C-IVIM) model fitting approach was compared with conventional fitting approaches (i.e., non-negative least squares and two-step least squares) in terms of (1) parameter map quality, (2) test–retest repeatability, and (3) voxel-wise accuracy. Using the in vivo data, the parameter map quality was assessed by the parameter contrast-to-noise ratio (PCNR) between normal-appearing white matter and white matter hyperintensities, and test–retest repeatability was expressed by the coefficient of variation (CV) and intraclass correlation coefficient (ICC). The voxel-wise accuracy of the 3C-IVIM parameters was determined by 10,000 computer simulations mimicking our in vivo data. Differences in PCNR and CV values obtained with the PINN approach versus conventional fitting approaches were assessed using paired Wilcoxon signed-rank tests. Results: The PINN-derived 3C-IVIM parameter maps were of higher quality and more repeatable than those of conventional fitting approaches, while also achieving higher voxel-wise accuracy. Conclusion: Physics-informed neural networks enable robust voxel-wise estimation of three diffusion components from the diffusion-weighted signal. The repeatable and high-quality biological parameter maps generated with PINNs allow for visual evaluation of pathophysiological processes in cerebrovascular disease.
KW - free water
KW - intravoxel incoherent motion
KW - microvascular perfusion
KW - multi-b-value diffusion MRI
KW - parenchymal diffusion
KW - physics-informed deep learning
UR - http://www.scopus.com/inward/record.url?scp=85161843321&partnerID=8YFLogxK
U2 - 10.1002/mrm.29753
DO - 10.1002/mrm.29753
M3 - Article
C2 - 37317641
AN - SCOPUS:85161843321
SN - 0740-3194
VL - 90
SP - 1657
EP - 1671
JO - Magnetic Resonance in Medicine
JF - Magnetic Resonance in Medicine
IS - 4
ER -