Tamás I. Józsa^{1}, Jan Petr^{2}, Alle Meije Wink^{3}, Frederik Barkhof^{3}, Henk J. M. M. Mutsaerts^{3}, and Stephen J. Payne^{1}

^{1}Department of Engineering Science, University of Oxford, Oxford, United Kingdom, ^{2}Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany, ^{3}Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, Netherlands

Human brain perfusion simulations have been limited to less than five patient-specific cases. We propose a pipeline based on MRI to overcome this limitation. Computational geometry is adjusted using T1-weighted MRI, and the perfusion model parameters are tuned based on arterial spin labeling perfusion MRI. A cohort of 75 patients is used to demonstrate that the pipeline is suitable to generate virtual patients with statistically accurate and precise cerebral blood flow maps. Our findings encourage future studies on* in silico* clinical trials using similar virtual cohorts to improve ischaemic stroke interventions.

Several computational studies have presented porous circulation models to estimate Cerebral Blood Flow (CBF) corresponding to healthy

The computational perfusion model describes CBF in the microcirculation based on a set of partial differential equations: $$ \nabla \cdot (K_i \nabla p_i) - \sum_{j=1}^3 \beta_{ij} (p_i-p_j) = 0. $$ Here, $$$i=1,~2,~3$$$ represents the arteriole, capillary, and venule compartments (the Einstein summation notation is not used). E.g., $$$p_1$$$ and $$$K_1$$$ are the arteriole pressure and permeability tensor, and $$$\beta_{12}$$$ is the arteriole-capillary coupling coefficient. Each permeability tensor $$$K_i$$$ is characterised by a single scalar $$$k_i$$$ as described previously

Several model parameters are randomised to simulate physiological variability whereas others are optimised to match real patients' CBF statistics

- $$$CPP=84.0\pm8.5$$$ [mmHg] (set based on
^{9}); - $$$k_2=(4.26\pm0.46)\cdot 10^{-4}$$$ [mm
^{3}s kg^{-1}] (set based on^{4}); - $$$k_3 / k_1=2.0\pm0.2$$$ (set based on
^{2,3}); - $$$\beta_{12}^{GM}=(1.17\pm0.27)\cdot 10^{-6}$$$ [Pa
^{-1}s^{-1}] (set based on^{2,3,4,5}), and - $$$\beta_{23}^{GM}=(4.22\pm1.02)\cdot 10^{-6}$$$ [Pa
^{-1}s^{-1}] (set based on^{2,3,4,5}).

- $$$k_1=0.98\pm0.25\cdot 10^{-4}$$$ [mm
^{3}s kg^{-1}], and - $$$\beta_{ij}^{GM} / \beta_{ij}^{WM}=3.68\pm0.91$$$.

This work was supported by the European Union‘s Horizon 2020 research and innovation programme, the INSIST project, under grant agreement No 777072. TIJ and SJP are grateful to the members of the INSIST consortium for useful discussions and their sustained support. The EPAD data were obtained with support from the following EU/EFPIA Innovative Medicines Initiatives (1 and 2) Joint Undertakings: EPAD grant no. 115736, AMYPAD grant no. 115952. HJMMM is supported by the Dutch Heart Foundation (2020T049), and by the Eurostars-2 joint programme with co-funding from the European Union Horizon 2020 research and innovation programme, provided by the Netherlands Enterprise Agency (RvO).

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Figure 1. Schematic drawing of the virtual patient generation pipeline. Solid boxes: input; dash-dotted box: output; dotted boxes: quasi-patient-speciﬁc mesh generation; dashed boxes: model parameter tuning.

Figure 2. (a) Coronal view of the affine registration from the mesh to the patient space, with overlap (green), patient only (yellow) and mesh only (red). (b) Bland-Altman plot of the brain volume corresponding to virtual ($$$V_{vp}$$$) and real patients ($$$V_{rp}$$$). (c) Average (ave) CBF values in virtual ($$$F_{vp}$$$) and real ($$$F_{rp}$$$) patients. (d) Minimum (min) and maximum (max) CBF values in real and virtual patients.

Figure 3. Simulated cerebral blood flow maps corresponding to the same virtual patient as Figure 1: (a)&(d) sagittal, (b)&(r) coronal, and (c)&(f) axial views. Healthy scenario (a)-(c) and right middle cerebral artery occlusion (d)-(f).