SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer

Voronova, Anna Kira, Grigoriou, Athanasios, Bernatowicz, Kinga, Simonetti, Sara, Serna, Garazi, Roson, Núria, Escobar, Manuel, Vieito, Maria, Nuciforo, Paolo, Toledo, Rodrigo, Garralda, Elena, Fieremans, Els, Novikov, Dmitry S., Palombo, Marco ORCID: https://orcid.org/0000-0003-4892-7967, Perez-Lopez, Raquel and Grussu, Francesco 2025. SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer. Medical Image Analysis 102 , 103531. 10.1016/j.media.2025.103531

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Abstract

Diffusion Magnetic Resonance Imaging (dMRI) sensitises the MRI signal to spin motion. This includes Brownian diffusion, but also flow across intricate networks of capillaries. This effect, the intra-voxel incoherent motion (IVIM), enables microvasculature characterisation with dMRI, through metrics such as the vascular signal fraction f V or Apparent Diffusion Coefficient (ADC) D ∗ . The IVIM metrics, while sensitive to perfusion, are in general protocol-dependent, and their interpretation can change depending on the flow regime spins experience during the dMRI measurements (e.g., diffusive vs ballistic), which is in general not known — facts that hamper their clinical utility. Innovative vascular dMRI models are needed to enable the in vivo calculation of biologically meaningful markers of capillary flow. These could have relevant applications in cancer, for instance assessing responses to anti-angiogenic therapies targeting tumor vessels. This paper tackles this need by introducing SpinFlowSim, an open-source simulator of dMRI signals arising from blood flow within pipe networks. SpinFlowSim, tailored for the laminar flow patterns in capillaries, enables the synthesis of highly-realistic microvascular dMRI signals, given networks reconstructed from histology. We showcase the simulator by generating synthetic signals for 15 networks, reconstructed from liver biopsies, and containing cancerous and non-cancerous tissue. Signals exhibit complex, non-mono-exponential behaviours, consistent with in vivo signal patterns, and pointing towards the co-existence of different flow regimes within the same network, as well as diffusion time dependence. We also demonstrate the potential utility of SpinFlowSim by devising a strategy for microvascular property mapping informed by the synthetic signals, focussing on the quantification of blood velocity distribution moments, and of an apparent network branching index. These were estimated in silico and in vivo, in healthy volunteers scanned at 1.5T and 3T and in 13 cancer patients, scanned at 1.5T. In conclusion, realistic flow simulations, as those enabled by SpinFlowSim, may play a key role in the development of the next-generation of dMRI methods for microvascular mapping, with immediate applications in oncology.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics Schools > Psychology Research Institutes & Centres > Cardiff University Brain Research Imaging Centre (CUBRIC)
Additional Information:	License information from Publisher: LICENSE 1: URL: http://creativecommons.org/licenses/by-nc/4.0/, Start Date: 2025-03-07
Publisher:	Elsevier
ISSN:	1361-8415
Date of First Compliant Deposit:	12 March 2025
Date of Acceptance:	24 February 2025
Last Modified:	12 Mar 2025 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/176818

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