SpinFlowSim: A blood flow simulation framework for histology-informed diffusion MRI microvasculature mapping in cancer
Year: 2025
Type: article
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 com-plex, non-mono-exponential behaviours, pointing towards the co-existence of different flow regimes within the same network, and 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 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.
Source: Medical Image Analysis
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,170
Funders: Prostate Cancer Foundation, AstraZeneca, Fundación BBVA, Fundación Cellex, UK Research and Innovation, Centres de Recerca de Catalunya, AstraZeneca España, Fundación Científica Asociación Española Contra el Cáncer, Generalitat de Catalunya, Banco Bilbao Vizcaya Argentaria, Universitat de Barcelona, Fundación Fero, Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona, CRIS Cancer Foundation, Horizon 2020 Framework Programme, Swiss Re Foundation, Agència de Gestió d'Ajuts Universitaris i de Recerca, Instituto de Salud Carlos III, European Regional Development Fund, Agencia Estatal de Investigación, Crown Research Institutes, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, European Social Fund, H2020 Marie Skłodowska-Curie Actions
Awards: G1518147888, Estudio de validación clínica de la resonancia de cuerpo completo con difusión en pacientes con cáncer de próstata resistente a la castración y metástasis óseas., G2480270773, G2628364946, G278658663, Beatriu de Pinos-3 Postdoctoral Programme, G343272511, G3480869486, G3573906735, G3760634547, G3870962429, G3986837662, G406815724, G4186234410, G4545228374, PRECISION: Descifrando la heterogeneidad del cáncer de colon con imagen de precisión y aprendizaje automático, G5096576663, G5248887686, G5386110335, G5640072750, G5955501538, G6294978995, G683931746, G7084143925, G8153271282, G8461804941, G868296542, G8824911526, Center for Advanced Imaging Innovation and Research (CAI2R)
OpenAlex ID: W4408167692