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, Perez-Lopez Raquel, Grussu Francesco
Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Department of Biomedicine, Faculty of Medicine and Health Sciences, University of Barcelona, Barcelona, Spain.
Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain.
Med Image Anal. 2025 May;102:103531. doi: 10.1016/j.media.2025.103531. Epub 2025 Mar 7.
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 or the vascular Apparent Diffusion Coefficient (ADC) D. The IVIM metrics, while sensitive to perfusion, are 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 for a given voxel. These facts hamper their practical clinical utility, and 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, as in the assessment of the response to anti-angiogenic therapies targeting tumour 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 within 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, and 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.
扩散磁共振成像（dMRI）使MRI信号对自旋运动敏感。这包括布朗扩散，也包括流经错综复杂的毛细血管网络的血流。这种效应，即体素内不相干运动（IVIM），通过诸如血管信号分数f或血管表观扩散系数（ADC）D等指标，能够利用dMRI对微血管进行表征。IVIM指标虽然对灌注敏感，但依赖于检查方案，并且其解释可能会根据dMRI测量期间自旋所经历的流动状态（例如，扩散与弹道）而改变，而对于给定的体素，这种流动状态通常是未知的。这些事实阻碍了它们在临床实践中的应用，因此需要创新的血管dMRI模型来实现体内计算具有生物学意义的毛细血管血流标志物。这些标志物在癌症中可能有相关应用，例如在评估针对肿瘤血管的抗血管生成疗法的反应中。本文通过引入SpinFlowSim来满足这一需求，SpinFlowSim是一种用于模拟管网内血流产生的dMRI信号的开源模拟器。SpinFlowSim针对毛细血管内的层流模式进行了定制，在给定从组织学重建的网络的情况下，能够合成高度逼真的微血管dMRI信号。我们通过为15个从肝活检重建的网络生成合成信号来展示该模拟器，这些网络包含癌组织和非癌组织。信号表现出复杂的、非单指数行为，与体内信号模式一致，表明同一网络内不同流动状态的共存以及扩散时间依赖性。我们还通过设计一种基于合成信号的微血管特性映射策略，并专注于血流速度分布矩和表观网络分支指数的量化，展示了SpinFlowSim的潜在效用。这些指标在计算机模拟和体内进行了估计，体内实验中，对1.5T和3T扫描的健康志愿者以及1.5T扫描的13名癌症患者进行了研究。总之，像SpinFlowSim所实现的逼真血流模拟，可能在下一代微血管映射dMRI方法的开发中发挥关键作用，并在肿瘤学中立即得到应用。
