Semmineh Natenael B, Guha Indranil, Healey Deborah, Chandrasekharan Anagha, Quarles C Chad, Boxerman Jerrold L
Department of Cancer Systems Imaging, Cancer Neuroscience Program, Neuroimaging Innovations to Transform Cancer Care (NeuroCare) Program, The University of Texas MD Anderson Cancer Center, Houston, TX, 77025, USA.
Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, 02903, USA.
ArXiv. 2025 Mar 25:arXiv:2503.17600v2.
Vascular remodelling is inherent to the pathogenesis of many diseases including cancer, neurodegeneration, fibrosis, hypertension, and diabetes. In this paper, a new susceptibility-contrast based MRI approach is established to non-invasively image intravoxel vessel size distribution (VSD), enabling a more comprehensive and quantitative assessment of vascular remodelling. The approach utilizes high-resolution light-sheet fluorescence microscopy images of rodent brain vasculature, simulating gradient echo sampling of free induction decay and spin echo (GESFIDE) MRI signals for the three-dimensional vascular networks, and training a deep learning model to predict cerebral blood volume (CBV) and VSD from GESFIDE signals. The results from experiments demonstrated strong correlation (r = 0.96) between the true and predicted CBV. High similarity between true and predicted VSDs was observed (mean Bhattacharya Coefficient = 0.92). With further validation, intravoxel VSD imaging could become a transformative preclinical and clinical tool for interrogating disease and treatment induced vascular remodelling.
血管重塑是包括癌症、神经退行性疾病、纤维化、高血压和糖尿病在内的许多疾病发病机制所固有的。本文建立了一种基于新的敏感性对比的MRI方法,以无创成像体素内血管大小分布(VSD),从而能够对血管重塑进行更全面和定量的评估。该方法利用啮齿动物脑血管系统的高分辨率光片荧光显微镜图像,模拟三维血管网络的自由感应衰减和自旋回波(GESFIDE)MRI信号的梯度回波采样,并训练深度学习模型从GESFIDE信号预测脑血容量(CBV)和VSD。实验结果表明,真实CBV与预测CBV之间具有很强的相关性(r = 0.96)。观察到真实VSD与预测VSD之间具有高度相似性(平均Bhattacharya系数 = 0.92)。经过进一步验证,体素内VSD成像可能成为一种变革性的临床前和临床工具,用于研究疾病和治疗引起的血管重塑。