2025/03/22 by Semmineh, Natenael B., Guha, Indranil, Healey, Deborah +3
#FOS: Electrical engineering #FOS: Physical sciences #Medical Physics (physics.med-ph) #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2503.17600
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 ex vivo 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 in vivo validation, intravoxel VSD imaging could become a transformative preclinical and clinical tool for interrogating disease and treatment induced vascular remodelling.