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A new MRI method non-invasively images vessel size distribution (VSD) and cerebral blood volume (CBV). This approach aids in assessing vascular remodelling in diseases like cancer and diabetes.

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Area of Science:

  • Biomedical Imaging
  • Neuroimaging
  • Vascular Biology

Background:

  • Vascular remodelling is a key factor in numerous diseases, including cancer, neurodegeneration, fibrosis, hypertension, and diabetes.
  • Accurate assessment of vascular changes is crucial for understanding disease progression and treatment efficacy.

Purpose of the Study:

  • To develop and validate a novel, non-invasive MRI approach for imaging intravoxel vessel size distribution (VSD).
  • To enable a more comprehensive and quantitative assessment of vascular remodelling in preclinical and clinical settings.

Main Methods:

  • Utilized high-resolution light-sheet fluorescence microscopy of rodent brain vasculature.
  • Simulated gradient echo sampling of free induction decay and spin echo (GESFIDE) MRI signals.
  • Trained a deep learning model to predict cerebral blood volume (CBV) and VSD from simulated GESFIDE signals.

Main Results:

  • Demonstrated a strong correlation (r = 0.96) between true and predicted CBV in ex vivo experiments.
  • Observed high similarity between true and predicted VSDs (mean Bhattacharya Coefficient = 0.92).
  • The deep learning model successfully predicted CBV and VSD from MRI signals.

Conclusions:

  • The developed susceptibility-contrast based MRI approach enables non-invasive intravoxel VSD imaging.
  • This technique offers a transformative tool for interrogating vascular remodelling in disease and in response to treatment.
  • Further in vivo validation could establish this as a valuable preclinical and clinical tool.