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Simulation-Informed Evaluation of Microvascular Parameter Mapping for Diffusion MR Imaging of Solid Tumours.

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Summary

New diffusion MRI (dMRI) methods using simulation-informed modeling identified mean volumetric flow rate (q_m) and Apparent Network Branching (ANB) as promising metrics for cancer microvasculature quantification.

Keywords:
cancerdiffusion MRImicrovasculaturemodellingsimulations

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Area of Science:

  • Biomedical Imaging
  • Medical Physics
  • Radiology

Background:

  • Diffusion MRI (dMRI) is crucial for microvasculature quantification.
  • Enhancing biological specificity in cancer imaging is an ongoing challenge.

Purpose of the Study:

  • To inform the design of novel dMRI approaches for cancer microvasculature quantification.
  • To improve the biological specificity of dMRI in cancer imaging.

Main Methods:

  • Utilized simulation-informed modeling of vascular dMRI signals.
  • Synthesized signals from 1500 synthetic vascular networks across various protocols and b-samplings.
  • Estimated recoverable signal degrees of freedom and ranked microvascular metrics based on estimation quality.

Main Results:

  • Vascular signals exhibited complex behavior, including diffusion time dependence.
  • Two independent degrees of freedom were recoverable from directionally-averaged signals.
  • Mean volumetric flow rate (q_m) and Apparent Network Branching (ANB) index showed maximal correlation in silico and detected re-vascularization in a cancer patient.

Conclusions:

  • Simulation-based modeling identifies q_m and ANB as promising metrics for microvasculature characterization.
  • In vivo estimation of q_m and ANB is feasible and captures biologically plausible trends.
  • These metrics show potential for future studies in cancer imaging.