A new framework for assessing subject-specific whole brain circulation and perfusion using MRI-based measurements and...

Area of Science:

• Medical Imaging
• Biophysics
• Computational Neuroscience

Background:

• Microvascular alterations are linked to severe medical conditions.
• Accurate simulation of brain perfusion is crucial for diagnostics, treatment evaluation, and surgical planning.
• Traditional one-compartment models suffer from scale dependency, limiting accuracy.

Purpose of the Study:

• To propose a scale-invariant mathematical framework for simulating whole brain perfusion.
• To address the limitations of traditional tracer kinetic models in clinical applications.
• To provide a more accurate method for assessing brain circulation and perfusion.

Main Methods:

• Developed a scale-invariant mathematical framework based on anatomical geometry segmentation down to voxel resolution.
• Identified large vessels using time-of-flight (ToF) and quantitative susceptibility mapping (QSM).
• Modeled macro-scale flow with Hagen-Poiseuille equation and capillary flow as two-compartment porous media flow, coupled via a support function.

Main Results:

• Demonstrated a whole brain simulation of tracer propagation on a realistic human brain model.
• The model included distinct grey/white matter areas and large arterial/venous networks.
• The framework successfully coupled macro- and micro-scale flow dynamics.

Conclusions:

• The proposed framework offers an accurate and viable alternative to traditional compartment models for brain perfusion simulation.
• This approach has high relevance for clinical brain perfusion applications and restoration of field parameters.
• The scale-invariant nature enhances applicability across different resolutions and scales in neuroimaging.