Abstract #0323

A novel biophysical simulation framework for intravascular MRI signals using 3D Vascular Anatomical Networks applied to VASO-fMRI

Grant Hartung^1,2, Daniel Gomez^1,2,3, Avery Berman^4,5, and Jonathan R. Polimeni^1,2,6

¹A.A. Martinos Center For Biomedical Imaging, MGH, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States, ⁴Physics, Carleton University, Ottowa, ON, Canada, ⁵Royal Ottowa Mental Health Centre, Ottowa, ON, Canada, ⁶Massachusetts Institute of Technology, Cambridge, MA, United States

Synopsis

Keywords: fMRI Acquisition, Blood vessels, brain, contrast mechanisms, flow, fMRI (task based), gray matter, high-field MRI, in silico, modelling, signal modeling

Motivation: The emerging fMRI method VASO provides improved neuronal specificity compared to BOLD, however the precise interpretation of its origins and principled means to optimize this sequence is not straightforward.

Goal(s): To use biophysical models to investigate the origins of the VASO signal and compare it with direct estimates of CBV.

Approach: We extend our 3D biophysical Vascular Anatomical Network framework to incorporate intravascular signals undergoing inversion recovery to model the VASO sequence.

Results: The VASO signal appears sensitive to slab thickness, and activation biases occur if the slab is too thin. Simulated profiles of VASO differ from measurements, possibly due to model simplifications.

Impact: Our new methodology enables biophysical simulations of fMRI based on inverting blood. Our findings may provide a deeper understanding of the hemodynamic origins of VASO and provide guidance for optimizing SS-SI VASO protocols to yield veridical representation of neural activity.

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