Monte Carlo simulation of VASO fMRI from real microvascular angiograms of the mouse cortex

Poster No:


Submission Type:

Abstract Submission 


Élie Genois1,2, Louis Gagnon1,2, Michèle Desjardins1,2


1Université Laval, Québec, Québec, 2Centre de recherche du CHU de Québec - Université Laval, Québec, Canada

First Author:

Élie Genois  
Université Laval|Centre de recherche du CHU de Québec - Université Laval
Québec, Québec|Québec, Canada


Louis Gagnon  
Université Laval|Centre de recherche du CHU de Québec - Université Laval
Québec, Québec|Québec, Canada
Michèle Desjardins  
Université Laval|Centre de recherche du CHU de Québec - Université Laval
Québec, Québec|Québec, Canada


The vascular space occupancy (VASO) (1) is a functional magnetic resonance imaging (fMRI) technique for probing cerebral blood volume (CBV) changes non-invasively under various physiological states, including neural activation in humans. The VASO contrast originates from the decrease in the number of extravascular protons arising from blood vessel dilation during neural activation. An important consideration when implementing VASO is the intrinsic blood oxygen-level dependent (BOLD) effect, which offsets the signal (2). Assessing the microscopic physical origin of this BOLD contamination and the accuracy of correction methods would improve the quantification of CBV changes with VASO.
Given the spatial heterogeneity of microvascular architecture, the cerebral vascular geometry of the MRI voxel can influence both BOLD and VASO signals. To investigate this effect, three-dimensional high-resolution images of vasculature can be measured in rodents using optical microscopy. Here, we used detailed angiograms of rodent brain acquired with two-photon microscopy to model fMRI signals (VASO and BOLD) from first principles using Monte Carlo diffusion of water protons.


The current results are based on our previously published Monte-Carlo model (3) of extravascular protons diffusing in realistic vascular networks through magnetic field inhomogeneities. These inhomogeneities are caused by deoxyhemoglobin content changes during functional activation (i.e. the BOLD effect). In order to model the VASO signal, our previous model (3) was modified by normalizing the extravascular BOLD signal using the same number of protons in the baseline and in the activated state. The loss in signal due to the decrease in the number of extravascular protons in the activated state was interpreted as the VASO response. See Fig. 1 for a schematization of the biophysical origins of fMRI signals in our model.
The details of the processing of experimental two-photon microscopy images of vascular networks, as well as of the BOLD model implementation, can be found in our previous publication (3). Compared to our previous approach, the oxygen content was populated homogeneously for arterioles, capillaries and venules from tabulated experimental data, both for baseline and for the activated states (4). The volume change of each vascular compartment was also taken from the literature (5). This method allowed us to extend the model to the full field-of-view of the two-photon angiograms, and especially to include all of the large pial vessels that strongly contribute to the BOLD-VASO signals.
Supporting Image: Fig1.png
   ·Bio-physical origins of BOLD and VASO


In our model, the averaged initial loss in signal due to the VASO effect was -0.52 %. As TE increased, the BOLD effect, which is of opposite sign compared to the VASO signal, reduced the amplitude of the VASO signal. At 7T, 44% of the initial VASO signal was lost at TE = 6 ms for GRE and 5% for SE. This contamination significantly increased at higher TE and higher magnetic fields. Our results showed that correction schemes can account for the majority of this contamination and recover accurate relative signal changes when TE is as short as possible (see Fig. 2).
Our model also estimates the EV/IV contribution to the total BOLD signal. The fractional contribution of extravascular (EV) BOLD to total GRE BOLD signal change is on the order of 50% at 3T and 80% at 7T. This estimation is strongly dependent on assumed values of T2 and T2*, indicating the need for more experimental studies to quantify these values at high B0-fields.
Supporting Image: Fig2.png
   ·Modeled BOLD-corrected VASO signals


These results may provide useful information to optimize the pulse sequence timing in human VASO and BOLD fMRI, leading the way to a wider application of these fMRI techniques in healthy and diseased brain. With the development of fMRI at higher B0 fields, our results demonstrate that care must be taken when choosing sequence parameters to optimize quantification of CBV.

Modeling and Analysis Methods:

Activation (eg. BOLD task-fMRI)
Methods Development

Neuroinformatics and Data Sharing:

Informatics Other 2

Novel Imaging Acquisition Methods:


Physiology, Metabolism and Neurotransmission :

Cerebral Metabolism and Hemodynamics


Cerebral Blood Flow
Other - Cerebral Blood Volume; Vascular Space Occupancy (VASO), Blood Oxygen-Level Dependent (BOLD); Monte-Carlo modeling

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.


Please indicate below if your study was a "resting state" or "task-activation” study.


Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Not applicable

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Optical Imaging
Computational modeling

For human MRI, what field strength scanner do you use?


Provide references using author date format

1. Lu H . (2003), Functional magnetic resonance imaging based on changes in vascular space occupancy, Mag. Res. Med., vol. 50, no 2, pp. 263-274
2. Lu H. (2013), Non-invasive functional imaging of Cerebral Blood Volume with Vascular-Space-Occupancy (VASO) MRI. NMR biomed., vol. 26, no. 8, pp. 932-948.
3. Gagnon L. (2015), Quantifying the microvascular origin of BOLD-fMRI from first principles with two-photon microscopy and an oxygen-sensitive nanoprobe. J Neurosci., vol. 35, no 8, pp. 3663‑75.
4. Sakadžić S. (2014), Large arteriolar component of oxygen delivery implies a safe margin of oxygen supply to cerebral tissue. Nat Commun., vol. 5, pp. 5734.
5. Drew P.J.,(2001), Fluctuating and sensory-induced vasodynamics in rodent cortex extend arteriole capacity, vol. 108, no 20, pp. 8473-8478.