Time-of-Flight-MRA-Derived-Probabilistic-Map of Each Major Cerebral Artery

Poster No:

1848 

Submission Type:

Abstract Submission 

Authors:

Samantha Cote1, Jean-Francois Lepage1, Kevin Whittingstall2

Institutions:

1Université de Sherbrooke, Sherbrooke, Quebec, 2Université de Sherbrooke, Sherbrooke, QC

First Author:

Samantha Cote  
Université de Sherbrooke
Sherbrooke, Quebec

Co-Author(s):

Jean-Francois Lepage, PhD  
Université de Sherbrooke
Sherbrooke, Quebec
Kevin Whittingstall, PhD  
Université de Sherbrooke
Sherbrooke, QC

Introduction:

The human brain is perfused by three main arteries: the middle cerebral arteries (MCA), anterior cerebral artery (ACA) and posterior cerebral arteries (PCA), each with distinct[1] and variable perfusion territories[2]. Attempts to parcellate the brain into functionally important regions have largely ignored these arteries; yet functional imaging methods are dependent on cerebral hemodynamics. Recent advancement in non-contrast enhanced Time-of-Flight (TOF) magnetic resonance angiography (MRA) allow non-invasive imaging of cerebral arteries and previous work from our lab has shown that this can be segmented into a whole-brain vascular tree[3], thus allowing for the isolation and study cerebral arteries with other MRI modalities. Here, we build on this by classifying tissue according to the cerebral artery's perfusion territory it resides in.

Methods:

Five healthy participants were imaged on a Phillips 3T Insignia scanner equipped with a 32-channel head-coil. MRI protocol consisted of a T1 weighted image; a whole-brain multi-band TOF image with a reconstructed resolution of 0.626x0.625x0.65mm to visualise the cerebral vascular tree; and a whole-brain arterial spin labelling (ASL) image to quantify cerebral blood flow (CBF). Each participant's vascular tree was segmented from TOF images and extracted to create a 3D image of the cerebral vascular tree and arterial diameters were computed (figure 1A)[3]. The arterial tree was warped to MNI[4,5] space and the region around each cerebral artery and collaterals were manually inspected and masked using FSLeyes[6–8] mask tool with a 10mm isotropic box (figure 1B). The masks for each artery was averaged to create a probabilistic map. Masks of 50%, 80% and 100% probability of each map were derived to examine the arterial diameters in regions of high and low probability. ASL images were quantified using Oxford ASL[9] by FSL and averaged in the gray (GM) and white matter (WM)[6–8] within the 50% , 80% and 100% probability masks. Then Broca's and Wernicke's area were compared using the probabilistic maps.

Results:

The probabilistic map of the ACA, MCA and PCA are depicted in figure1C-E. The MCA had the largest territory (35.54% of voxels), followed by the PCA (28.69%) and ACA (28.69%). The probability of tissue near an artery ranged from 20 to 100%. Figure 1G depicts the 50%, 80% and 100% probability map of the MCA. The diameters were larger in regions of high probability and decreased as probability decreased (figure 1G). GM-CBF remained stable between 50% and 80% and increased at 100% probability except for around the PCA (figure 1H). On average GM-CBF tended to be highest in the GM around the PCA (ACA: 43.82 ± 7.15ml/100g/min; MCA: 50.97 ± 7.45ml/100g/min; PCA: 63.30 ± 8.91ml/100g/min). WM-CBF was stable across regions of probability and highest in the WM around the PCA (ACA: 34.73 ± 5.94ml/100g/min; MCA: 47. 67 ± 6.72ml/100g/min; PCA: 56.71 ± 8.91ml/100g/min). Figure 2A shows the relative position of Broca's and Wernicke's area. Broca's area is in a region of higher probability of the MCA than Wernicke's (Figure 2B; Broca: 67.40 ± 10%; Wernicke's 41.75 ± 6.70%).
Supporting Image: Figure_1.png
Supporting Image: FIgure_2.png
 

Conclusions:

Here we propose a maps of the tissues surrounding the ACA, MCA and PCA derived from non-invasive TOF images, our maps fit within anatomical boundaries of post-mortem-derived arterial territories[1,2,10] The diameters of the 3 major cerebral arteries and collaterals were relatively similar; however, CBF was not. Notably, the ACA and PCA had an average CBF difference of about 20 ml/100g/min in the GM and WM respectively. Our analysis of Broca's and Wernicke's area show that the probability of an artery passing within each area differed by over 20%. To our knowledge, this is the first probabilistic map of each major cerebral artery. It can be used with other MRI modalities to gain insight on underlying vasculature, relationship to immediate and long-term neurological outcomes of cerebrovascular incidents and treatment of brain injuries.

Modeling and Analysis Methods:

Segmentation and Parcellation

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Anatomy and Functional Systems
Neuroanatomy Other 1

Novel Imaging Acquisition Methods:

Imaging Methods Other

Physiology, Metabolism and Neurotransmission :

Cerebral Metabolism and Hemodynamics 2

Keywords:

ANGIOGRAPHY
Atlasing
Cerebral Blood Flow
Language
MR ANGIOGRAPHY
Segmentation
STRUCTURAL MRI
Other - Brain Parcellation; Vascular Structure, Arterial Territory

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

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

Other

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.

Yes

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:

Functional MRI
Neurophysiology
Structural MRI

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

3.0T

Which processing packages did you use for your study?

AFNI
FSL

Provide references using author date format

1. Blumenfeld, H. (2010) Neuroanatomy Through Clinical Cases.
2. Zwan, A. Van Der & Hillen, B. (1991) Comments, Opinions, and Reviews Review of the Variability of the Territories of the Major Cerebral Arteries. 1078–1085.
3. Bernier, M., Cunnane, S. C. & Whittingstall, K. (2018). The morphology of the human cerebrovascular system. Human Brain Mapping 39, 4962–4975.
4. Fonov, V. et al. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54, 313–327.
5. Fonov, V., Evans, A., McKinstry, R., Almli, C. & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage 47, S102.
6. Woolrich, M. W. et al. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage 45, S173–S186.
7. Smith, S. M. et al. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 23, S208–S219.
8. Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. (2012). FSL. NeuroImage 62, 782–790.
9. Chappell, M. A., Groves, A. R., Whitcher, B. & Woolrich, M. W. (2009). Variational Bayesian Inference for a Nonlinear Forward Model. IEEE Transactions on Signal Processing 57, 223–236.
10. Tatu, L., Moulin, T., Vuillier, F. & Bogousslavsky, J. (1998). Arterial territories of the human brain. Frontiers of Neurology and Neuroscience 50, 1699–1708.