Regional and depth-dependence variations of cortical blood-flow assessed with high-resolution ASL

Poster No:

2326 

Submission Type:

Abstract Submission 

Authors:

Manuel Taso1, Fanny Munsch1, Li Zhao2, David Alsop1

Institutions:

1Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 2Children's National Medical Center, Washington, DC

First Author:

Manuel Taso  
Beth Israel Deaconess Medical Center, Harvard Medical School
Boston, MA

Co-Author(s):

Fanny Munsch  
Beth Israel Deaconess Medical Center, Harvard Medical School
Boston, MA
Li Zhao  
Children's National Medical Center
Washington, DC
David Alsop  
Beth Israel Deaconess Medical Center, Harvard Medical School
Boston, MA

Introduction:

Differences in blood flow between cortical regions and cortical layers may be an important indicator of local autoregulation, metabolic activity and neural signaling. Unfortunately, Arterial Spin Labeling (ASL) CBF images are typically acquired at a too low resolution to resolve the cortex and especially cortical layers. They also frequently suffer from blurring and distortion associated with fast imaging techniques. However, volumetric FSE sequences with sparse encoding and Compressed-Sensing have shown promise for high resolution perfusion imaging1. We therefore seek to push the limits of ASL resolution to collect high-resolution isotropic whole brain perfusion volumes to study the spatial distribution of cortical blood-flow.

Methods:

We implemented a background-suppressed PCASL preparation with a sparse variable-density 3D-FSE sequence2, with a 42 oversampled k-space center region followed by pseudo-random distribution of the outer k-space. We scanned 10 healthy volunteers at 3T (GE Discovery MR750) with a 32-ch head coil. We acquired 1mm3 T1-weighted volumes, followed by high-resolution single-delay ASL perfusion (1.5s labeling, 1.5 or 2s PLD, average B1=1.4uT, Gmax/Gav=3.5/0.5mT/m) at 1.7mm3 (TR/TE=6000/6-8ms, rBW=31kHz, ETL=120, Tacq=15-16min depending on the number of slice-encodes). After k-space filtering to reduce T2-blurring3, we performed a 4D L1-ESPIRiT4 Compressed-Sensing reconstruction using spatial wavelets and temporal total-variation for sparsity with the BART toolbox5 and absolute CBF using a single-compartment model. FreeSurfer cortical surface estimation and parcellation was performed on the T1-w volume, followed by boundary-based registration of ASL to the T1-w and group normalization to an average surface. The CBF was sampled at mid-distance between the pial and white surface followed by calculation of a mean CBF in 34 ROIs. Additionally, we sampled the CBF by 5% steps from the pial to white surface to evaluate its depth-dependence. We additionally created a high-resolution perfusion-weighted template using ANTs multi-channel (T1/ASL) template construction pipeline (SyN transform, 4 iterations, CC metric)6.

Results:

We successfully reconstructed high-resolution individual subject images and an average ASL template (Fig.1) that reveals interesting features such as definition of the choroid plexus and regional variations of the perfusion signal in the cortex with higher signal for example in the precentral gyrus and posterior cingulate. Secondly, when performing the surface-based analysis of the group-averaged CBF (Fig.2), we observed an interesting marked heterogeneity of CBF across the cortex, with higher flow observed in some associative regions such as the middle frontal and inferior parietal gyri but also in the precentral gyrus. Additionally, we observed a negative significant correlation between cortical thickness and CBF sampled at mid-distance (-0.46, p=0.005). Finally, when looking at the CBF distribution throughout the cortical thickness, we found an expected blood-flow increase from the white matter surface to the pial surface.
Supporting Image: fig2.jpg
   ·Figure 2 – (Top) Group-averaged surface projection of cortical thickness and CBF and (Bottom) regional and depth-dependence of CBF
Supporting Image: fig1.jpg
   ·Figure 1 – Individual (top) and group-averaged (bottom) ASL perfusion weighted-template
 

Conclusions:

Thanks to the combination between sparse variable-density FSE and CS reconstruction, we successfully collected high-resolution, high-quality whole brain perfusion data, showing good consistency with anatomy, hence providing an average perfusion-weighted template that could be used for spatial normalization of group studies involving ASL as it is aligned with the MNI T1-weighted template. Additionally, the group surface-based analysis highlighted a regional heterogeneity and depth-dependence of cortical flow. These results are encouraging as the blood-flow profile through the cortex seems to be similar to its known microvascular density distribution7 This technique may be useful for more detailed studies of the cerebral cortex functional architecture.

Modeling and Analysis Methods:

Methods Development

Neuroanatomy, Physiology, Metabolism and Neurotransmission:

Anatomy and Functional Systems

Neuroinformatics and Data Sharing:

Brain Atlases 2

Novel Imaging Acquisition Methods:

Imaging Methods Other

Physiology, Metabolism and Neurotransmission :

Cerebral Metabolism and Hemodynamics 1

Keywords:

Acquisition
Atlasing
Cerebral Blood Flow
Cortical Layers
MRI
NORMAL HUMAN

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.

Resting state

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

Healthy subjects

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.

Yes

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
Structural MRI

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

3.0T

Which processing packages did you use for your study?

Free Surfer
Other, Please list  -   ANTs

Provide references using author date format

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