As Simple as Height: Estimating Labelling Efficiency in ASL MRI for CBF Quantification
Poster No:
1496
Submission Type:
Abstract Submission
Authors:
Lise Klaksvik1, Joana Pinto1, Daniel Bulte1
Institutions:
1University of Oxford, Oxford, United Kingdom
First Author:
Co-Author(s):
Introduction:
Arterial spin labelling (ASL) is an MR perfusion technique that allows quantification of cerebral blood flow (CBF) non-invasively. To obtain CBF in ml/100g/min it is necessary to scale it by labelling efficiency. Although the current recommendation is to use a constant value based on the ASL sequence used (0.85 for pCASL) (Alsop et al., 2015), the true labelling efficiency is dependent on blood velocity in the labelling plane, which has been shown to vary with age (Zócalo & Bia, 2021) and pathology (Kelly et al., 2013). In studies with altered blood flow dynamics, such as hypercapnia, this is known to underestimate CBF change. An acquisition-specific parameter estimate would address this inter- and intrasubject variability (Aslan et al., 2010), however, this requires acquisition and manual labelling of a phase-contrast image. With this work, we propose and validate a simple linear model to estimate ICA velocity and labelling efficiency based on ASL imaging and physiological features.
Methods:
Multiple post labelling delay (multi-PLD) pCASL data (6 PLDs 250-1500ms, labelling duration 1400ms, 3.5x3.5x4.5mm3) from 23 female subjects (32.48±5.38 years old) was acquired on a 3T Siemens Prisma scanner during two conditions, normocapnia (6 min) and hypercapnia (5% CO2, 3 min). 3 subjects were excluded based on noisy/artefactual data. ASL data were analysed with FSL tools (Chappell et al., 2023), yielding maps of CBF and arterial arrival time (AAT). Structural images were segmented to retrieve grey matter (GM) masks. Regions of interest (ROI) of the MCA-fed parietal region were created by combining the GM mask with the corresponding arterial atlas territory (Liu et al., 2023). Right- and left side ICA velocities were calculated from the phase contrast data and labelling efficiencies were estimated using a sequence-dependent model (Okell, 2011).
A linear regression model to predict ICA velocity was fitted to 14 subjects and tested on the remaining 6. Estimates were obtained for left and right hemispheres separately. Features considered were mean AAT in the ROI, condition, spatial (brain length, volume), physiological (height, weight, blood pressure) and demographic measures (age). Features were selected based on statistical significance (p<0.05) and multicollinearity.
A linear regression model to predict ICA velocity was fitted to 14 subjects and tested on the remaining 6. Estimates were obtained for left and right hemispheres separately. Features considered were mean AAT in the ROI, condition, spatial (brain length, volume), physiological (height, weight, blood pressure) and demographic measures (age). Features were selected based on statistical significance (p<0.05) and multicollinearity.
Results:
A linear model with condition, height, anteroposterior brain length and mean AAT resulted in an R²-score of 0.73 and a cross-validated R²-score of 0.60, with normalised coefficients of respectively 8.17, 3.48, 2.86 and -2.48. Including a non-linear relationship between mean AAT and ICA velocity did not improve the fit. Excluding height reduced the R²-score to 0.49. All other features were not significant.
The ICA velocity based on phase-contrast was 11.70±6.00% higher in hypercapnia than normocapnia, resulting in mean labelling efficiencies of 0.90 and 0.94, respectively (Fig. 1). Compared to the constant labelling efficiency, the model that included height is closer to the ground truth in 91.67% of cases, whereas the model without height only in 79.17% of cases.
The constant labelling efficiency consistently overestimated CBF (95.45%) and underestimated the CBF difference between conditions by 18.98%. The estimates of the model with height yielded an average error in CBF difference of 9.40%, while the model without height had an average error of 11.11% (Fig. 2).
The ICA velocity based on phase-contrast was 11.70±6.00% higher in hypercapnia than normocapnia, resulting in mean labelling efficiencies of 0.90 and 0.94, respectively (Fig. 1). Compared to the constant labelling efficiency, the model that included height is closer to the ground truth in 91.67% of cases, whereas the model without height only in 79.17% of cases.
The constant labelling efficiency consistently overestimated CBF (95.45%) and underestimated the CBF difference between conditions by 18.98%. The estimates of the model with height yielded an average error in CBF difference of 9.40%, while the model without height had an average error of 11.11% (Fig. 2).
·Figure 1: Labelling efficiency in left (circle) and right (triangle) hemispheres in both conditions (normocapnia, blue; hypercapnia, red). White paper constant (0.85) is depicted with a dashed line.
·Figure 2: CBF estimates by subject and hemisphere for phase contrast (PC, blue), white paper constant (0.85, grey) and model with height (red). Normocapnia in solid colour, hypercapnia in opaque.
Conclusions:
This work highlights the impact of labelling efficiency estimation when quantifying CBF with ASL, particularly in conditions with altered blood flow dynamics. The proposed linear regression model to predict ICA velocity on subject- and hemisphere specific metrics outperformed the constant labelling efficiency in most cases. Height was a strong predictor of ICA velocity.
The model allows for quantification of labelling efficiency without the need to acquire phase-contrast data and could be incorporated into ASL analysis software. Future work should extend these methods to larger and more diverse data.
The model allows for quantification of labelling efficiency without the need to acquire phase-contrast data and could be incorporated into ASL analysis software. Future work should extend these methods to larger and more diverse data.
Modeling and Analysis Methods:
Classification and Predictive Modeling
Methods Development 1
Novel Imaging Acquisition Methods:
Non-BOLD fMRI
Physiology, Metabolism and Neurotransmission:
Cerebral Metabolism and Hemodynamics 2
Keywords:
Blood
Cerebral Blood Flow
Data analysis
Design and Analysis
FUNCTIONAL MRI
Modeling
MRI
Other - ASL
1|2Indicates the priority used for review
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Provide references using APA citation style.
Alsop, D. C. et al. (2015). Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: A consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia. Magnetic Resonance in Medicine, 73(1), 102-116. https://doi.org/10.1002/mrm.25197
Aslan, S. et al. (2010). Estimation of labeling efficiency in pseudocontinuous arterial spin labeling. Magnetic Resonance in Medicine, 63(3), 765-771. https://doi.org/10.1002/mrm.22245
Chappell, M. et al. (2023). BASIL: A toolbox for perfusion quantification using arterial spin labelling. Imaging Neuroscience, 1, 1-16. https://doi.org/10.1162/imag_a_00041
Kelly, M. et al. (2013). Pseudo-Continuous Arterial Spin Labelling MRI for Non-Invasive, Whole-Brain, Serial Quantification of Cerebral Blood Flow Following Aneurysmal Subarachnoid Haemorrhage. Translational Stroke Research, 4(6), 710-718. https://doi.org/10.1007/s12975-013-0269-y
Liu, C.-F. et al. (2023). Digital 3D Brain MRI Arterial Territories Atlas. Scientific Data, 10(1), 74. https://doi.org/10.1038/s41597-022-01923-0
Okell, T. W. (2011). Assessment of collateral blood flow in the brain using magnetic resonance imaging [Doctoral dissertation, Oxford University]. Oxford University Research Archive.
Zócalo, Y., & Bia, D. (2021). Sex- and Age-Related Physiological Profiles for Brachial, Vertebral, Carotid, and Femoral Arteries Blood Flow Velocity Parameters During Growth and Aging (4–76 Years): Comparison With Clinical Cut-Off Levels [Original Research]. Frontiers in Physiology, 12. https://doi.org/10.3389/fphys.2021.729309