Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps

Sudipto Dolui; Ze Wang; Ronald L Wolf; Ali Nabavizadeh; Long Xie; Duygu Tosun; Ilya M Nasrallah; David A Wolk; John A Detre; Alzheimer's Disease Neuroimaging Initiative

doi:10.1002/jmri.29308

Automated Quality Evaluation Index for Arterial Spin Labeling Derived Cerebral Blood Flow Maps

J Magn Reson Imaging. 2024 Feb 24. doi: 10.1002/jmri.29308. Online ahead of print.

Authors

Affiliations

¹ Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
² Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
³ Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
⁴ Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
⁵ Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
⁶ Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

PMID: 38400805
DOI: 10.1002/jmri.29308

Abstract

Background: Arterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality.

Purpose: To develop an automated and objective quality evaluation index (QEI) for ASL CBF maps.

Study type: Retrospective.

Population: Data from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury.

Field strength/sequence: Pulsed or pseudocontinuous ASL acquired at 3 T using non-background suppressed 2D gradient-echo echoplanar imaging or background suppressed 3D spiral spin-echo readouts.

Assessment: The QEI was developed using N = 101 2D CBF maps rated as unacceptable, poor, average, or excellent by two neuroradiologists and validated by 1) leave-one-out cross validation, 2) assessing if CBF reproducibility in N = 53 cognitively normal adults correlates inversely with QEI, 3) if iterative discarding of low QEI data improves the Cohen's d effect size for CBF differences between preclinical AD (N = 27) and controls (N = 53), 4) comparing the QEI with manual ratings for N = 50 3D CBF maps, and 5) comparing the QEI with another automated quality metric.

Statistical tests: Inter-rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant.

Results: The correlation between QEI and manual ratings (R = 0.83, CI: 0.76-0.88) was similar (P = 0.56) to inter-rater correlation (R = 0.81, CI: 0.73-0.87) for the 2D data. CBF reproducibility correlated negatively (R = -0.74, CI: -0.84 to -0.59) with QEI. The effect size comparing patients and controls improved (R = 0.72, CI: 0.59-0.82) as low QEI data was discarded iteratively. The correlation between QEI and manual ratings (R = 0.86, CI: 0.77-0.92) of 3D ASL was similar (P = 0.09) to inter-rater correlation (R = 0.78, CI: 0.64-0.87). The QEI correlated (R = 0.87, CI: 0.77-0.92) significantly better with manual ratings than did an existing approach (R = 0.54, CI: 0.30-0.72).

Data conclusion: Automated QEI performed similarly to manual ratings and can provide scalable ASL quality control.

Evidence level: 2 TECHNICAL EFFICACY: Stage 1.

Keywords: Alzheimer's Disease Neuroimaging Initiative; arterial spin labeling; automated quality evaluation; cerebral blood flow; data quality; preclinical Alzheimer's Disease.

Abstract

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