Affiliation:
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
Abstract
BackgroundArterial spin labeling (ASL) derived cerebral blood flow (CBF) maps are prone to artifacts and noise that can degrade image quality.PurposeTo develop an automated and objective quality evaluation index (QEI) for ASL CBF maps.Study TypeRetrospective.PopulationData from N = 221 adults, including patients with Alzheimer's disease (AD), Parkinson's disease, and traumatic brain injury.Field Strength/SequencePulsed or pseudocontinuous ASL acquired at 3 T using non‐background suppressed 2D gradient‐echo echoplanar imaging or background suppressed 3D spiral spin‐echo readouts.AssessmentThe 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 TestsInter‐rater reliability and manual vs. automated QEI were quantified using Pearson's correlation. P < 0.05 was considered significant.ResultsThe 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 ConclusionAutomated QEI performed similarly to manual ratings and can provide scalable ASL quality control.Evidence Level2Technical EfficacyStage 1
Funder
National Institutes of Health