Abstract #1999

Image quality assessment model for 3D ASL in children

Ruoge Lin¹, Qing Yang², Sudipto Dolui³, Yu Zeng¹, Yong He⁴, Hongxi Zhang², and Li Zhao¹

¹College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ²Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China, ³Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States, ⁴State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

Synopsis

Keywords: Arterial Spin Labelling, Arterial spin labelling

Motivation: Arterial spin labeling (ASL) is vulnerable to motion and off-resonance, which may result in unstable image quality, particularly in children.

Goal(s): To propose an automatic image quality assessment model for 3D ASL in children.

Approach: The proposed model was trained and validated on 51 3D ASL scans from children, and was compared to a previously developed reference method. The performance was evaluated using AUC and 5-fold cross-validation tests.

Results: The proposed model yielded 8%-11% higher AUC, accuracy, and F1 score compared to the reference method.

Impact: The proposed model for assessing image quality in children's ASL may offer a valuable tool for clinics and studies on brain development with a large cohort.

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