Image Quality Assessment of Fetal Brain MRI Using Multi-Instance Deep Learning Methods.

backgroundDue to random motion of fetuses and maternal respirations, image quality of fetal brain MRIs varies considerably. To address this issue, visual inspection of the images is performed during acquisition phase and after 3D-reconstruction, and the images are re-acquired if they are deemed to be of insufficient quality. However, this process is time-consuming and subjective. Multi-instance (MI) deep learning methods (DLMs) may perform this task automatically. PURPOSE: To propose an MI count-based DLM (MI-CB-DLM), an MI vote-based DLM (MI-VB-DLM), and an MI feature-embedding DLM (MI-FE-DLM) for automatic assessment of 3D fetal-brain MR image quality. To quantify influence of fetal gestational age (GA) on DLM performance. STUDY TYPE: Retrospective. SUBJECTS: Two hundred and seventy-one MR exams from 211 fetuses (mean GA ± SD = 30.9 ± 5.5 weeks). FIELD STRENGTH/SEQUENCE: T ASSESSMENT: The T STATISTICAL TESTS: DLM performance was evaluated using precision, recall, F-score, accuracy, and AUC values.

resultsPrecision, recall, F-score, accuracy, and AUC averaged over the three cross validations were 0.85 ± 0.01, 0.85 ± 0.01, 0.85 ± 0.01, 0.85 ± 0.01, 0.93 ± 0.01, for MI-CB-DLM (without GA); 0.75 ± 0.03, 0.75 ± 0.03, 0.75 ± 0.03, 0.75 ± 0.03, 0.81 ± 0.03, for MI-VB-DLM (without GA); 0.81 ± 0.01, 0.81 ± 0.01, 0.81 ± 0.01, 0.81 ± 0.01, 0.89 ± 0.01, for MI-FE-DLM (without GA); and 0.86 ± 0.01, 0.86 ± 0.01, 0.86 ± 0.01, 0.86 ± 0.01, 0.93 ± 0.01, for MI-CB-DLM with GA. DATA

conclusionMI-CB-DLM performed better than other DLMs. Including GA as an input of MI-CB-DLM improved its performance. MI-CB-DLM may potentially be used to objectively and rapidly assess fetal MR image quality. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.