Image Quality Assessment of Fetal Brain MRI Using Multi‐Instance Deep Learning Methods. Issue 3 (23rd April 2021)

• Record Type: Journal Article
• Authors: Largent, Axel; Kapse, Kushal; Barnett, Scott D.; De Asis‐Cruz, Josepheen; Whitehead, Matthew; Murnick, Jonathan; Zhao, Li; Andersen, Nicole; Quistorff, Jessica; Lopez, Catherine; Limperopoulos, Catherine
• Is Part Of: Journal of magnetic resonance imaging. Volume 54:Issue 3(2021)
• Page Start: 818
• Page End: 829
• Publication Date: 2021-04-23
• Subjects: fetal brain MRI -- image quality assessment -- deep learning -- multi‐instance learning -- weakly supervised learning; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27649
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: Due 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: T2 ‐weighted single‐shot fast spin‐echo acquired at 1.5 T. Assessment: The T2 ‐weighted images were reconstructed in 3D. Then, two fetal neuroradiologists, a clinical neuroscientist, and a fetal MRI technician independently labeled the reconstructed images as 1 or 0 based on image quality (1 = high; 0 = low). These labels were fused and served as ground truth. The proposed DLMs were trained and evaluated using three repeated 10‐fold cross‐validations (training and validation sets of 244 and 27 scans). To quantify GA influence, this variable was included as an input of the DLMs. Statistical Tests: DLM performance was evaluated using precision, recall, F‐score, accuracy, and AUC values. Results: Precision, 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 Conclusion: MI‐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