Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain

• Published in: Pediatric radiology Vol. 54; no. 8; pp. 1337 - 1343
• Main Authors: Nagaraj, Usha D., Dillman, Jonathan R., Tkach, Jean A., Greer, Joshua S., Leach, James L.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2024; Springer Nature B.V
• Subjects: Adolescent; Algorithms; Artificial Intelligence; Brain; Brain - diagnostic imaging; Brain Diseases - diagnostic imaging; Child; Child, Preschool; Communications systems; Diagnostic systems; Female; Humans; Image contrast; Image Interpretation, Computer-Assisted - methods; Image processing; Image quality; Image reconstruction; Imaging; Infant; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Materials recovery; Medical imaging; Medicine; Medicine & Public Health; Neuroimaging; Neuroradiology; Nuclear Medicine; Oncology; Original; Original Article; Patients; Pediatrics; Prospective Studies; Quantitative analysis; Radiology; Signal quality; Signal to noise ratio; Substantia alba; Substantia grisea; Ultrasound; Brain; Fluid-attenuated inversion recovery; Magnetic resonance imaging; Artificial intelligence; Child; Gray matter; White matter
• ISSN: 1432-1998; 0301-0449; 1432-1998
• DOI: 10.1007/s00247-024-05968-8

Background Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice. Objective To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging. Materials and methods This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios. Results AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p <0.001) respectively. Conclusion We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.