Deep learning-based denoising image reconstruction of body magnetic resonance imaging in children

作者：Vanda Počepcová, Michael Zellner, Fraser M. Callaghan, Xinzeng Wang, Maélène Lohézic, Julia Geiger, Christian J. Kellenberger · 发表于：Pediatric Radiology · 年份：2025 · DOI：10.1007/s00247-025-06230-5 · 被引用次数：12 · 研究领域：Advanced MRI Techniques and Applications、Atomic and Subatomic Physics Research、MRI in cancer diagnosis

BACKGROUND: Radial k-space sampling is widely employed in paediatric magnetic resonance imaging (MRI) to mitigate motion and aliasing artefacts. Artificial intelligence (AI)-based image reconstruction has been developed to enhance image quality and accelerate acquisition time. OBJECTIVE: To assess image quality of deep learning (DL)-based denoising image reconstruction of body MRI in children. MATERIALS AND METHODS: Children who underwent thoraco-abdominal MRI employing radial k-space filling technique (PROPELLER) with conventional and DL-based image reconstruction between April 2022 and January 2023 were eligible for this retrospective study. Only cases with previous MRI including comparable PROPELLER sequences with conventional image reconstruction were selected. Image quality was compared between DL-reconstructed axial T1-weighted and T2-weighted images and conventionally reconstructed images from the same PROPELLER acquisition. Quantitative image quality was assessed by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of the liver and spleen. Qualitative image quality was evaluated by three observers using a 4-point Likert scale and included presence of noise, motion artefact, depiction of peripheral lung vessels and subsegmental bronchi at the lung bases, sharpness of abdominal organ borders, and visibility of liver and spleen vessels. Image quality was compared with the Wilcoxon signed-rank test. Scan time length was compared to prior MRI obtained with conv...