HallerSHedderichDFederauC, et al. The current status of AI-accelerated MRI techniques in clinical use. Radiology. 2025;317(2):e243819.
2.
RajamohanNBaggaBBansalBGinocchioLGuptaAChandaranaH.Deep learning-accelerated MRI in body and chest. J Comput Assist Tomogr. 2025;49(4):531-544.
3.
WangNLiuMWangW, et al. Accelerated reduced field of view T2-weighted imaging of pancreatobiliary disorders using deep learning-based reconstruction: reduction of acquisition time and improvement of image quality. Can Assoc Radiol J. Published online 2026. doi: 10.1177/08465371251407889.
4.
WaryPHossuGAmbarkiK, et al. Deep learning HASTE sequence compared with T2-weighted BLADE sequence for liver MRI at 3 Tesla: a qualitative and quantitative prospective study. Eur Radiol. 2023;33(10):6817-6827.
5.
MuléSKharratRZerbibP, et al. Fast T2-weighted liver MRI: image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence. Diagn Interv Imaging. 2022;103(10):479-485.
6.
ZhouXWuYQinY, et al. Thin-slice T2-weighted images and deep-learning-based super-resolution reconstruction: improved preoperative assessment of vascular invasion for pancreatic ductal adenocarcinoma. Insights Imaging. 2025;16(1):144.