Vosshenrich Jan, Fritz Jan
Department of Radiology, Grossman School of Medicine, New York University, 660 First Avenue, 10016, New York, NY, USA.
Klinik für Radiologie und Nuklearmedizin, Universitätsspital Basel, Petersgraben 4, 4031, Basel, Schweiz.
Radiologie (Heidelb). 2024 Oct;64(10):758-765. doi: 10.1007/s00117-024-01325-w. Epub 2024 Jun 12.
CLINICAL/METHODICAL ISSUE: Magnetic resonance imaging (MRI) is a central component of musculoskeletal imaging. However, long image acquisition times can pose practical barriers in clinical practice.
MRI is the established modality of choice in the diagnostic workup of injuries and diseases of the musculoskeletal system due to its high spatial resolution, excellent signal-to-noise ratio (SNR), and unparalleled soft tissue contrast.
Continuous advances in hardware and software technology over the last few decades have enabled four-fold acceleration of 2D turbo-spin-echo (TSE) without compromising image quality or diagnostic performance. The recent clinical introduction of deep learning (DL)-based image reconstruction algorithms helps to minimize further the interdependency between SNR, spatial resolution and image acquisition time and allows the use of higher acceleration factors.
The combined use of advanced acceleration techniques and DL-based image reconstruction holds enormous potential to maximize efficiency, patient comfort, access, and value of musculoskeletal MRI while maintaining excellent diagnostic accuracy.
Accelerated MRI with DL-based image reconstruction has rapidly found its way into clinical practice and proven to be of added value. Furthermore, recent investigations suggest that the potential of this technology does not yet appear to be fully harvested.
Deep learning-reconstructed fast musculoskeletal MRI examinations can be reliably used for diagnostic work-up and follow-up of musculoskeletal pathologies in clinical practice.
临床/方法学问题:磁共振成像(MRI)是肌肉骨骼成像的核心组成部分。然而,较长的图像采集时间可能在临床实践中构成实际障碍。
由于其高空间分辨率、出色的信噪比(SNR)和无与伦比的软组织对比度,MRI是肌肉骨骼系统损伤和疾病诊断检查中既定的首选方式。
在过去几十年中,硬件和软件技术的不断进步使得二维 turbo-spin-echo(TSE)能够在不影响图像质量或诊断性能的情况下实现四倍加速。最近基于深度学习(DL)的图像重建算法在临床上的引入有助于进一步最小化SNR、空间分辨率和图像采集时间之间的相互依赖性,并允许使用更高的加速因子。
先进的加速技术与基于DL的图像重建相结合,在保持出色诊断准确性的同时,具有极大的潜力可最大化肌肉骨骼MRI的效率、患者舒适度、可及性和价值。
基于DL图像重建的加速MRI已迅速进入临床实践,并被证明具有附加价值。此外,最近的研究表明,这项技术的潜力似乎尚未得到充分挖掘。
深度学习重建的快速肌肉骨骼MRI检查在临床实践中可可靠地用于肌肉骨骼疾病的诊断检查和随访。