MRI深度学习图像重建技术在肌骨系统疾病诊断的应用进展

Application and research progress of MRI deep learning image reconstruction technology in clinical diagnosis of musculoskeletal system diseases

深度学习图像重建(deep learning-based reconstruction,DLR)技术是目前MRI图像重建领域最为前沿的技术进展之一。相对于常规MRI图像重建技术而言,DLR技术重新定义了MRI的信噪比、空间分辨率和扫描时间之间新的边界,其突出的技术优势是有效去除图像噪声及伪影,大幅缩短扫描时间,且在提高病灶的检出率和定性准确率方面也具有潜在优势。随着算法的不断优化和模型泛化性的提升,DLR目前已被广泛应用于神经系统、肌骨系统及心脏等多部位的MRI检查,其适用的扫描序列及临床应用场景也在不断拓展。DLR技术在维持原有空间分辨率条件下,通过减少信号采集次数联合增加并行采集加速因子,将成像时间缩短50%以上,实现肌骨系统快速成像,且所获得图像质量明显优于传统重建图像。目前,DLR在膝关节、肩关节、手腕关节及脊柱等肌骨系统的MRI检查中被广泛应用,并证实了其在缩短成像时间、提升图像信噪比和提高分辨率方面具有卓越表现。

Deep learning based reconstruction(DLR)technology is currently one of the most cutting⁃edge techno⁃logical advancements in the field of MRI image reconstruction.Compared to conventional MRI image reconstruction tech⁃niques,DLR technology redefines a new boundary between signal⁃to⁃noise ratio,spatial resolution,and scanning time on MRI.Its outstanding technical advantage is the effective removal of image noise and artifacts,significantly reducing scan⁃ning time,and also has potential advantages in improving the detection rate and accuracy qualitative diagnosis of lesions.With the continuous optimization of algorithms and the improvement of model generalization,DLR has been widely used in MRI examinations for multiple parts,such as the nervous system,musculoskeletal system,and heart.Its applicable scan⁃ning sequences and clinical application scenarios are also constantly expanding.DLR technology,while maintaining the original spatial resolution,reduces the number of signal acquisition times and increases the parallel acquisition accelera⁃tion factor to shorten the imaging time by more than 50%,achieving rapid imaging of the musculoskeletal system,and ob⁃taining significantly better image quality than traditional reconstructed images.Currently,DLR is widely used in MRI exa⁃minations of musculoskeletal systems,such as the knee,shoulder,wrist,and spine,and has demonstrated its outstanding performance in shortening imaging time,improving image signal⁃to⁃noise ratio and resolution.