Advances in MRI hardware and acceleration strategies have enabled substantial reductions in musculoskeletal MRI acquisition times over the past decade. Advanced acceleration techniques have facilitated four- to eightfold acceleration but are often limited by noise amplification and reconstruction artifacts at higher acceleration factors. The clinical introduction of deep learning (DL)-based image reconstruction addresses traditional constraints by improving SNRs, reducing artifacts, and enhancing image quality, thereby enabling higher acceleration factors than previously achievable with conventional reconstruction methods. DL reconstruction and superresolution techniques allow comprehensive musculoskeletal MRI protocols to be performed in less than 10 minutes across a range of applications, field strengths, and vendors. Successful implementation requires consideration of hardware capabilities, anatomic constraints, protocol design, and workflow adaptation to fully realize efficiency gains. In addition to technical factors, operational considerations-including scheduling logistics and infrastructure adjustments-are important to translate scan time reductions into clinical value. Early validation studies show preserved or improved diagnostic performance of DL-accelerated MRI compared with conventional protocols, supporting their growing integration into clinical practice. Continued technical development and clinical validation will further define the role of DL reconstruction and potentially facilitate even greater acceleration and efficiency gains.
Vosshenrich et al. (Wed,) studied this question.