In the current study, our purpose is to evaluate the feasibility of applying deep learning (DL) enabled algorithms to quantify bilateral knee biomarkers in healthy controls scanned at 0.55T and compared with 3.0T. The current study assesses the performance of standard in-practice bone, and cartilage segmentation algorithms at 0.55T, both qualitatively and quantitatively, in terms of comparing segmentation performance, areas of improvement, and compartment-wise cartilage thickness values between 0.55T vs. 3.0T. Initial results demonstrate a usable to good technical feasibility of translating existing quantitative deep-learning-based image segmentation techniques, trained on 3.0T, out of 0.55T for knee MRI, in a multi-vendor acquisition environment. Especially in terms of segmenting cartilage compartments, the models perform almost equivalent to 3.0T in terms of Likert ranking. The 0.55T low-field sustainable and easy-to-install MRI, as demonstrated, thus, can be utilized for evaluating knee cartilage thickness and bone segmentations aided by established DL algorithms trained at higher-field strengths out-of-the-box initially. This could be utilized at the far-spread point-of-care locations with a lack of radiologists available to manually segment low-field images, at least till a decent base of low-field data pool is collated. With further fine-tuning with manual labeling of low-field data or utilizing synthesized higher SNR images from low-field images, OA biomarker quantification performance is potentially guaranteed to be further improved.

翻译：本研究旨在评估将深度学习（DL）算法应用于0.55T场强下健康对照组双侧膝关节生物标志物定量分析的可行性，并与3.0T场强结果进行比对。通过定性与定量相结合的方式，本研究系统评估了现有骨骼与软骨分割算法在0.55T场强下的表现，具体包括：分割性能对比、改进空间分析以及不同场强（0.55T vs 3.0T）间软骨分区厚度值的比较。初步结果表明，在多厂商采集环境下，将基于3.0T数据训练的现有定量深度学习图像分割技术直接迁移至0.55T膝关节MRI具有"可用至良好"的技术可行性。特别是在软骨分区分割任务中，基于Likert评级的模型表现与3.0T几乎相当。本研究证实：0.55T低场强可持续且易部署的MRI系统，可借助已在高场强数据上训练的成熟DL算法，在无需额外适配条件下直接用于评估膝关节软骨厚度与骨骼分割。该方案适用于缺乏放射科医师进行低场强图像手动分割的广泛基层医疗点，至少在积累足够数量的低场强数据池之前具有重要应用价值。未来通过低场强数据人工标注的微调，或利用低场强图像生成高信噪比合成图像的策略，骨关节炎（OA）生物标志物的量化性能有望得到进一步优化。