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.

评估将深度学习算法应用于0.55T与3.0T之间的双侧膝关节生物标志物量化的可行性，证明0.55T低场MRI可以用于评估膝关节软骨厚度和骨分割，尤其在使用已训练3.0T的深度学习算法时，其表现与3.0T几乎相当。

Technical Note: Feasibility of translating 3.0T-trained Deep-Learning
  Segmentation Models Out-of-the-Box on Low-Field MRI 0.55T Knee-MRI of Healthy
  Controls

3.0T训练的深度学习分割模型在0.55T健康控制者的低场MRI上的开箱即用可行性