Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that results in progressive articular destruction and severe disability. Joint space narrowing (JSN) progression has been regarded as an important indicator for RA progression and has received sustained attention. In the diagnosis and monitoring of RA, radiology plays a crucial role to monitor joint space. A new framework for monitoring joint space by quantifying JSN progression through image registration in radiographic images has been developed. This framework offers the advantage of high accuracy, however, challenges do exist in reducing mismatches and improving reliability. In this work, a deep intra-subject rigid registration network is proposed to automatically quantify JSN progression in the early stage of RA. In our experiments, the mean-square error of Euclidean distance between moving and fixed image is 0.0031, standard deviation is 0.0661 mm, and the mismatching rate is 0.48\%. The proposed method has sub-pixel level accuracy, exceeding manual measurements by far, and is equipped with immune to noise, rotation, and scaling of joints. Moreover, this work provides loss visualization, which can aid radiologists and rheumatologists in assessing quantification reliability, with important implications for possible future clinical applications. As a result, we are optimistic that this proposed work will make a significant contribution to the automatic quantification of JSN progression in RA.

翻译：类风湿关节炎（RA）是一种慢性自身免疫性炎症疾病，会导致进行性关节破坏和严重功能障碍。关节间隙狭窄（JSN）进展已被视为RA进展的重要指标，并受到持续关注。在RA的诊断与监测中，放射学对评估关节间隙至关重要。我们开发了一种基于图像配准的放射影像JSN进展量化新框架。该框架具有高精度优势，但在减少误匹配和提高可靠性方面仍存在挑战。本研究提出了一种深度受试者内刚性配准网络，用于自动量化RA早期JSN进展。实验结果显示，移动图像与固定图像间的欧氏距离均方误差为0.0031，标准差为0.0661 mm，误匹配率为0.48%。该方法达到亚像素级精度，远超人工测量，且对关节噪声、旋转和缩放具有免疫性。此外，本工作提供了损失可视化功能，可辅助放射科医师和风湿科医师评估量化可靠性，这对未来临床应用具有重要意义。综上所述，我们乐观地认为，本项研究将为RA中JSN进展的自动量化作出重要贡献。