The auxiliary diagnosis of femoroacetabular impingement (FAI) can be approached in two primary methods: one relying on motion analysis and dynamic test, and the other based solely on metrics derived from computed tomography (CT) images. The former method produces a single motion model but lacks quantitative analysis, while the latter depends on the doctor’s prior knowledge due to varying perspectives. In this paper, we present a novel and comprehensive 3D hip joint motion model. The term “comprehensive” refers to the integration of both quantitative and qualitative analyses. Specifically, we extract point clouds from CT images to reconstruct 3D models, calculating five key points and diagnostic indicators using the equations representing the hip joint point cloud. Additionally, we reconstruct hip joint motion based on motion sequences extracted from videos. Finally, we perform a statistical analysis comparing the quantitatively calculated indicators with the doctor’s measured results. To better assist in diagnosis, we visualize the 3D hip joint motion model, further demonstrating the comprehensiveness of our approach.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Video-Driven Comprehensive 3D Hip Joint Motion Model for FAI Auxiliary Diagnosis

  • Yaxin Zhang,
  • Xiaodong Ju,
  • Shuting Chang,
  • Yijian Wen,
  • Peng Du,
  • Zhongke Wu,
  • Xingce Wang

摘要

The auxiliary diagnosis of femoroacetabular impingement (FAI) can be approached in two primary methods: one relying on motion analysis and dynamic test, and the other based solely on metrics derived from computed tomography (CT) images. The former method produces a single motion model but lacks quantitative analysis, while the latter depends on the doctor’s prior knowledge due to varying perspectives. In this paper, we present a novel and comprehensive 3D hip joint motion model. The term “comprehensive” refers to the integration of both quantitative and qualitative analyses. Specifically, we extract point clouds from CT images to reconstruct 3D models, calculating five key points and diagnostic indicators using the equations representing the hip joint point cloud. Additionally, we reconstruct hip joint motion based on motion sequences extracted from videos. Finally, we perform a statistical analysis comparing the quantitatively calculated indicators with the doctor’s measured results. To better assist in diagnosis, we visualize the 3D hip joint motion model, further demonstrating the comprehensiveness of our approach.