Erosion Identification in Metacarpophalangeal Joints in Rheumatoid Arthritis using High-Resolution Peripheral Quantitative Computed Tomography

作者： Yousif Al-Khoury1,2, Stephanie Finzel3, Camille Figueiredo4, Andrew J. Burghardt5, Kathryn S. Stok6, Lai-Shan Tam7, Isaac Cheng7, Justin J. Tse1, Sarah L. Manske1 1Department of Radiology, McCaig Institute for Bone and Joint Health,University of Calgary, 2Department of Biomedical Engineering,University of Calgary, 3University Medical Center Freiburg, 4Department of Rheumatology,University of São Paulo, 5Department of Radiology and Biomedical Imaging,University of California, San Francisco, 6Department of Biomedical Engineering,University of Melbourne, 7Department of Medicine and Therapeutics,The Chinese University of Hong Kong

简介：

Overview

This article discusses the development of a training tool for identifying pathological cortical breaks in high resolution peripheral quantitative computed tomography (HRPQCT) images related to rheumatoid arthritis. The tool aims to enhance the efficiency of erosion analysis in clinical settings.

Key Study Components

Area of Science

• Rheumatology
• Medical Imaging
• Bone Analysis

Background

• Bone erosions are a significant pathological feature of rheumatoid arthritis.
• HRPQCT is more sensitive than standard X-rays for detecting bone erosions.
• Accurate identification of erosions is challenging and time-consuming.
• Expert rheumatologists typically perform the analysis.

Purpose of Study

• To introduce a new open-source software module for analyzing bone erosions.
• To provide guidance for users in identifying erosions in HRPQCT images.
• To improve the efficiency of investigating treatment effects on bone damage.

Methods Used

• Development of a training module within the 3D Slicer software.
• Step-by-step instructions for using the module to analyze HRPQCT images.
• Utilization of seed points for identifying erosion sites.
• Statistical analysis of erosion volumes and generation of output data.

Main Results

• The module provides accurate feedback on seed point placement.
• It can identify cysts and errors in seed point placements.
• Results are comparable to established methods of erosion analysis.
• Facilitates faster and more efficient analysis of bone erosions.

Conclusions

• The new software module enhances the identification of bone erosions.
• It allows for quicker analysis compared to traditional methods.
• Implementation can lead to better understanding of treatment impacts on bone health.

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