Subtle Radiographic Progression at Six Months Can Be Detected Using Automated Quantitative Software in Rheumatoid Arthritis While Receiving Tocilizumab.

OBJECTIVES: We investigated whether our in-house software equipped with partial image phase-only correlation (PIPOC) can detect subtle radiographic joint space narrowing (JSN) progression at six months and predict JSN progression in rheumatoid arthritis (RA) patients receiving Tocilizumab. METHODS: The study included 39 RA patients who were treated with Tocilizumab. Radiological progression of the metacarpophalangeal and the proximal interphalangeal joints was evaluated according to the Genant-modified Sharp score (GSS) at 0, 6, and 12 months. Automatic measurements were performed with the software. We validated the software in terms of accuracy in detecting the JSN progression. RESULTS: The success rate of the software for joint space width (JSW) measurement was 96.8% (449/464). The 0-12-month JSW change by the software was significantly greater in joints with the 0-6-month PIPOC (+) group than the 0-6-month PIPOC (-) group (p < 0.001). The 0-12-month JSW change by the software was 0-12-month GSS (+) than with 0-12-month GSS (-) (p = 0.02). Here, "(+)" indicates the JSN progression during the follow-up period. Meanwhile, "(-)" indicates no JSN progression during the follow-up period. Linear regression tests showed significant correlations between the 0-6-month and the 0-12-month PIPOC in the left 2nd and 3rd MCP joints (R2 = 0.554 and 0.420, respectively). CONCLUSIONS: Our in-house software equipped with PIPOC could predict subsequent JSN progression with only short-term observations.

Corrections to "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis".

Presents corrections to the article "A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis".

A deep registration method for accurate quantification of joint space narrowing progression in rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease that leads to progressive articular destruction and severe disability. Joint space narrowing (JSN) has been regarded as an important indicator for RA progression and has received significant attention. Radiology plays a crucial role in the diagnosis and monitoring of RA through the assessment of joint space. A new framework for monitoring joint space by quantifying joint space narrowing (JSN) progression through image registration in radiographic images has emerged as a promising research direction. This framework offers the advantage of high accuracy; however, challenges still exist in reducing mismatches and improving reliability. In this work, we utilize a deep intra-subject rigid registration network to automatically quantify JSN progression in the early stages of RA. In our experiments, the mean-square error of the Euclidean distance between the moving and fixed images was 0.0031, the standard deviation was 0.0661 mm and the mismatching rate was 0.48%. Our method achieves sub-pixel level accuracy, surpassing manual measurements significantly. The proposed method is robust to noise, rotation and scaling of joints. Moreover, it provides misalignment visualization, which can assist radiologists and rheumatologists in assessing the reliability of quantification, exhibiting potential for future clinical applications. As a result, we are optimistic that our proposed method will make a significant contribution to the automatic quantification of JSN progression in RA. Code is available at https://github.com/pokeblow/Deep-Registration-QJSN-Finger.git.

A Sub-Pixel Accurate Quantification of Joint Space Narrowing Progression in Rheumatoid Arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects peripheral synovial joints, like fingers, wrists and feet. Radiology plays a critical role in the diagnosis and monitoring of RA. Limited by the current spatial resolution of radiographic imaging, joint space narrowing (JSN) progression of RA for the same reason above can be less than one pixel per year with universal spatial resolution. Insensitive monitoring of JSN can hinder the radiologist/rheumatologist from making a proper and timely clinical judgment. In this paper, we propose a novel and sensitive method that we call partial image phase-only correlation which aims to automatically quantify JSN progression in the early RA. The majority of the current literature utilizes the mean error, root-mean-square deviation and standard deviation to report the accuracy at pixel level. Our work measures JSN progression between a baseline and its follow-up finger joint images by using the phase spectrum in the frequency domain. Using this study, the mean error can be reduced to 0.0130 mm when applied to phantom radiographs with ground truth, and 0.0519 mm standard deviation for clinical radiography. With the sub-pixel accuracy far beyond usual manual measurements, we are optimistic that the proposed work is a promising scheme for automatically quantifying JSN progression.

Fully automatic software for detecting radiographic joint space narrowing progression in rheumatoid arthritis: phantom study and comparison with visual assessment.

PURPOSE: We have developed an in-house software equipped with partial image phase-only correlation (PIPOC) which can automatically quantify radiographic joint space narrowing (JSN) progression. The purpose of this study was to evaluate the software in phantom and clinical assessments. MATERIALS AND METHODS: In the phantom assessment, the software's performance on radiographic images was compared to the joint space width (JSW) difference using a micrometer as ground truth. A phantom simulating a finger joint was scanned underwater. In the clinical assessment, 15 RA patients were included. The software measured the radiological progression of the finger joints between baseline and the 52nd week. The cases were also evaluated with the Genant-modified Sharp score (GSS), a conventional visual scoring method. We also quantitatively assessed these joints' synovial vascularity (SV) on power Doppler ultrasonography (0, 8, 20 and 52 weeks). RESULTS: In the phantom assessment, the PIPOC software could detect changes in JSN with a smallest detectable difference of 0.044 mm at 0.1 mm intervals. In the clinical assessment, the JSW change of the joints with GSS progression detected by the software was significantly greater than those without GSS progression (p = 0.004). The JSW change of joints with positive SV at baseline was significantly higher than those with negative SV (p = 0.024). CONCLUSION: Our in-house software equipped with PIPOC can automatically and quantitatively detect slight radiographic changes of JSW in clinically inactive RA patients.

Quantification of Joint Space Width Difference on Radiography Via Phase-Only Correlation (POC) Analysis: a Phantom Study Comparing with Various Tomographical Modalities Using Conventional Margin-Contouring.

Several visual scoring methods are currently used to assess progression of rheumatoid arthritis (RA) on radiography. However, they are limited by its subjectivity and insufficient sensitivity. We have developed an original measurement system which uses a technique called phase-only correlation (POC). The purpose of this study is to validate the system by using a phantom simulating the joint of RA patients.A micrometer measurement apparatus that can adjust arbitrary joint space width (JSW) in a phantom joint was developed to define true JSW. The phantom was scanned with radiography, 320 multi detector CT (MDCT), high-resolution peripheral quantitative CT (HR-pQCT), cone beam CT (CBCT), and tomosynthesis. The width was adjusted to the average size of a women's metacarpophalangeal joint, from 1.2 to 2.2 mm with increments of 0.1 mm and 0.01 mm. Radiographical images were analyzed by the POC-based system and manual method, and images from various tomographical modalities were measured via the automatic margin detection method. Correlation coefficients between true JSW difference and measured JSW difference were all strong at 0.1 mm intervals with radiography (POC-based system and manual method), CBCT, 320MDCT, HR-pQCT, and tomosynthesis. At 0.01 mm intervals, radiography (POC-based system), 320MDCT, and HR-pQCT had strong correlations, while radiography (manual method) and CBCT had low correlations, and tomosynthesis had no statistically significant correlation. The smallest detectable changes for radiography (POC-based system), radiography (manual method), 320MDCT, HR-pQCT, CBCT, and tomosynthesis were 0.020 mm, 0.041 mm, 0.076 mm, 0.077 mm, 0.057 mm, and 0.087 mm, respectively. We conclude that radiography analyzed with the POC-based system might sensitively detect minute joint space changes of the finger joint.