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Objectives: Tenosynovitis (inflammation of the synovial lining of the sheath surrounding tendons) is frequently observed on MRI of early arthritis patients. Since visual assessment of tenosynovitis is a laborious task, we investigated the feasibility of automatic quantification of tenosynovitis on MRI of the wrist in a large cohort of early arthritis patients., Methods: For 563 consecutive early arthritis patients (clinically confirmed arthritis ≥ 1 joint, symptoms < 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution three-dimensional image. Next, 10 extensor/flexor tendon regions were segmented using atlas-based segmentation and marker-based watershed. A measurement region of interest (ROI) was defined around the tendons. Finally, tenosynovitis was quantified by identifying image intensity values associated with tenosynovial inflammation using fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within the measurement ROI. A subset of 60 patients was used for training and the remaining 503 patients for validation. Correlation between quantitative measurements and visual scores was assessed through Pearson correlation coefficient., Results: Pearson correlation between quantitative measurements and visual scores across 503 patients was r = 0.90, p < 0.001. False detections due to blood vessels and synovitis present within the measurement ROI contributed to a median offset from zero equivalent to 13.8% of the largest measurement value., Conclusion: Quantitative measurement of tenosynovitis on MRI of the wrist is feasible and largely consistent with visual scores. Further improvements in segmentation and exclusion of false detections are warranted., Key Points: • Automatic measurement of tenosynovitis on MRI of the wrist is feasible and largely consistent with visual scores. • Blood vessels and synovitis in the vicinity of evaluated tendons can contribute to false detections in automatic measurements. • Further improvements in segmentation and exclusion of false detections are important directions of future work on the path to a robust quantification framework.

Objective: MRI-detected inflammation is considered of diagnostic value for rheumatoid arthritis (RA), but its evaluation involves a time-consuming scoring of 61 joint-level features. It is not clear, however, which of these features are specific for RA and whether evaluating a subset of specific features is sufficient to differentiate RA patients. This study aimed to identify a subset of RA-specific features in a case-control setting and validate them in a longitudinal cohort of arthralgia patients., Methods: The difference in frequency of MRI-detected inflammation (bone marrow edema, synovitis, and tenosynovitis) between 199 RA patients and 193 controls was studied in 61 features across the wrist, metacarpophalangeal, and metatarsophalangeal joints. A subset of RA-specific features was obtained by applying a cutoff on the frequency difference while maximizing discriminative performance. For validation, this subset was used to predict arthritis development in 225 clinically suspect arthralgia (CSA) patients. Diagnostic performance was compared to a reference method that uses the complete set of 61 features normalized for inflammation levels in age-matched controls., Results: Subset of 30 features, mainly (teno)synovitis, was obtained from the case-control setting. Validation in CSA patients yielded an area of 0.69 (95% CI: 0.59-0.78) under the ROC curve and a positive predictive value (PPV) of 31%, compared to 0.68 (95% CI: 0.60-0.77) and 29% PPV of the reference method with 61 features., Conclusion: Subset of 30 MRI-detected inflammatory features, dominated by (teno)synovitis, offers a considerable reduction of scoring efforts without compromising accuracy for prediction of arthritis development in CSA patients., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Purpose: To investigate the feasibility of automatic quantification of bone marrow edema (BME) on MRI of the wrist in patients with early arthritis., Methods: For 485 early arthritis patients (clinically confirmed arthritis of one or more joints, symptoms for less than 2 years), MR scans of the wrist were processed in three automatic stages. First, super-resolution reconstruction was applied to fuse coronal and axial scans into a single high-resolution 3D image. Next, the carpal bones were located and delineated using atlas-based segmentation. Finally, the extent of BME within each bone was quantified by identifying image intensity values characteristic of BME by fuzzy clustering and measuring the fraction of voxels with these characteristic intensities within each bone. Correlation with visual BME scores was assessed through Pearson correlation coefficient., Results: Pearson correlation between quantitative and visual BME scores across 485 patients was r=0.83, P<0.001., Conclusions: Quantitative measurement of BME on MRI of the wrist has the potential to provide a feasible alternative to visual scoring. Complete automation requires automatic detection and compensation of acquisition artifacts. Magn Reson Med 79:1127-1134, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made., (© 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine.)

Background: Evaluating inflammatory changes over time on MR images of the spine in patients with suspected axial Spondyloarthritis (axSpA) can be a labor-intensive task, requiring readers to manually search for and perceptually align a set of vertebrae between two scans. The purpose of this study was to assess the feasibility of computer-aided (CA) evaluation of such inflammatory changes in a framework where scans from two time points are fused into a single color-encoded image integrated into an interactive scoring tool., Methods: For 30 patients from the SPondyloArthritis Caught Early (SPACE) cohort (back pain ≥ 3 months, ≤ 2 years, onset < 45 years), baseline and follow-up MR scans acquired 9-12 months apart were fused into a single color-encoded image through locally-rigid image registration to evaluate inflammatory changes in 23 vertebral units (VUs). Scoring was performed by two expert readers on a (-2, 2) scale using an interactive scoring tool. For comparison of direction of change (increase/decrease) indicated by an existing reference, Berlin method scores ((-3, 3) scale) of the same MR scans from a different ongoing study were used. The distributions of VU-level differences between CA readers and between the CA and Berlin methods (sign of change scores) across patients were analyzed descriptively. Patient-level agreement between CA readers was assessed by intraclass correlation coefficient (ICC)., Results: Five patients were excluded from evaluation due to failed vertebrae segmentation. Patient-level inter-reader agreement ICC was 0.56 (95% CI: 0.22 to 0.78). Mean VU-level inter-reader differences across 25 patients ranged (-0.04, 0.12) with SD range (0, 0.45). Across all VUs, inter-reader differences ranged (-1, 1) in 573/575 VUs (99.7%). Mean VU-level inter-method differences across patients ranged (-0.04, 0.08) with SD range (0, 0.61). Across all VUs, inter-method differences ranged (-1, 1) in 572/575 VUs (99.5%)., Conclusions: Fusion of MR scans of the spine from two time points into a single color-encoded image allows for direct visualization and measurement of inflammatory changes over time in patients with suspected axSpA.