Objective: To improve on the reproducibility and sensitivity of the assessment of patients with rheumatoid arthritis (RA), two semi-automated measurement methods of the area of enhancing pannus (AEP), based on thresholding (AEP_THRES) and pixel-by-pixel timeintensity curve analysis (AEP_TIC), were evaluated as an alternative for the gold-standard manual contouring method (AEP_MANUAL). Methods: 8 patients (7 females and 1 male) with RA of the wrist or finger joints participated in the study. A threedimensional contrast-enhanced dynamic sequence was used at 3 T. After identifying the most relevant timeintensity curve (TIC) shape in terms of synovitis by comparing with the synovitis score using the RA-MRI scoring system, three different approaches for measuring the AEP were performed. Spearman's test of rank correlation was used to compare AEPs via two semi-automated methods (AEP_THRES and AEP_TIC) against manual segmentation (AEP_MANUAL) in the entire hand region as well as the wrist and the finger regions. Results: The TIC shape of "washout after fast initial enhancement" had excellent correlation with synovitis score (r 5 0.809). The correlation coefficient between AEP_TIC and AEP_MANUAL was evaluated to be better than that of AEP_THRES and AEP_MANUAL in the wrist region (AEP_THRES: r 5 0.716, AEP_TIC: r 5 0.815), whereas these were of comparable accuracy for the entire hand and the finger regions. Conclusion: This study suggests that TIC analysis may be an alternative to manual contouring for pannus quantification and provides important clinical information of the extent of the disease in patients with RA. Advances in knowledge: TIC shape analysis can be applied for new quantitative assessment for RA synovitis in the wrist.
We examined the capability of a gray-scale arterial spin labeling blood flow pattern variation (BFPV) map with two different post labeling delay (PLD) times to discriminate pannus in patients with rheumatoid arthritis (RA) at 3T. There was a statistically significant difference in the BFPV values between artery, pannus, and surrounding tissue. Furthermore, the color-coded BFPV map was able to accurately distinguish pannus from other tissues. These results suggest this approach may be capable of identifying pannus noninvasively.
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