UCSF

Background Imaging of structural disease in osteoarthritis has traditionally relied on MRI and radiography. Joint space mapping (JSM) can be used to quantitatively map joint space width (JSW) in three dimensions from CT images. Purpose To demonstrate the reproducibility, repeatability, and feasibility of JSM of the knee using weight-bearing CT images. Materials and Methods Two convenience samples of weight-bearing CT images of left and right knees with radiographic Kellgren-Lawrence grades (KLGs) less than or equal to 2 were acquired from 2014 to 2018 and were analyzed retrospectively with JSM to deliver three-dimensional JSW maps. For reproducibility, images of three sets of knees were used for novice training, and then the JSM output was compared against an expert's assessment. JSM was also performed on 2-week follow-up images in the second cohort, yielding three-dimensional JSW difference maps for repeatability. Statistical parametric mapping was performed on all knee imaging data (KLG, 0-4) to show the feasibility of a surface-based analysis in three dimensions. Results Reproducibility (in 20 individuals; mean age, 58 years ± 7 [standard deviation]; mean body mass index, 28 kg/m² ± 6; 14 women) and repeatability (in nine individuals; mean age, 53 years ± 6; mean body mass index, 26 kg/m² ± 4; seven women) reached their lowest performance at a smallest detectable difference less than ±0.1 mm in the central medial tibiofemoral joint space for individuals without radiographically demonstrated disease. The average root mean square coefficient of variation was less than 5% across all groups. Statistical parametric mapping (33 individuals; mean age, 57 years ± 7; mean body mass index, 27 kg/m² ± 6; 23 women) showed that the central-to-posterior medial joint space was significantly narrower by 0.5 mm for each incremental increase in the KLG (threshold P < .05). One knee (KLG, 2) demonstrated a baseline versus 24-month change in its three-dimensional JSW distribution that was beyond the smallest detectable difference across the lateral joint space. Conclusion Joint space mapping of the knee using weight-bearing CT images is feasible, demonstrating a relationship between the three-dimensional joint space width distribution and structural joint disease. It is reliably learned by novice users, can be personalized for disease phenotypes, and can be used to achieve a smallest detectable difference that is at least 50% smaller than that reported to be achieved at the highest performance level in radiography. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Roemer in this issue.