To study the articular cartilage surface curvature determined automatically from magnetic resonance (MR) knee scans, evaluate accuracy of the curvature estimates on digital phantoms, and an evaluation of their potential as disease markers for different stages of osteoarthritis (OA).
Knee MR data were acquired using a low-field 0.18T scanner, along with posteroanterior x-rays for evaluation of radiographic signs of OA according to the Kellgren-Lawrence index (KL). Scans from a total of 114 knees from test subjects with KL 0-3, 59% females, ages 21-79 years were evaluated. The surface curvature for the medial tibial compartment was estimated automatically on a range of scales by two different methods: Euclidean shortening flow and boundary normal comparison on a cartilage shape model. The curvature estimates were normalized for joint size for intersubject comparisons. Digital phantoms were created to establish the accuracy of the curvature estimation methods.
A comparison of the two curvature estimation methods to ground truth yielded absolute pairwise differences of 1.1%, and 4.8%, respectively. The interscan reproducibility for the two methods were 2.3% and 6.4% (mean coefficient of variation), respectively. The surface curvature was significantly higher in the OA population (KL > 0) compared with the healthy population (KLi = 0) for both curvature estimates, with P values of .000004 and .000006, respectively. The shape model based curvature estimate could also separate healthy from borderline OA (KL = 1) populations (P = .005).
The phantom study showed that the shape model method was more accurate for a coarse-scale analysis, whereas the shortening flow estimated fine scales better. Both the fine- and the coarse-scale curvature estimates distinguished between healthy and OA populations, and the coarse-scale curvature could even distinguish between healthy and borderline OA populations. The highly significant differences between populations demonstrate the potential of cartilage curvature as a disease marker for OA.