A Technique for Regional Analysis of Femorotibial Cartilage Thickness Based on Quantitative Magnetic Resonance Imaging

Abstract

The objective of this work was to develop a methodology for measuring cartilage thickness in anatomically based subregions in the tibial and in the central weight-bearing femoral cartilage from magnetic resonance (MR) images. The tibial plateau was divided into a central area of the total subchondral bone area (tAB), and anterior, posterior, internal, and external subregions surrounding it. In the weight-bearing femoral condyles, central, internal, and external subregions were determined. The Euclidean distance between the tAB and cartilage surface was used for determining cartilage thickness. The reproducibility of the method was evaluated on test-retest data sets of 12 participants (six healthy, six with osteoarthritis). The subregion size was varied systematically to study the influence on the reproducibility. The size of the subregions was highly consistent under conditions of repositioning (standard deviation 0.0%-0.3%). The precision errors for regional mean cartilage thickness measurements ranged from 19 microm (1.5%) to 84 microm (4.7%). The computation of regional cartilage thickness values from segmented MR images is shown to be highly reproducible and robust under conditions of joint repositioning. In longitudinal studies, this technique may substantially enhance the ability of quantitative MRI to monitor structural changes in osteoarthritis at narrow time intervals.

Patterns of Femoral Cartilage Thickness are Different in Asymptomatic and Osteoarthritic Knees and Can be Used to Detect Disease-Related Differences Between Samples

“…1). Several regions of interest have been proposed for mean thickness measurements [4,5,[7][8][9], and the method can be adapted to characterize the thickness shape in these regions rather than in the regions of thicker cartilage, as done in the present study.…”

“…For a long time, the best measure of cartilage thickness was the tibiofemoral joint space width derived from a knee radiograph. However, this technique is two-dimensional and does not allow for a direct visualization of the cartilage and fine-grained analyses of cartilage thickness [4]. More recently, magnetic resonance imaging (MRI) has replaced radiography, notably because it can visualize articular cartilage with high resolution and is noninvasive [5,6].…”

“…More recently, magnetic resonance imaging (MRI) has replaced radiography, notably because it can visualize articular cartilage with high resolution and is noninvasive [5,6]. In this case, cartilage thickness is usually analyzed by segmenting consecutive MR images to build a three-dimensional thickness map for the cartilage and then mean thickness measurements are performed on the map [4,5,[7][8][9]. While mean thickness measurements have provided important insights into the characteristics of knee OA [6,10], they suffer from a limited ability to detect OA-related differences; specifically, differences between asymptomatic knees and knees at early disease stages [6,11,12].…”

An efficient subset of morphological measures for articular cartilage in the healthy and diseased human knee

“…All morphologic measures mentioned previously were computed, and change (between baseline and follow-up) was measured in the four femorotibial cartilage plates. In order to determine the mean cartilage thickness (ThCtAB.Me and ThCcAB.Me), the mean of two distance transformations, one from the tAB to the AC and one from the AC to the tAB, was considered (28).…”

An Atlas-Based Approach to Study Morphological Differences in Human Femoral Cartilage Between Subjects from Incidence and Progression Cohorts: MRI Data from Osteoarthritis Initiative

“…Most quantitative studies divide the cartilage into a few compartments and report cartilage thickness (mean and standard deviation) for each compartment. Wirth et al reported a technique for regional analysis of femorotibial cartilage thickness based on quantitative MRI (Wirth et al, 2008). The latter paper uses an elegant algorithm driven identification of sub-regions with user-controlled parameters to define the sub-regions.…”