Determination of knee joint cartilage thickness using three‐dimensional magnetic resonance chondro‐crassometry (3D MR‐CCM)

Abstract

The objective of this article was to analyze the accuracy and precision with which the quantitative distribution of articular cartilage can be determined in the knee joint using MRI. A three-dimensional (3D) technique that accounts for the out-of-plane deviation of the interface normal in strongly curved joint surfaces (3D MR-CCM) has been developed for cartilage thickness measurements. Eight cadaveric knee-joint specimens and six volunteers were imaged using a fat-suppressed gradient-echo sequence at a resolution of 2 x 0.31 x 0.31 mm3. Cartilage volumes and topographical thickness maps were obtained and compared with those derived from anatomical sections by image analysis. The deviation of the MR volumes from those of the sections was 1-12%, the coefficient of variation after repositioning ranged from 2.9% (patella) to 8.2% (lateral tibial plateau). Between 60% and 80% of all image points could be attributed to identical thickness intervals, less than 20% deviating by more than 0.5 mm. The intraobserver and interobserver reproducibilities were very high in both the specimens and the volunteers. In the knee joint, 3D reconstructions of the cartilages, and measurements that take into account the out-of-plane deviation of the interface normals (3D MR-CCM), are required.

Correlation of knee‐joint cartilage morphology with muscle cross‐sectional areas vs. anthropometric variables

“…In the present study only the right knee joint was investigated, based on the observations that side differences in cartilage morphology are small (0 -6% for the various parameters) and that side dominance of the limbs does not significantly influence side differences in knee cartilage macromorphology (Eckstein et al, 2002b). Previous studies have shown that high-resolution gradient-echo sequences with fat-suppression or water excitation can provide valid information on cartilage volume and thickness (Eckstein et al, 1996(Eckstein et al, , 1998bGraichen et al, 2000;Burgkart et al, 2001;Glaser et al, 2001). For the protocol used in the current work, precision errors have been shown to range between 2% and 6% for cartilage volume, thickness, and size of the bone-cartilage interface area in the various compartments of the knee Eckstein et al, 2001b;Hohe et al, 2002).…”

“…The development of magnetic resonance imaging (MRI) has made it possible to delineate articular cartilage in vivo (Recht et al, 1993;Peterfy et al, 1994;Eckstein et al, 1996). In conjunction with state-of-the-art three-dimensional (3D) postprocessing tools (qMRI) the technique has been shown to produce accurate and precise data on the cartilage morphology (volume, thickness, and surface area) of the knee in healthy individuals (Stammberger et al, 1999a;Hohe et al, 2002), and in patients with femorotibial osteoarthritis (OA) (Burgkart et al, 2001).…”

Quantitative MRI of cartilage and bone: degenerative changes in osteoarthritis

“…This is accomplished by either spectral fat saturation using a prepulse tuned to the resonant frequency of fat (40,(71)(72)(73) or by frequency-selective water excitation (74)(75)(76)(77)(78). Acquisition times are generally shorter for selective water-excitation protocols.…”

“…Owing to the relatively low contrast in some areas of the cartilage surface (AC) and the bone cartilage interface (tAB), particularly in OA, fully automated segmentation of cartilage from MR images has so far been unsuccessful. Various semi-automated segmentation techniques have been proposed, such as region growing (71,72,90,91), active shape models (92), edge detection (93), fitting Bsplines to manually segmented points (94), B-spline snake (active contours) (62,77,95,96), immersion-based watershed segmentation (97) and live wire algorithms (98,99), but to date most large-scale studies have relied on manual segmentation. One reason is that, in the hand of trained and manually skilled users, correction of semi-automated algorithms may take longer than manual segmentation.…”

“…Therefore, each of these parameters should be measured independently. Also, computations of cartilage thickness (ThC) and surfaces (AC/tAB/cAB/dAB) should account for out-of-plane deviations of these measures and be performed in 3D objects, independent of the original sectional orientation (72,104).…”