Cluster analysis of quantitative MRI T2 and T1ρ relaxation times of cartilage identifies differences between healthy and ACL-injured individuals at 3T.

PURPOSE: To identify focal lesions of elevated MRI T2 and T1ρ relaxation times in articular cartilage of an ACL-injured group using a novel cluster analysis technique. MATERIALS AND METHODS: Eighteen ACL-injured patients underwent 3T MRI T2 and T1ρ relaxometry at baseline, 6 months and 1 year and six healthy volunteers at baseline, 1 day and 1 year. Clusters of contiguous pixels above or below T2 and T1ρ intensity and area thresholds were identified on a projection map of the 3D femoral cartilage surface. The total area of femoral cartilage plate covered by clusters (%CA) was split into areas above (%CA+) and below (%CA-) the thresholds and the differences in %CA(+ or -) over time in the ACL-injured group were determined using the Wilcoxon signed rank test. RESULTS: %CA+ was greater in the ACL-injured patients than the healthy volunteers at 6 months and 1 year with average %CA+ of 5.2 ± 4.0% (p = 0.0054) and 6.6 ± 3.7% (p = 0.0041) for T2 and 6.2 ± 7.1% (p = 0.063) and 8.2 ± 6.9% (p = 0.042) for T1ρ, respectively. %CA- at 6 months and 1 year was 3.0 ± 1.8% (p > 0.1) and 5.9 ± 5.0% (p > 0.1) for T2 and 4.4 ± 4.9% (p > 0.1) and 4.5 ± 4.6% (p > 0.1) for T1ρ, respectively. CONCLUSION: With the proposed cluster analysis technique, we have quantified cartilage lesion coverage and demonstrated that the ACL-injured group had greater areas of elevated T2 and T1ρ relaxation times as compared to healthy volunteers.

Variability of CubeQuant T1ρ, quantitative DESS T2, and cones sodium MRI in knee cartilage.

OBJECTIVE: To measure the variability of T1ρ relaxation times using CubeQuant, T2 relaxation times using quantitative double echo in steady state (DESS), and normalized sodium signals using 3D cones sodium magnetic resonance imaging (MRI) of knee cartilage in vivo at 3 T. DESIGN: Eight healthy subjects were scanned at 3 T at baseline, 1 day, 5 months, and 1 year. Ten regions of interest (ROIs) of knee cartilage were segmented in the medial and lateral compartments of each subject's knee. T1ρ and T2 relaxation times and normalized sodium signals were measured and the root-mean-square coefficient of variation (CVRMS) was calculated. Intra-subject variability was measured over short, moderate and long-term, as well as intra-observer and inter-observer variability. RESULTS: The average intra-subject CVRMS measurements over short, moderate, and long-term time periods were 4.6%, 6.1%, and 6.0% for the T1ρ measurements, 6.4%, 9.3%, and 10.7% for the T2 measurements and 11.3%, 11.6%, and 12.9% for the sodium measurements, respectively. The average CVRMS measurements for intra-observer and inter-observer segmentation were 3.8% and 5.7% for the T1ρ measurements, 4.7% and 6.7% for the T2 measurements, and 8.1% and 11.4% for the sodium measurements, respectively. CONCLUSIONS: These CVRMS measurements are substantially lower than previously measured changes expected in patients with advanced osteoarthritis compared to healthy volunteers, suggesting that CubeQuant T1ρ, quantitative DESS T2 and 3D cones sodium measurements are sufficiently sensitive for in vivo cartilage studies.

The effect of a patellar brace on three-dimensional patellar kinematics in patients with lateral patellofemoral osteoarthritis.

OBJECTIVE: Patellar bracing is a mechanical treatment strategy for patellofemoral osteoarthritis (OA) that aims to unload the lateral compartment of the joint by translating the patella medially. Our objective was to determine whether a patellar brace can correct patellar kinematics in patients with patellofemoral OA. DESIGN: We assessed the effect of a patellar brace on three-dimensional patellar kinematics (flexion, spin and tilt; proximal, lateral and anterior translation) at sequential, static knee postures, using a validated magnetic resonance imaging (MRI)-based method, in 19 patients with radiographic lateral patellofemoral OA. Differences in kinematics between unbraced and braced conditions were assessed in the unloaded and loaded knee (15% bodyweight load) using hierarchical linear random-effects models. Random slope and quadratic terms were included in the model when significant (P<0.05). RESULTS: Bracing with load caused the patellae to translate 0.46 mm medially (P<0.001), tilt 1.17° medially (P<0.001), spin 0.62° externally (P=0.012) and translate 1.09 mm distally (P<0.001) and 0.47 mm anteriorly (P<0.001) over the range of knee flexion angles studied. Bracing also caused the patellae to extend in early angles of knee flexion (P<0.001). The brace caused similar trends for the unloaded condition, though magnitudes of the changes varied. CONCLUSION: Bracing changed patellar kinematics, but these changes did not appear large enough to be clinically meaningful because no reduction in pain was observed in the parent study.

WE-G-217BCD-05: Fiducial Marker-Based Motion Compensation for the Acquisition of 3D Knee Geometry Under Weight-Bearing Conditions Using a C-Arm CT Scanner.

PURPOSE: Imaging the knee under realistic load-bearing conditions can be carried out in a horizontal plane using a C-arm CT scanner. Human subjects can be scanned in a standing position and acquired data successfully reconstructed. However, reconstructing this data is a challenge due to significant artifacts that are induced due to involuntary motion. Here, we propose motion correction methods in 2D and 3D. METHODS: Four volunteers were scanned for 8 seconds while squatting with ∼30 degree flexion. Eight tantalum fiducial markers suitably attached around the knee were used to track motion. The marker position in each projection was semi- automatically detected. Each marker's static 3D position, which served as a reference to correct temporal motion, was estimated by triangulating each marker's 2D position from 248 projections using known projection matrices. Motion was corrected in 3 ways: 1) 2D projection shifting based on the mean position of markers, 2) 2D projection warping using approximate thin- plate splines, 3) 3D rigid body warping. RESULTS: The original reconstruction was severely motion-corrupted which made it impossible to distinguish the boundaries of bones. Reconstruction with projection shifting and warping in 2D improved visualization of edges of soft tissue as well as bone. A simple numerical metric of residual bead deviation from static position was reduced from 3.2mm to 0.4mm. The 2D-based methods are inherently limited in that they cannot fully accommodate different 3D movements at different depths from the X-ray source. Reconstruction with 3D warping shows clearer edges and less streak artifact than the 2D methods. CONCLUSIONS: The proposed three motion correction methods effectively reduced motion-induced artifacts in the reconstruction and are therefore suitable for weight-bearing scanning. Future work includes scanning patients in standing position after contrast injection for evaluating the soft tissue structure and constructing 3D finite element models for the estimation of joint cartilage stress. This study was supported by Center for Biomedical Imaging at Stanford, by Siemens AG, Healthcare Sector, and by the Lucas Foundation at Stanford. The concepts and contents proposed here are based on research and are not commercially available.

The effect of load magnitude on three-dimensional patellar kinematics in vivo.

Studies of three-dimensional patellar kinematics done with little or no applied load may not accurately reflect kinematics at physiological load levels, and may provide different results to those acquired with greater applied loads or in physiologic weightbearing. We report the effect of load magnitude on three-dimensional patellar kinematics (flexion, spin and tilt; proximal, lateral and anterior translation) using a validated, sequential static, MRI-based method. Ten healthy subjects loaded their study knee to 0% (no load), 15% and 30% bodyweight (BW) using a custom designed loading rig. Differences between loading levels were determined as a function of knee flexion for each kinematic parameter using linear hierarchical random-effects models. Quadratic and random slope terms were included in the models when significant. We found that the patellae flexed less with knee flexion at 30% BW load compared to 0% BW load (p<0.001) and 15% BW (p=0.004) load. The patellae showed a slight medial tilt with knee flexion at 30% BW load which was significantly less than the medial tilt seen at 0% BW load (p=0.017) and 15% BW load (p=0.043) with knee flexion. Small but statistically significant differences were also observed for proximal and anterior translation; the patellae were in a more proximal and posterior position at 30% BW load than at 0% BW load (p=0.010 and p=0.005, respectively) and 15% BW load (p<0.001 and p=0.029, respectively). Since differences in three-dimensional patellar kinematics were observed between loading levels, magnitudes of prescribed loads must be considered when designing studies and comparing results between studies.

A single measure of patellar kinematics is an inadequate surrogate marker for patterns of three-dimensional kinematics in healthy knees.

Patellofemoral disorders, such as osteoarthritis and patellofemoral pain, are thought to be associated with abnormal patellar kinematics. However, assessments of three-dimensional patellar kinematics are time consuming and expensive. The aim of this study was to determine whether a single static measure of three-dimensional patellar kinematics provides a surrogate marker for three-dimensional patellar kinematics over a range of flexion angles. We assessed three-dimensional patellar kinematics (flexion, tilt and spin; lateral, anterior and proximal translation) at sequential static angles through approximately 45 degrees of loaded knee flexion in 40 normal subjects using a validated, MRI-based method. The surrogate marker was defined as the static measure at 30 degrees of knee flexion and the pattern of kinematics was defined as the slope of the linear best fit line of each subject's kinematic data. A regression model was used to examine the relationship between the surrogate marker and pattern of kinematics. The surrogate marker predicted 26% of the variance in pattern of patellar flexion (p<0.001), 27% of the variance in pattern of patellar spin (p=0.003), 11% of the variance in pattern of proximal translation (p=0.037) and 39% of the variance in pattern of anterior translation (p<0.001). No relationships were seen between the surrogate marker and tilt or lateral translation. The results suggest that a single measure of patellar parameters at 30 degrees knee flexion is an inadequate surrogate marker of three-dimensional patellar kinematics; therefore, a complete assessment of patellar kinematics, over a range of knee flexion angles, is preferable to adequately assess patterns of patellar kinematics.

Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images.

OBJECTIVE: To study the effect of new interactive computer input devices on cartilage segmentation in terms of time, consistency between input devices, and precision in quantitative magnetic resonance imaging (qMRI). DESIGN: We compared two new input devices, an interactive digitizing tablet and an interactive touch-sensitive screen, to a traditional mouse. Medial tibial and patellar cartilage of six healthy and six osteoarthritic knees were segmented using each input device. Cartilage volume, surface area and mean thickness were assessed using a validated algorithm and used to determine consistency and precision. Segmentation time was also measured. RESULTS: Segmenting with an interactive touch-sensitive screen reduced segmentation time by 15% when compared to the traditional mouse but we found no significant difference in segmentation time between the interactive digitizing tablet and the traditional mouse. We found no difference in consistency or precision of cartilage volume, mean thickness or surface area between the three input devices tested. CONCLUSIONS: We conclude that measurements of cartilage made using articular cartilage segmentation from MR images are independent of the input device chosen for user interaction.