The motivation for developing joint space mapping was the drive to improve on radiographic joint space assessment for more accurate and sensitive detection of osteoarthritis disease progression. We have proposed that the most logical advance from a 2-D planar representation of a joint would be a 3-D model approach, with logic further dictating that computed tomography (CT) could achieve this with x-ray based volumetric imaging of mineralised tissues.
The concept of measuring joint space width as the distance between two bony articular surfaces was taken from cortical bone mapping. This imaging analysis technique was initially developed to assess properties such as cortical bone thickness and mineral density in the setting of osteoporosis, and is now well established in its utility for identifying links with fracture risk at the proximal femur. Cortical bone mapping deblurs clinical CT imaging data to measure the thickness of cortical bone to levels of accuracy well below the imaging system resolution capability, presenting measurement data on a topographically realistic 3-D surface (Figure 1).
However, rather than just looking at the thickness of cortical bone, the same algorithmic optimisation approach can be used to measure the distance between outer bone surfaces at a joint, hence delivering joint space width.
But in order to do this, the margins of the joint space need to be defined. Figure 2 summarises how this is achieved at the hip joint, taking the ‘shadow’ of opposing acetabular bone projected back onto the surface of the femoral head and manually segmenting this out for extraction as the ‘joint space patch’.
These steps, along with the joint space measurement algorithm, are performed using the free-to-download software package StradView, developed by Dr Graham Treece and colleagues at the Cambridge University Engineering Department. The joint space patch then acts as a 3-D framework for the joint space mapping algorithm in which the distance between bony surfaces is measured from the imaging data volume and mapped out vertex by vertex (Figure 3).
Figure 3 The algorithm for joint space mapping (JSM) at the hip, with joint space width (JSW) defined as the distance between the acetabular (blue) and femoral (orange) articular bone surfaces in 3-D as measured from the original joint space patch (yellow).
Application of joint space mapping at the hip in collaboration with the Icelandic Heart Association and AGES-Reykjavik cohort showed that embracing a 3-D CT-based approach to joint space width assessment (including 3-D morphological parameters and Kellgren & Lawrence grade derived from the same CT imaging) led to an 18% improvement in prediction of future total hip replacement over the FDA gold standard of 2-D minimum JSW measurement.
In contrast to these hip studies that used supine imaging, an important technical development over the last decade has been the ability to acquire CT imaging at weight bearing lower limb joints with cone beam technology. The orthopaedic foot and ankle community have been early adopters of this, with joint space mapping having now also been demonstrated at the weight bearing ankle. However, it is only relatively recently that weight bearing CT has been gaining popularity at the knee (see the feature by Neil Segal on this page as an exemplar of this pioneering work).
In collaboration with Professor Segal from Kansas, USA, joint space mapping has now also been applied at the knee (Figure 4) to show its reproducibility, test-retest sensitivity, and relationship with radiographic grading.
Figure 4 Joint space mapping at the knee using weight bearing cone beam CT technology in two individuals, one with a Kellgren and Lawrence grade (KLG) of 0 (no osteoarthritis) and another with 4 (severe osteoarthritis).The yellow lines represent the bony articular surfaces within the limits of the joint space perimeter, while the orange lines represent the ‘skeleton’ of the joint at the halfway point, on which the colour wash joint space maps are displayed.
The main perceived advantage of a 3-D joint space mapping approach over radiographic joint space measurement is the potential for greater sensitivity in phenotyping and detecting structural disease progression. However the concurrent and predictive validity of this does now need to be tested in a cohort with relevant clinical outcome measures, as is underway in collaboration with the Multicenter Osteoarthritis Study.
In addition, joint space maps from the same (or different) individuals can be easily compared by registration to a ‘canonical’ surface, which can then not only be used to look at differences between time points and study groups, but also to perform relevant statistical analysis with statistical parametric mapping. This registration process is performed using free-to-download software developed by Dr. Andrew Gee at the Cambridge University Engineering Department. Statistical parametric mapping can be performed using the SurfStat toolbox for MATLAB.
The robustness and versatility of a 3-D surface-based approach to assessment of structural joint disease has also been translated to magnetic resonance imaging (MRI). Cartilage surface mapping, recently developed and tested by MacKay and colleagues from Cambridge and Norwich, UK, has delivered a platform for multiparametric analysis of MRI data at the knee in prize-winning research recognised by the International Society of Magnetic Resonance Medicine (Figure 5).
Figure 5 Baseline and 6-month follow-up thickness and relaxation time data for a single OA participant displayed on canonical femoral and tibial surfaces (dGEMRIC = delayed gadolinium enhanced MRI of cartilage). Note the spatial heterogeneity of changes and the co-occurrence of both significant positive and negative changes in thickness and T1rho/T2 relaxation times.
If you are interested in finding out more or using any of these 3-D imaging analysis techniques, then please contact Dr. Turmezei through his OCTA profile page.
The figure presents a visualization of calcified cartilage thickness in micro-computed tomography of rabbit patella. Calcified cartilage is segmented with a deep learning model consisting of ResNet-18 with FPN. Please see further details in https://doi.org/10.1111/joa.13435
Looking for PhD project at the juncture of arthritis and imaging? See these unique opportunities to work with the Integrative Cartilage Research Group, headed by Associate Professor Kathryn Stok at the Department of Biomedical Engineering, The University of Melbourne, and Assistant Professor Andy Kin On Wong in the School Public Health at the University of Toronto on projects focused on data & image analysis as well as time-lapsed imaging of arthritis progression.
Neil Segal, MD, MS
Dr. George Varghese Professor of Rehabilitation Medicine
Director of Clinical Research
Medical Director of Musculoskeletal Rehabilitation
Department of Rehabilitation Medicine
University of Kansas Medical Center
INTRODUCTION: Meniscal extrusion may be missed on non-weight-bearing MRI. Failure to detect meniscal extrusion has hampered development of effective therapies for osteoarthritis (OA) prevention. Weight-Bearing CT (WBCT) has been found to be more sensitive and accurate for other knee OA features, and more accurate assessment of meniscal damage could potentially improve prediction of worsening joint structure and pain.
OBJECTIVE: To assess the rate of detection and severity of meniscal extrusions visualized on WBCT vs. on MRI in older adults with or at elevated risk for knee OA.
METHODS: Ancillary to the Multicenter Osteoarthritis Study (MOST), a longitudinal study of knee OA in older Americans, fixed-flexion knee images were acquired using a prototype WBCT scanner. A 3D dataset with an isotropic resolution of 0.37mm was reconstructed from cone beam projections. MRI was acquired using a 1.5T peripheral scanner with participants seated and the knee semi-flexed. Radiologists, blinded to patient identifiers, scored meniscal extrusion severity on each modality (0/1/2/3). Kellgren-Lawrence (KL) grade of knee OA was collected as part of MOST.
RESULTS: Of 864 participants with WBCT imaging of the knees, 284 had MRI read for meniscal extrusion. WBCT detected extrusion not detected on MRI in 27.1% of medial and 8.5% of lateral menisci and higher grades of extrusion for 30.6% of medial and 8.8% of lateral menisci (full results in Tables 1 & 2). Knees with greater medial and lateral extrusions visualized on WBCT were predominantly those with early OA (KL<2 for 80.5% and 64% respectively). An example case in which meniscal extrusion visualization differed between modalities is included in Figure 1.
CONCLUSION: WBCT detects meniscal extrusions not detected on standard MRI. Detection of this risk factor for OA progression people with early disease supports a need to assess longitudinal associations between meniscal extrusion detected on WBCT and worsening of pain and joint structure.
SPONSOR: NIH-NIAMS R01 AR071648, NIH-NIA U01 AG018832, U01 AG19069
Andy Kin On Wong, PhD
Scientist, Joint Department of Medical Imaging, UHN
Peripheral quantitative computed tomography (pQCT) has been used to measure subchondral BMD of the knee.
Although pQCT model XCT2000 has a limited gantry diameter, it can still accommodate most individuals with BMI≤ 30 kg/m2. XCT3000 has a larger gantry able to accommodate most knees even for individuals with higher BMI.
Bennell previously examined subchondral BMD at the 2% and 4% tibial plateau relative to a reference line placed at a level between medial and lateral tibial compartments’ most radio-opaque plateau regions. No femoral condyles were examined, and if compartments were misaligned from the scanner’s Z-axis, the compartment-specific analyses would be oblique.
Whyte presented alternative protocols suggesting 4 individual reference lines for each of medial and lateral tibial and femoral condyles, then using 1% relative distance for tibial plateaus and 2% relative distance for femoral condyles – to account for differences in regions of interests.
Subchondral regions of interests marked by periosteal perimeters inset by 20% and total BMD computed to yield subchondral BMD for each compartment.
The results of test-retest precision for Protocol 3 applied to healthy and diseased patients, demonstrating sufficient test-retest precision for subchondral BMD measurements overall: