Robust microCT image preprocessing workflow for quantitative morphometric analysis (QMA) of the knee in small and medium animal models
Pholpat Durongbhan, PhD Candidate
Figure 2 Joint space patch definition at the hip as the perimeter of the joint space using standard clinical CT imaging. The ‘shadow’ of the acetabulum is projected back onto the femoral head for segmentation and then extraction (yellow).
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).
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.
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).
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.
SPONSOR: NIH-NIAMS R01 AR071648, NIH-NIA U01 AG018832, U01 AG19069
Andy Kin On Wong, PhD
Scientist, Joint Department of Medical Imaging, UHN
Assistant Professor, Epidemiology, DLSPH, University of Toronto
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.
- Tibial plateau 1% region avoiding the cortex in most cases.
- Femoral condyle 2% region avoiding the cortex and permitting a substantial amount of trabecular bone for analysis.
|Rachel Whyte Comparison of pQCT Subchondral Bone Imaging Protocols.pdf|
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