<![CDATA[Computed Tomography in Osteoarthritis (OCTA) Research - Featured Research]]>Wed, 18 May 2022 01:54:55 -0500Weebly<![CDATA[Robust microCT image preprocessing workflow for quantitative morphometric analysis (QMA) of the knee in small and medium animal models]]>Mon, 28 Mar 2022 03:42:07 GMThttp://octa-research.org/featured-research/robust-microct-image-preprocessing-workflow-for-quantitative-morphometric-analysis-qma-of-the-knee-in-small-and-medium-animal-modelsPholpat Durongbhan, PhD Candidate
Dr. Kathryn S. Stok

Integrative Cartilage Research Group at the University of Melbourne
Department of Biomedical Engineering, The University of Melbourne, Victoria 3010 Australia

​Impact: Modern medical imaging serves as a powerful tool for understanding disease progression, performing accurate diagnosis, and facilitating development of appropriate treatments. Computed tomography (CT) imaging provides 3-dimensional structural information in a non-invasive and real-time manner, making it a key modality in musculoskeletal preclinical and clinical research. Automating the image processing workflow, as described here, will allow for a rapid and reproducible quantitative analysis of joints. Fast and reliable measurements can then be used to track and assess disease progression in osteoarthritic joints.

Introduction: Recent studies [1][2] have proposed a suite of 17 quantitative morphometric analysis measures (QMA) describing structures of the joint to assess it as a single organ and have demonstrated its reproducibility and sensitivity in assessing joint health in preclinical rat and rabbit models using micro-computed tomography (microCT). However, the accessibility and use of quantitative measurements of joint morphometry have been limited by its high sensitivity to tibial alignment and appropriate volume of interest (VOI) selection of joint compartments; often a challenging and time-consuming manual task.

Objective: The objective of this work is to develop a novel automatic, efficient, and model-invariant image preprocessing pipeline that allows for highly reproducible 3D quantitative morphometric analysis (QMA) of the joint.

Methods: Two modules working as a pipeline were developed to tackle to problems of tibial alignment and volume of interest (VOI) selection of joint compartments. Joint alignment is achieved by representing the tibia’s basic form using lower degree spherical harmonic basis functions (SPHARM) [3] and performing alignment analytically using principal component analysis. The second module subdivides the joint into appropriate lateral and medial VOIs with a novel application of a watershedding approach based on persistence homology [4]. Multiple repeated microCT scans of small (rat) and medium (rabbit) animal knees were processed using the pipeline to demonstrate its model invariance. Existing 3D joint QMA was performed to evaluate the pipeline’s ability to generate reproducible measurements. A summary of the workflow is shown in Figure 1.

Results: Typical results of the alignment module can be seen in Figure 2. Measurements for the joint centre of mass, cartilage contact area under virtual loading, joint space width, and joint space volume showed excellent reproducibility (all intraclass correlation coefficients > 0.75) with less than 9.5% root-mean-squared error compared to manual processing results from previous studies [1][2]. Compared to earlier manual work, this workflow has reduced time (average time < 4 minutes per sample) and technical requirements (fully automated, no recalibration between models) needed to preprocess joint images for 3D joint QMA.

Conclusions: The software provides an automated and efficient preprocessing solution that allows highly reproducible 3D joint QMA for two animal models. This work can increase the accessibility of 3D joint QMA measurements, which shows potential as a platform to quantify disease-based morphometric features for joint research from microCT scans using multiple preclinical animal models.
Please find further details of this work at: https://doi.org/10.1038/s41598-021-04542-8.

Figure 1: Overview of the pipeline as well as the relevant joint QMA used to evaluate each process. 3D microCT masks of cartilage (left), femur (central), and tibia (right) of a typical rat knee is used to highlight each process’s input and result.

Figure 2: Typical images of a segmented rat knee joint (a) before alignment and (b) after processing through the automated alignment software.
<![CDATA[Joint Space Mapping: History and Future]]>Mon, 04 Oct 2021 05:00:00 GMThttp://octa-research.org/featured-research/joint-space-mapping-history-and-futureThe 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[1]. 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).
Figure 1 Cortical bone mapping demonstrating the 3-D surface distribution of cortical bone on a mesh representation of the proximal femur[2]. This has been shown to be related to radiographic grading of osteoarthritis[3].
​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. 
Figure 2 Joint space patch definition at the hip as the perimeter of the joint space using standard clinical CT imaging[4]. The ‘shadow’ of the acetabulum is projected back onto the femoral head for segmentation and then extraction (yellow). 
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[5]. 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)[6].
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[7]
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[8]. 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[9]

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[10]
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[11]. Statistical parametric mapping can be performed using the SurfStat toolbox for MATLAB[12].
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)[13].
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[14].
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[15].
Dr. Tom Turmezei
Consultant Radiologist, Norfolk and Norwich University Hospital, UK
Honorary Associate Professor, University of East Anglia, UK
Twitter handles: @3DJointSpace | @tomturmezei
<![CDATA[MicroCT of Calcified Cartilage]]>Mon, 16 Aug 2021 17:46:52 GMThttp://octa-research.org/featured-research/microct-of-calcified-cartilage
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
<![CDATA[Joint PhD Positions at the University of Toronto & The University Melbourne]]>Fri, 23 Jul 2021 02:13:38 GMThttp://octa-research.org/featured-research/joint-phd-positions-at-the-university-of-toronto-the-university-melbourneLooking 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.
Data & Image Analysis of Arthritis Progression.pdf
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Time-lapsed Imaging of Arthritis Progression.pdf
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<![CDATA[WBCT Provides an Increased Rate of Detection of Meniscal Extrusion Compared with MRI]]>Tue, 22 Jun 2021 12:25:00 GMThttp://octa-research.org/featured-research/wbct-provides-an-increased-rate-of-detection-of-meniscal-extrusion-compared-with-mriPicture
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

Figure 1
<![CDATA[pQCT Subchondral Bone Imaging]]>Mon, 18 Jan 2021 06:00:00 GMThttp://octa-research.org/featured-research/pqct-subchondral-bone-imagingAndy Kin On Wong, PhD

Scientist, Joint Department of Medical Imaging, UHN 
Assistant Professor, Epidemiology, DLSPH, University of Toronto

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. 
  • 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. ​
See protocol comparison poster in downloadable PDF below– presented in 2019 at QMSKI in Calgary, AB, Canada. 
Rachel Whyte Comparison of pQCT Subchondral Bone Imaging Protocols.pdf
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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: