Publications by authors named "James W MacKay"

Objective: Cartilage surface mapping is a technique that can visualize 3D cartilage thickness variation throughout a joint without a need for arbitrary regional definitions. The objective of this cross-sectional study was to utilize this technique to evaluate the cartilage thickness distribution in knee osteoarthritis patients and to analyze to what extent it depends on demographic, radiographic, and MRI structural pathology strata.

Methods: Patients of the IMI-APPROACH cohort were included, with MRIs obtained at 1.

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The objective of this study was to determine the optimal meniscal radiomic features to classify people who will develop an incident destabilizing medial meniscal tear. We used magnetic resonance (MR) images from an existing case-control study that includes images from the first 4 years of the Osteoarthritis Initiative (OAI). For this exploratory analysis (n = 215), we limited our study sample to people with (1) intact menisci at the OAI baseline visit, (2) 4-year meniscal status data, and (3) complete meniscal data from each region of interest.

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Objectives: We present a 3-D approach to joint space width (JSW) measurement across the ankle from weight-bearing CT (WBCT) to demonstrate inter-operator reproducibility, test-retest repeatability, and how differences in angulation affect ankle JSW distribution.

Methods: One side from repeat WBCT imaging of both feet and ankles was analysed from 23 individuals as part of their routine clinical care pathway. Joint space mapping was performed at four facets across the talus: talonavicular, talar dome and medial gutter (dome-medial), lateral gutter, and posterior subtalar.

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The objective of this study was to assess the prognostic potential of magnetic resonance (MR)-detected meniscal degeneration in relation to incident destabilizing meniscal tears (radial, complex, root, or macerated) or accelerated knee osteoarthritis (AKOA). We used existing MR data from a case-control study of three groups from the Osteoarthritis Initiative without radiographic KOA at baseline: AKOA, typical KOA, and no KOA. From these groups, we included people without medial and lateral meniscal tear at baseline (n = 226) and 48-month meniscal data (n = 221).

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Objective: Computed tomography (CT) can deliver multiple parameters relevant to osteoarthritis. In this study we demonstrate that a 3-D multiparametric approach at the weight bearing knee with cone beam CT is feasible, can include multiple parameters from across the joint space, and can reveal stronger relationships with disease status in combination.

Design: 33 participants with knee weight bearing CT (WBCT) were analysed with joint space mapping and cortical bone mapping to deliver joint space width (JSW), subchondral bone plate thickness, endocortical thickness, and trabecular attenuation at both sides of the joint.

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Article Synopsis
  • Deep learning (DL) models for automatic segmentation can save time but need extensive fine-tuning for new datasets; this study evaluates their ability to work without such adjustments.
  • The researchers tested pretrained DL models on different datasets that varied by scanner type and patient characteristics, including healthy individuals and those with knee issues.
  • Results showed that the DL model trained specifically on qDESS data outperformed the one trained on Osteoarthritis Initiative data in terms of segmentation accuracy and agreement with manual measurements.
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Background: Quantitative magnetic resonance imaging (MRI) methods such as T1rho and T2 mapping are sensitive to changes in tissue composition, however their use in cruciate ligament assessment has been limited to studies of asymptomatic populations or patients with posterior cruciate ligament tears only. The aim of this preliminary study was to compare T1rho and T2 relaxation times of the anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) between subjects with mild-to-moderate knee osteoarthritis (OA) and healthy controls.

Methods: A single knee of 15 patients with mild-to-moderate knee OA (Kellgren-Lawrence grades 2-3) and of 6 age-matched controls was imaged using a 3.

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Background Advances in computer processing and improvements in data availability have led to the development of machine learning (ML) techniques for mammographic imaging. Purpose To evaluate the reported performance of stand-alone ML applications for screening mammography workflow. Materials and Methods Ovid Embase, Ovid Medline, Cochrane Central Register of Controlled Trials, Scopus, and Web of Science literature databases were searched for relevant studies published from January 2012 to September 2020.

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The dynamic contrast-enhanced (DCE)-MRI parameter K can quantify the intensity of synovial inflammation (synovitis) in knees with osteoarthritis (OA), but requires the use of gadolinium-based contrast agent (GBCA). Diffusion tensor imaging (DTI) measures the diffusion of water molecules with parameters mean diffusivity (MD) and fractional anisotropy (FA), and has been proposed as a method to detect synovial inflammation without the use of GBCA. The purpose of this study is to (1) determine the ability of DTI to quantify the intensity of synovitis in OA by comparing MD and FA with our imaging gold standard K within the synovium and (2) compare DTI and DCE-MRI measures with the semi-quantitative grading of OA severity with the Kellgren-Lawrence (KL) and MRI Osteoarthritis Knee Score (MOAKS) systems, in order to assess the relationship between synovitis intensity and OA severity.

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Background: Stress fractures are a common and potentially debilitating overuse injury to bone and occur frequently among military recruits and athletes. Recovery from a lower body stress fracture typically requires several weeks of physical rehabilitation. Teriparatide, a recombinant form of the bioactive portion of parathyroid hormone (1-34 amino acids), is used to treat osteoporosis, prevent osteoporotic fractures, and enhance fracture healing due to its net anabolic effect on bone.

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Synovitis, inflammation of the synovial membrane, is a common manifestation of osteoarthritis (OA) and is recognized to play a role in the complex pathophysiology of OA. Increased recognition of the importance of synovitis in the OA disease process and its potential as a target for treatment has increased the need for noninvasive detection and characterization of synovitis using medical imaging. Numerous imaging methods can assess synovitis involvement in OA with varying sensitivity, specificity, and complexity.

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Objectives: Knee joint distraction (KJD) has been shown to result in long-term clinical improvement and short-term cartilage restoration in young OA patients. The objective of the current study was to evaluate MRI cartilage thickness up to 10 years after KJD treatment, using a 3D surface-based approach.

Methods: Twenty end-stage knee OA patients were treated with KJD.

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Objectives: To determine whether synovitis graded by radiologists using hybrid quantitative double-echo in steady-state (qDESS) images can be utilized as a non-contrast approach to assess synovitis in the knee, compared against the reference standard of contrast-enhanced MRI (CE-MRI).

Methods: Twenty-two knees (11 subjects) with moderate to severe osteoarthritis (OA) were scanned using CE-MRI, qDESS with a high diffusion weighting (qDESS), and qDESS with a low diffusion weighting (qDESS). Four radiologists graded the overall impression of synovitis, their diagnostic confidence, and regional grading of synovitis severity at four sites (suprapatellar pouch, intercondylar notch, and medial and lateral peripatellar recesses) in the knee using a 4-point scale.

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Background Imaging of structural disease in osteoarthritis has traditionally relied on MRI and radiography. Joint space mapping (JSM) can be used to quantitatively map joint space width (JSW) in three dimensions from CT images. Purpose To demonstrate the reproducibility, repeatability, and feasibility of JSM of the knee using weight-bearing CT images.

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Article Synopsis
  • * The study involved 14 OA participants and 6 healthy volunteers undergoing DCE-MRI, with K showing the best performance in repeatability and discrimination compared to other biomarkers.
  • * Results suggest that K is the most reliable DCE-MRI biomarker for future research in knee OA, outperforming other methods in measuring synovitis.
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Femoroacetabular impingement (FAI) is a disorder that causes hip pain and disability in young patients, particularly athletes. Increased stress on the hip during development has been associated with increased risk of cam morphology. The specific forces involved are unclear, but may be due to continued rotational motion, like the eggbeater kick.

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Automated semantic segmentation of multiple knee joint tissues is desirable to allow faster and more reliable analysis of large datasets and to enable further downstream processing e.g. automated diagnosis.

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The Erlenmeyer flask deformity is a common skeletal modeling deformity, but current classification systems are binary and may restrict its utility as a predictor of associated skeletal conditions. A quantifiable 3-point system of severity classification could improve its predictive potential in disease. Ratios were derived from volumes of regions of interests drawn in 50 Gaucher's disease patients.

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Background: Determining the compositional response of articular cartilage to dynamic joint-loading using MRI may be a more sensitive assessment of cartilage status than conventional static imaging. However, distinguishing the effects of joint-loading vs. inherent measurement variability remains difficult, as the repeatability of these quantitative methods is often not assessed or reported.

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Article Synopsis
  • Vasculitis is a rare condition that can affect the testicles and can be either an isolated issue or part of a larger problem in the body.
  • If a patient has another autoimmune disease, doctors might mistakenly think the testicular issue is just because of that disease or its treatment.
  • This misunderstanding can make it harder for doctors to provide the best care, potentially harming the patient's health.
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