Computed Tomography of the Knee Joint

Author(s):  
Iswadi Damasena ◽  
Tim Spalding
2019 ◽  
Vol 16 ◽  
pp. 78-84 ◽  
Author(s):  
Thomas P. Scherer ◽  
Sebastian Hoechel ◽  
Magdalena Müller-Gerbl ◽  
Andrej M. Nowakowski

1986 ◽  
Vol 22 (1) ◽  
pp. 131
Author(s):  
B W Jang ◽  
J H Kwon ◽  
S H Park ◽  
T H Kim ◽  
I K Park ◽  
...  

2021 ◽  
pp. 20200493
Author(s):  
Yuesheng Xie ◽  
Ling Li ◽  
Riqiang Luo ◽  
Ting Xu ◽  
Lin Yang ◽  
...  

Objective: This study aimed to investigate the diagnostic performance of minimally invasive arthroscopy for knee gout when comparing with joint ultrasonography and dual-energy computed tomography (DECT). Methods: From January 2016 to December 2018, 121 inpatients with knee joint swelling and pain were prospectively enrolled, including 63 gout patients and 58 non-gout patients. All patients underwent pre-operative ultrasonography and DECT to evaluate knee joint monosodium urate (MSU) deposits, followed by minimally invasive arthroscopy. The gold-standard for gout diagnosis was defined as the detection of MSU crystals in the synovial fluid under polarizing microscopic or pathological analysis. Results: The diagnostic results of ultrasonic double contour sign, hyperechogenic foci, MSU deposition (detected by DECT), MSU deposition (detected by arthroscopy) and MSU deposition in cartilage (detected by arthroscopy) were significantly associated with that of the gold-standard. Except for hyperechogenic foci, the other four indexes had high sensitivity and specificity (approximately or over 80%) and a large odds ratio (OR) (14.73 to 36.56), indicating good diagnostic performance. Detection of MSU deposition in cartilage by arthroscopy had a good diagnostic agreement with the ultrasonic double contour sign (κ = 0.711, p < 0.001). Conclusion: Joint ultrasonography, DECT, and minimally invasive arthroscopy had high sensitivity and specificity for the diagnosis of knee gouty arthritis. Minimally invasive arthroscopy was superior to joint ultrasonography and DECT, which can be a useful supplement for the diagnosis of gout. Advances in knowledge: This is the first study comparing the diagnostic performance for knee gout among the joint ultrasonography, DECT, and minimally invasive arthroscopy.


1983 ◽  
Vol 7 (6) ◽  
pp. 1043-1049 ◽  
Author(s):  
Roberto Passariello ◽  
Fausto Trecco ◽  
Fosco De Paulis ◽  
Rosanna De Amicis ◽  
Giuseppe Bonanni ◽  
...  

2020 ◽  
Vol 142 (5) ◽  
Author(s):  
Katariina A. H. Myller ◽  
Rami K. Korhonen ◽  
Juha Töyräs ◽  
Petri Tanska ◽  
Sami P. Väänänen ◽  
...  

Abstract Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were &lt;1 MPa, &lt;0.72% and &lt;0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p &gt;0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.


2020 ◽  
Vol 29 (4) ◽  
pp. 705-716
Author(s):  
Ruijing Li ◽  
Houjin Chen ◽  
Yahui Peng ◽  
Jupeng Li ◽  
Yanfeng Li

2020 ◽  
Vol 10 (2) ◽  
pp. 129-139
Author(s):  
S.K. Ternovoy ◽  
◽  
N.S. Serova ◽  
V.A. Bakhvalova ◽  
A.V. Lychagin ◽  
...  

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