scholarly journals Correlating Ex Vivo Carotid Calcification Measurements With Cerebrovascular Symptoms

Stroke ◽  
2020 ◽  
Vol 51 (9) ◽  
Author(s):  
Rachel M. Cahalane ◽  
Julie M. O’Brien ◽  
Eamon G. Kavanagh ◽  
Michael A. Moloney ◽  
Fiona C. Leahy ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ex Vivo ◽  
High Density ◽  
Micro Computed Tomography ◽  
Density Measurements ◽  
Carotid Plaques ◽  
Asymptomatic Patients ◽  
Noncontrast Computed Tomography ◽  
Carotid Calcification ◽  
Cerebrovascular Symptoms

Background and Purpose: The purpose of this study is to examine the ability of ex vivo derived Agatston, Volume, and Density-Volume calcium scores or calcium density measurements to differentiate between carotid plaques based on preoperative cerebrovascular symptomatology. Methods: Thirty-eight carotid plaques were acquired from standard endarterectomy. Micro-computed tomography was performed on the ex vivo samples. Image series were downsampled to represent the resolution of clinical multidetector computed tomography. Agatston, Volume, and Density-Volume carotid calcium scores were then calculated using coronary methodologies. The fractions of low- and high-density calcification were also determined. Results: The coronary calcium scores could not differentiate between carotid plaques from asymptomatic versus symptomatic patients. However, plaques from asymptomatic patients contained significantly lower fractions of low-density calcification and higher fractions of high-density calcification. Conclusions: Screening for carotid calcium density in noncontrast computed tomography could reflect plaque stability.

scholarly journals Correlation of Micro-Computed Tomography Assessment of Valvular Mineralisation with Histopathological and Immunohistochemical Features of Calcific Aortic Valve Disease

2019 ◽  
Vol 9 (1) ◽  
pp. 29
Author(s):  
Guillermo Solache-Berrocal ◽  
Ana María Barral-Varela ◽  
Sheila Areces-Rodríguez ◽  
Alejandro Junco-Vicente ◽  
Aitana Vallina-Álvarez ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Aortic Valve ◽  
Molecular Mechanisms ◽  
Soft Tissues ◽  
Ex Vivo ◽  
Developed Countries ◽  
Macrophage Infiltration ◽  
Micro Computed Tomography ◽  
Calcific Aortic Valve Disease ◽  
Calcium Deposits

Aortic valve stenosis is a serious disease with increasing prevalence in developed countries. Research aimed at uncovering the molecular mechanisms behind its main cause, aortic valve calcification, is thus crucial for the development of future therapies. It is frequently difficult to measure the extent of mineralisation in soft tissues and some methods require the destruction of the sample. Micro-computed tomography (µCT), a non-destructive technique, was used to quantify the density and volume of calcium deposits on cusps from 57 explanted aortic valves. Conventional and immunostaining techniques were used to characterise valve tissue degeneration and the inflammatory and osteogenic stage with several markers. Although most of the analysed cusps came from severe stenosis patients, the µCT parameter bone volume/tissue volume ratio distinguished several degrees of mineralisation that correlated with the degree of structural change in the tissue and the amount of macrophage infiltration as determined by CD68 immunohistochemistry. Interestingly, exosomal markers CD63 and Alix co-localised with macrophage infiltration surrounding calcium deposits, suggesting that those vesicles could be produced at least in part by these immune cells. In conclusion, we have shown that the ex vivo assessment of aortic valve mineralisation with µCT reflects the molecular and cellular changes in pathological valves during progression towards stenosis. Thus, our results give additional validity to quantitative μCT as a convenient laboratory tool for basic research on this type of cardiovascular calcification.

scholarly journals Root Canal Morphology and Configuration of 118 Mandibular First Molars by Means of Micro–Computed Tomography: An Ex Vivo Study

2016 ◽  
Vol 42 (4) ◽  
pp. 610-614 ◽  
Author(s):  
Thomas Gerhard Wolf ◽  
Frank Paqué ◽  
Maximilian Zeller ◽  
Brita Willershausen ◽  
Benjamín Briseño-Marroquín
Keyword(s):  
Computed Tomography ◽  
Root Canal ◽  
Ex Vivo ◽  
Micro Computed Tomography ◽  
Root Canal Morphology ◽  
Ex Vivo Study ◽  
Canal Morphology

Ex Vivo Visualization of the Mouse Otoconial Layer Compared With Micro-Computed Tomography

2015 ◽  
Vol 36 (2) ◽  
pp. 311-317 ◽  
Author(s):  
Keiji Honda ◽  
Yoshihiro Noguchi ◽  
Yoshiyuki Kawashima ◽  
Masatoki Takahashi ◽  
Ayako Nishio ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ex Vivo ◽  
Micro Computed Tomography

scholarly journals Quantifying Subresolution 3D Morphology of Bone with Clinical Computed Tomography

2019 ◽  
Vol 48 (2) ◽  
pp. 595-605 ◽  
Author(s):  
S. S. Karhula ◽  
M. A. J. Finnilä ◽  
S. J. O. Rytky ◽  
D. M. Cooper ◽  
J. Thevenot ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Statistical Model ◽  
Trabecular Bone ◽  
Volume Fraction ◽  
Ex Vivo ◽  
Bone Volume ◽  
Micro Computed Tomography ◽  
Bone Volume Fraction ◽  
Model Combining ◽  
Occurrence Matrix

Abstract The aim of this study was to quantify sub-resolution trabecular bone morphometrics, which are also related to osteoarthritis (OA), from clinical resolution cone beam computed tomography (CBCT). Samples (n = 53) were harvested from human tibiae (N = 4) and femora (N = 7). Grey-level co-occurrence matrix (GLCM) texture and histogram-based parameters were calculated from CBCT imaged trabecular bone data, and compared with the morphometric parameters quantified from micro-computed tomography. As a reference for OA severity, histological sections were subjected to OARSI histopathological grading. GLCM and histogram parameters were correlated to bone morphometrics and OARSI individually. Furthermore, a statistical model of combined GLCM/histogram parameters was generated to estimate the bone morphometrics. Several individual histogram and GLCM parameters had strong associations with various bone morphometrics (|r| > 0.7). The most prominent correlation was observed between the histogram mean and bone volume fraction (r = 0.907). The statistical model combining GLCM and histogram-parameters resulted in even better association with bone volume fraction determined from CBCT data (adjusted R2 change = 0.047). Histopathology showed mainly moderate associations with bone morphometrics (|r| > 0.4). In conclusion, we demonstrated that GLCM- and histogram-based parameters from CBCT imaged trabecular bone (ex vivo) are associated with sub-resolution morphometrics. Our results suggest that sub-resolution morphometrics can be estimated from clinical CBCT images, associations becoming even stronger when combining histogram and GLCM-based parameters.

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