Regional differences of fat depot attenuation using non-contrast, contrast-enhanced, and delayed-enhanced cardiac CT

2018 ◽  
Vol 60 (4) ◽  
pp. 459-467 ◽  
Author(s):  
Gaston A Rodriguez-Granillo ◽  
Carlos Capunay ◽  
Alejandro Deviggiano ◽  
Macarena De Zan ◽  
Patricia Carrascosa
Keyword(s):  
Cardiac Ct ◽  
Energy Levels ◽  
Delayed Enhancement ◽  
Low Energy ◽  
Epicardial Fat ◽  
Dual Energy Ct ◽  
Perivascular Adipose Tissue ◽  
Fat Depots ◽  
Contrast Enhanced ◽  
Significant Attenuation

Background Regional fat density assessed by computed tomography (CT) has been suggested as a marker of perivascular adipose tissue inflammation. Dual energy CT (DECT) allows improved tissue characterization compared to conventional CT. Purpose To explore whether DECT might aid regional fat density discrimination. Material and Methods We included patients who had completed a non-enhanced cardiac CT scan, CT coronary angiography (CTCA), and a delayed enhancement CT. Attenuation levels (Hounsfield units [HU]) were assessed at the epicardial, paracardial, visceral, and subcutaneous fat. The number of coronary segments with disease (SIS) was calculated. Results A total of 36 patients were included in the analysis. Twenty-six (72%) patients had evidence of obstructive disease at CCTA and 25 (69%) patients had evidence of previous myocardial infarction. At non-contrast CT, we did not identify significant attenuation differences between epicardial, paracardial, subcutaneous, and visceral fat depots (−110.8 ± 9 HU, vs. −113.7 ± 9 HU, vs. −114.7 ± 8 HU, vs. −113.8 ± 11 HU, P = 0.36). Significant attenuation differences were detected between fat depots at mid and low energy levels, both at CTCA and delayed-enhancement scans ( P < 0.05 for all). Epicardial fat showed the least negative attenuation, irrespective of the acquisition mode; epicardial fat evaluated at 40 keV was related to the SIS (r = 0.37, P = 0.03). Conclusions In this study, regional fat depots amenable to examination during thoracic CT scans have distinctive regional attenuation values. Furthermore, such differences were better displayed using contrast-enhanced monochromatic imaging at low energy levels.

scholarly journals Quantitative Breast Density in Contrast-Enhanced Mammography

2021 ◽  
Vol 10 (15) ◽  
pp. 3309
Author(s):  
Gisella Gennaro ◽  
Melissa L. Hill ◽  
Elisabetta Bezzon ◽  
Francesca Caumo
Keyword(s):  
Breast Density ◽  
Potential Role ◽  
Multiple Correlation Coefficient ◽  
Low Energy ◽  
Coefficient Of Determination ◽  
Contrast Enhanced ◽  
Increased Risk ◽  
Significant Difference ◽  
Automatic Software ◽  
The Relationship

Contrast-enhanced mammography (CEM) demonstrates a potential role in personalized screening models, in particular for women at increased risk and women with dense breasts. In this study, volumetric breast density (VBD) measured in CEM images was compared with VBD obtained from digital mammography (DM) or tomosynthesis (DBT) images. A total of 150 women who underwent CEM between March 2019 and December 2020, having at least a DM/DBT study performed before/after CEM, were included. Low-energy CEM (LE-CEM) and DM/DBT images were processed with automatic software to obtain the VBD. VBDs from the paired datasets were compared by Wilcoxon tests. A multivariate regression model was applied to analyze the relationship between VBD differences and multiple independent variables certainly or potentially affecting VBD. Median VBD was comparable for LE-CEM and DM/DBT (12.73% vs. 12.39%), not evidencing any statistically significant difference (p = 0.5855). VBD differences between LE-CEM and DM were associated with significant differences of glandular volume, breast thickness, compression force and pressure, contact area, and nipple-to-posterior-edge distance, i.e., variables reflecting differences in breast positioning (coefficient of determination 0.6023; multiple correlation coefficient 0.7761). Volumetric breast density was obtained from low-energy contrast-enhanced spectral mammography and was not significantly different from volumetric breast density measured from standard mammograms.

Dose heterogeneity correction for low-energy brachytherapy sources using dual-energy CT images

2014 ◽  
Vol 59 (18) ◽  
pp. 5305-5316 ◽  
Author(s):  
S Mashouf ◽  
E Lechtman ◽  
P Lai ◽  
B M Keller ◽  
A Karotki ◽  
...  
Keyword(s):  
Ct Images ◽  
Dual Energy ◽  
Low Energy ◽  
Dual Energy Ct ◽  
Heterogeneity Correction

Low-energy levels and gamma transitions inCo58

1975 ◽  
Vol 11 (3) ◽  
pp. 1042-1047 ◽  
Author(s):  
B. J. Brunner ◽  
R. G. Arns ◽  
S. E. Caldwell ◽  
C. M. Rozsa ◽  
J. W. Smith ◽  
...  
Keyword(s):  
Energy Levels ◽  
Low Energy ◽  
Gamma Transitions

Cancer Conspicuity on Low-energy Images of Contrast-enhanced Mammography Compared With 2D Mammography

2021 ◽  
Author(s):  
Christina Konstantopoulos ◽  
Tejas S Mehta ◽  
Alexander Brook ◽  
Vandana Dialani ◽  
Rashmi Mehta ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Analysis Of Variance ◽  
Digital Mammography ◽  
Regions Of Interest ◽  
Low Energy ◽  
Wilcoxon Test ◽  
Digital Mammogram ◽  
Contrast Enhanced ◽  
Improved Performance ◽  
2D Images

Abstract Objective Low-energy (LE) images of contrast-enhanced mammography (CEM) have been shown to be noninferior to digital mammography. However, our experience is that LE images are superior to 2D mammography. Our purpose was to compare cancer appearance on LE to 2D images. Methods In this IRB-approved retrospective study, seven breast radiologists evaluated 40 biopsy-proven cancer cases on craniocaudal (CC) and mediolateral oblique (MLO) LE images and recent 2D images for cancer visibility, confidence in margins, and conspicuity of findings using a Likert scale. Objective measurements were performed using contrast-to-noise ratio (CNR) estimated from regions of interest placed on tumor and background parenchyma. Reader agreement was evaluated using Fleiss kappa. Per-reader comparisons were performed using Wilcoxon test and overall comparisons used three-way analysis of variance. Results Low-energy images showed improved performance for visibility (CC LE 4.0 vs 2D 3.5, P &lt; 0.001 and MLO LE 3.7 vs 2D 3.5, P = 0.01), confidence in margins (CC LE 3.2 vs 2D 2.8, P &lt; 0.001 and MLO LE 3.1 vs 2D 2.9, P &lt; 0.008), and conspicuity compared to tissue density compared to 2D mammography (CC LE 3.6 vs 2D 3.2, P &lt; 0.001 and MLO LE 3.5 vs 2D 3.2, P &lt; 0.001). The average CNR was significantly higher for LE than for digital mammography (CC 2.1 vs 3.2, P &lt; 0.001 and MLO 2.1 vs 3.4, P &lt; 0.001). Conclusion Our results suggest that cancers may be better visualized on the LE CEM images compared with the 2D digital mammogram.

scholarly journals Low-energy levels calculation for 193Ir

2006 ◽  
Vol 36 (4b) ◽  
pp. 1354-1356
Author(s):  
Guilherme Soares Zahn ◽  
Cibele Bugno Zamboni ◽  
Frederico Antonio Genezini ◽  
Joel Mesa-Hormaza ◽  
Manoel Tiago Freitas da Cruz
Keyword(s):  
Energy Levels ◽  
Low Energy

scholarly journals Deep learning-based whole-heart segmentation in 4D contrast-enhanced cardiac CT

2021 ◽  
pp. 105191
Author(s):  
Steffen Bruns ◽  
Jelmer M. Wolterink ◽  
Thomas P.W. van den Boogert ◽  
Jurgen H. Runge ◽  
Berto J. Bouma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cardiac Ct ◽  
Contrast Enhanced ◽  
Whole Heart

scholarly journals Quantification of Epicardial and Thoracic Adipose Tissue using WOA Optimized CNN

2019 ◽  
Vol 8 (9S2) ◽  
pp. 720-724
Keyword(s):  
Adipose Tissue ◽  
Optimization Algorithm ◽  
Selection Process ◽  
Heart Diseases ◽  
Epicardial Fat ◽  
Fat Depots ◽  
Krill Herd ◽  
Whale Optimization ◽  
Fully Automatic ◽  
Paracardial Fat

Cardiac fat depots are associated with the heart diseases. Epicardial fat and thoracic fat plays the major role in the development of cardiovascular disease. The increased thickness of the epicardial and thoracic fat leads to several diseases such as metabolic syndrome, coronary atherosclerosis, etc. It is necessary to quantify the epicardial adipose tissue and thoracic adipose tissue. There are different imaging and assessing techniques for epicardial and thoracic adipose tissue quantification. These tissues can be quantified automatically or manually from the CT and MRI cardiac scans. The quantification of the epicardial fat and thoracic fat requires segmentation of these fats by various segmentation methods and then they are quantified. This project proposes the fully automatic segmentation and quantification of the epicardial and thoracic adipose tissues from the cardiac CT scan images using the krill herd optimization algorithm and fuzzy c-means segmentation algorithm. The whale optimization algorithm performs the feature selection process. The fuzzy cmeans algorithm is used for the segmentation process by means of clustering which segments the epicardial fat and paracardial adipose tissue(EAT &PAT) from the input image. The segmented epicardial and paracardial fat region are then used for the quantification process which provides the epicardial and thoracic fat volume. The thoracic fat is the combination of the epicardial and paracardial fat. This proposed system is implemented by using the MATLAB code. The proposed system is simple, fully automatic and produces accurate results.

Sign in / Sign up

Export Citation Format

Share Document