Subcutaneous Tissue Thickness is an Independent Predictor of Image Noise in Cardiac CT

Staniak, Henrique Lane; Sharovsky, Rodolfo; Pereira, Alexandre C.; Castro, Cláudio Campi de; Benseñor, Isabela Judith Martins; Lotufo, Paulo A.; Bittencourt, Márcio Sommer

doi:10.5935/abc.20130215

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…Continuous data are presented as mean ± SD, and categorical variables are expressed as number and percentage. We defined “high noise” as >30 HU as in previous reports [13], [14], [15], and t test analysis was used to compare numerical variables whereas chi-square analysis was used to compare categorical variables between groups with noise >30 HU versus ≤30 HU. Linear regression analysis was used to assess the correlation between the image noise with fat volumes and other parameters.…”

Section: Methodsmentioning

confidence: 99%

Fat volume measurements as a predictor of image noise in coronary computed tomography angiography

Abazid

Smettei

Almeman

et al. 2019

Journal of the Saudi Heart Association

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IntroductionImage noise can negatively affect the overall quality of coronary computed tomography angiography (CCTA).ObjectivesThe purpose of this study was to evaluate the relationship between image noise and fat volumes in the chest wall. We also aimed to compare these with other patient-specific predictors of image noise, such as body weight (BW) and body mass index (BMI).MethodsWe undertook a cross-sectional, single-center study. A tube voltage of 100 kV was used for patients with BW <85 kg and 120 kV for BW ≥85 kg. The image noise in the aortic root, single-slice fat volume (SFV) at the level of the left main coronary artery and the total fat volume of the chest (TFV) were analyzed.ResultsA total of 132 consecutive patients were enrolled (mean age ± standard deviation, 51 ± 11 years; 64% male). The mean image noise was 30.5 ± 11 Hounsfield units (HU). We found that patients with image noise >30 HU had significantly higher SFV (75 ± 33 vs. 51 ± 24, p < 0.0001) and TFV (2206 ± 927 vs. 1815 ± 737, p < 0.01) compared with patients having noise ≤30 HU, whereas BW and BMI showed no significant difference (78 ± 13 vs. 81 ± 14, p < 0.34) and (28.7 ± 4.7 vs. 26.8 ± 3.8, p < 0.19), respectively. Linear regression analysis showed that image noise has better correlation with SFV (R = 0.399; p < 0.0001); and TFV (R = 0, p < 0.009) than BMI (R = 0.154, p < 0.039) and BW (R = –0.102, p = 0.12).ConclusionsFat volume measurements of the chest wall can predict CCTA image noise better than other patient-specific predictors, such as BW and BMI.

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Section: Methodsmentioning

confidence: 99%

Fat volume measurements as a predictor of image noise in coronary computed tomography angiography

Abazid

Smettei

Almeman

et al. 2019

Journal of the Saudi Heart Association

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Cardiovascular Computed Tomography and Magnetic Resonance: History and Growing Impact in Brazil and in the World

Nacif¹,

Rochitte²

2014

Arquivos Brasileiros De Cardiologia

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Accuracy Evaluation of Bi-medium Deep Body Thermometer Based on Finite Element Simulation

Sim¹,

Ryou²,

Kim³

et al. 2014

Journal of Biomedical Engineering Research

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Continuous body temperature monitoring is useful and essential in diverse medical procedures such as infection onset detection, therapeutic hypothermia, circadian rhythm monitoring, sleep disorder assessment, and gynecological research. However, the existing thermometers are too invasive or intrusive to be applied to long-term body temperature monitoring. In our previous study, we invented the bi-medium deep body thermometer which can noninvasively and continuously monitor deep tissue temperature. And the ratio of thermal resistances expressed as K-value should be obtained to estimate body temperature with the thermometer and it can be different under various measurement environments. Although the device was proven to be useful through preliminary simulation test and small group of human study, the experimental environment was restrictive in our previous approach. In this study, a finite element simulation was executed to obtain the K-value and evaluate the accuracy of bi-medium thermometer under various measurement environments. In addition, K-value estimation equation was developed by analyzing the influence of 5 measurement environmental factors (medium length, medium height, tissue depth, blood perfusion rate, and ambient temperature) on K-value. The results revealed that the estimation accuracy of bi-medium deep body thermometer based on computer simulation was very high (RMSE < 0.003 o C) in various measurement environments. Also, bi-medium deep body thermometer based on K-value estimation equation showed relatively accurate results (RMSE < 0.3 o C) except for one case. Although the K-value estimation technology should be improved for more accurate body temperature estimation, the results of finite element simulation showed that bi-medium deep body thermometer could accurately measure various tissue temperatures under diverse environments.

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Subcutaneous Tissue Thickness is an Independent Predictor of Image Noise in Cardiac CT

Cited by 3 publications

References 24 publications

Fat volume measurements as a predictor of image noise in coronary computed tomography angiography

Fat volume measurements as a predictor of image noise in coronary computed tomography angiography

Cardiovascular Computed Tomography and Magnetic Resonance: History and Growing Impact in Brazil and in the World

Accuracy Evaluation of Bi-medium Deep Body Thermometer Based on Finite Element Simulation

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