A multi-dimensional approach for describing internal bleeding in an artery: implications for Doppler ultrasound guiding HIFU hemostasis

Yang, Di; Zhang, Dong; Guo, Xiasheng; Gong, Xinxin; Fei, Xingbo

doi:10.1088/0031-9155/53/18/009

Cited by 2 publications

(4 citation statements)

References 23 publications

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“…We believe our method specifically captured the turbulence of blood flow at near time-points post-injury (T 1 and T 2 ) as the oxygenated blood spurted out through the site of arterial injury with increased systolic and diastolic velocities causing flow turbulence. It can be explained as the converging blood at injury causing contradictive angles between the flow and Doppler beam that cause the non-uniform pattern at site of arterial injury [25]. The red-blue check patterns observed in Figure 6 further corroborate the findings of [23], [24] related to blood flow turbulence at arterial injury site.…”

Section: Discussionsupporting

confidence: 67%

“…We believe our method specifically captured the turbulence of blood flow at near time-points post-injury ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${T_{1}}$ \end{document} and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${T_{2}}$ \end{document} ) as the oxygenated blood spurted out through the site of arterial injury with increased systolic and diastolic velocities causing flow turbulence. It can be explained as the converging blood at injury causing contradictive angles between the flow and Doppler beam that cause the non-uniform pattern at site of arterial injury [25] . The red-blue check patterns observed in Figure 6 further corroborate the findings of [23] , [24] related to blood flow turbulence at arterial injury site.…”

Section: Discussionmentioning

confidence: 99%

“…Although these unsupervised methods detected blood pool, these were not specifically designed to detect the location of the hemorrhage captured by color Doppler ultrasound, which showed that jet flow at the site of hemorrhage had a different (red-blue check) pattern probably capturing turbulence compared to the uniform blood flow in the entire artery [23] , [24] , [25] . This difference in pattern fits the definition of outliers (or anomalies) in the data, thus motivating the application of unsupervised anomaly detection.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Mitra

Qiu

MacDonald

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Hemorrhage control has been identified as a priority focus area both for civilian and military populations in the United States because exsanguination is the most common cause of preventable death in hemorrhagic injury. Non-compressible torso hemorrhage (NCTH) has high mortality rate and there are currently no broadly available therapies for NCTH outside of a surgical room environment. Novel therapies, which include High Intensity Focused Ultrasound (HIFU) have emerged as promising methods for hemorrhage control as they can non-invasively cauterize bleeding tissue deep within the body without injuring uninvolved regions. A major challenge in the application of HIFU with color Doppler US guidance is the interpretation and optimization of the blood flow images in real-time to identify the hemorrhagic focus. Today, this task requires an expert sonographer, limiting the utility of this therapy in non-clinical environments. In this work, we investigated the feasibility of an automated hemorrhage detection method using a Generative Adversarial Network (GAN) for anomaly detection that learns a manifold of normal blood flow variability and subsequently identifies anomalous flow patterns that fall outside the learned manifold. As an initial feasibility study, we collected ultrasound color Doppler images of femoral arteries in an animal model of vascular injury (N=11 pigs). Velocity information of the blood flow were extracted from the color Doppler images that were used for training and testing the anomaly detection network. Normotensive images from 8 pigs were used for training, and testing was performed on normotensive, immediately after injury, 10 minutes post-injury and 30 minutes post-injury images from 3 other pigs. The residual images or the reconstructed error maps show promise in detecting hemorrhages with an AUC of 0.90, 0.87, 0.62 immediately, 10 minutes post-injury and 30 minutes post-injury respectively with an overall AUC of 0.83.

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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Mitra

Qiu

MacDonald

et al. 2022

IEEE J. Transl. Eng. Health Med.

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“…presence of characteristic features from blood flow turbulence [6], but it is time-consuming and requires well-trained sonographers. Quantitative flow parameters from spectral Doppler, such as resistance index, have been evaluated for bleeding detection, but it is operator-dependent [7,8,9]. Perivascular tissue vibration is another indicator for bleeding [10].…”

Section: Introductionmentioning

confidence: 99%

Tissue vibration pulsatility for arterial bleeding detection using Doppler ultrasound

Xie

Kim

2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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Trauma is the number one cause of death among Americans between 1 and 44 years old, and exsanguination due to internal bleeding resulting from arterial injuries is a major factor in trauma deaths. We have evaluated the feasibility of using tissue vibration pulsatility in arterial bleeding detection. Eight femoral arteries from four juvenile pigs were punctured transcutaneously with a 6 or 9-French catheter. Also, 11 silicone vessels wrapped with turkey breast were placed in a pulsatile flow phantom and penetrated with an 18-gauge needle. The tissue vibration pulsatility was derived as a ratio of the maximum spectral energy from 200 to 2500 Hz of tissue vibration in systole over a baseline value in diastole. Then, the tissue vibration pulsatility index (TVPI) was defined as the maximum tissue vibration pulsatility value for each experimental condition. Both in vitro and in vivo results showed that the TVPI from injured vessels is significantly higher (p<0.005) than that of intact vessels. In addition, we constructed the 2D map of tissue vibration pulsatility during in vitro studies and found that it could be used for spatial localization of the puncture site. Our preliminary results indicate that the tissue vibration pulsatility may be useful for detecting arterial bleeding and localizing the bleeding site.

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A multi-dimensional approach for describing internal bleeding in an artery: implications for Doppler ultrasound guiding HIFU hemostasis

Cited by 2 publications

References 23 publications

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method

Tissue vibration pulsatility for arterial bleeding detection using Doppler ultrasound

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