scholarly journals Analysis of vortex ring formation in the heart chamber by instantaneous vortex deviation based on convolutional neural network

2021 ◽  
Vol 271 ◽  
pp. 03009
Author(s):  
Ke Yang ◽  
Shiqian Wu ◽  
Kelvin K.L. Wong
Keyword(s):  
Neural Network ◽  
Blood Flow ◽  
Convolutional Neural Network ◽  
Quantitative Methods ◽  
Vortex Formation ◽  
Left Ventricular ◽  
Vortex Identification ◽  
Heart Chamber ◽  
Vortex Ring Formation ◽  
Blood Transport

The formation of vortex rings during the left ventricle (LV) filling is an optimized mechanism for blood transport, and the vorticity is an important measure of a healthy heart and LV. There is a relationship between abnormal diastolic vortex structure and impaired LV, and hence vortex identification is vital for understanding the underlying physical mechanism of blood flow. However, due to lack of quantitative methods, defining, computing and mapping the left ventricular vortices has not been rigorously studied previously. In this paper, a novel method of vortex detection based on the convolutional neural network (CNN) is created, which enables determination of the boundary of vortex and integrates the local and global flow fields. We have used the CNN-based vortex identification and vector flow mapping (VFM) to quantify left ventricular vorticity. In the clinical application of our methodology to healthy subjects and uremic patients, we find differences in the strength and position of the vortices between healthy and patients with uremia cardiomyopathy. Our results can accurately indicate the role of vortex formation in intracardiac flow, and provide new insights into the blood flow within the heart structure.

scholarly journals Diagnosis of left ventricular hypertrophybased on convolutional neural network

2019 ◽  
Author(s):  
Zini Jian ◽  
Xianpei Wang ◽  
Jingzhe Zhang ◽  
Xinyu Wang ◽  
Youbin Deng
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Posterior Wall ◽  
Video Stream ◽  
Detection Algorithm ◽  
Left Ventricular ◽  
Diagnostic Significance ◽  
Design Algorithm ◽  
Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, which is not only time-consuming and laborious, but also difficult to accurately locate the edge, which will bring errors to the measurement results. Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

scholarly journals Diagnosis of left ventricular hypertrophy using convolutional neural network

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zini Jian ◽  
Xianpei Wang ◽  
Jingzhe Zhang ◽  
Xinyu Wang ◽  
Youbin Deng
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Posterior Wall ◽  
Video Stream ◽  
Detection Algorithm ◽  
Left Ventricular ◽  
Diagnostic Significance ◽  
Design Algorithm ◽  
Actual Measurement

Abstract Background Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor’s selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results. Methods In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions Therefore, the measurement method proposed in this paper has the advantages of less manual intervention, and the processing method is reasonable and has practical value.

scholarly journals Evaluation of patients with a HeartMate 3 left ventricular assist device using echocardiographic particle image velocimetry

2020 ◽  
Author(s):  
Arend F. L. Schinkel ◽  
Sakir Akin ◽  
Mihai Strachinaru ◽  
Rahatullah Muslem ◽  
Dan Bowen ◽  
...  
Keyword(s):  
Blood Flow ◽  
Particle Image Velocimetry ◽  
Particle Image ◽  
Vortex Formation ◽  
Left Ventricular ◽  
Flow Dynamics ◽  
Lv Function ◽  
Image Velocimetry ◽  
Intraventricular Flow ◽  
Blood Flow Dynamics

Abstract Purpose Poor left ventricular (LV) function may affect the physiological intraventricular blood flow and physiological vortex formation. The aim of this study was to investigate the pattern of intraventricular blood flow dynamics in patients with LV assist devices (LVADs) using echocardiographic particle image velocimetry. Materials and methods This prospective study included 17 patients (mean age 57 ± 11 years, 82% male) who had received an LVAD (HeartMate 3, Abbott Laboratories, Chicago, Illinois, USA) because of end-stage heart failure and poor LV function. Eleven (64%) patients had ischemic cardiomyopathy, and six patients (36%) had nonischemic cardiomyopathy. All patients underwent echocardiography, including intravenous administration of an ultrasound-enhancing agent (SonoVue, Bracco, Milan, Italy). Echocardiographic particle image velocimetry was used to quantify LV blood flow dynamics, including vortex formation (Hyperflow software, Tomtec imaging systems Gmbh, Unterschleissheim, Germany). Results Contrast-enhanced ultrasound was well tolerated in all patients and was performed without adverse reactions or side effects. The LVAD function parameters did not change during or after the ultrasound examination. The LVAD flow was on average 4.3 ± 0.3 L/min, and the speed was 5247 ± 109 rotations/min. The quantification of LV intraventricular flow demonstrated substantial impairment of vortex parameters. The energy dissipation, vorticity, and kinetic energy fluctuation indices were severely impaired. Conclusions Echo particle velocimetry is safe and feasible for the quantitative assessment of intraventricular flow in patients with an LVAD. The intraventricular LV flow and vortex parameters are severely impaired in these patients.

scholarly journals A Convolutional Neural Network Combining Discriminative Dictionary Learning and Sequence Tracking for Left Ventricular Detection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3693
Author(s):  
Xuchu Wang ◽  
Fusheng Wang ◽  
Yanmin Niu
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Dictionary Learning ◽  
Heart Diseases ◽  
Volume Measurement ◽  
Blood Pool ◽  
Left Ventricular ◽  
Data Set ◽  
Discriminative Dictionary Learning

Cardiac MRI left ventricular (LV) detection is frequently employed to assist cardiac registration or segmentation in computer-aided diagnosis of heart diseases. Focusing on the challenging problems in LV detection, such as the large span and varying size of LV areas in MRI, as well as the heterogeneous myocardial and blood pool parts in LV areas, a convolutional neural network (CNN) detection method combining discriminative dictionary learning and sequence tracking is proposed in this paper. To efficiently represent the different sub-objects in LV area, the method deploys discriminant dictionary to classify the superpixel oversegmented regions, then the target LV region is constructed by label merging and multi-scale adaptive anchors are generated in the target region for handling the varying sizes. Combining with non-differential anchors in regional proposal network, the left ventricle object is localized by the CNN based regression and classification strategy. In order to solve the problem of slow classification speed of discriminative dictionary, a fast generation module of left ventricular scale adaptive anchors based on sequence tracking is also proposed on the same individual. The method and its variants were tested on the heart atlas data set. Experimental results verified the effectiveness of the proposed method and according to some evaluation indicators, it obtained 92.95% in AP50 metric and it was the most competitive result compared to typical related methods. The combination of discriminative dictionary learning and scale adaptive anchor improves adaptability of the proposed algorithm to the varying left ventricular areas. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.

scholarly journals Diagnosis of left ventricular hypertrophybased on convolutional neural network

2020 ◽  
Author(s):  
Zini Jian ◽  
Xianpei Wang ◽  
Jingzhe Zhang ◽  
Xinyu Wang ◽  
Youbin Deng
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Posterior Wall ◽  
Video Stream ◽  
Detection Algorithm ◽  
Left Ventricular ◽  
Diagnostic Significance ◽  
Design Algorithm ◽  
Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, which is not only time-consuming and laborious, but also difficult to accurately locate the edge, which will bring errors to the measurement results.Material/Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

scholarly journals A Sensorless Control System for an Implantable Heart Pump using a Real-time Deep Convolutional Neural Network

2021 ◽  
Author(s):  
Masoud Fetanat ◽  
Michael Stevens ◽  
Christopher Hayward ◽  
Nigel H. Lovell
Keyword(s):  
Neural Network ◽  
Control System ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Sensorless Control ◽  
Left Ventricular ◽  
Deep Convolutional Neural Network ◽  
Ventricular Assist Devices ◽  
Physiological Control ◽  
Flow Measurements

Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44 %, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.

scholarly journals Diagnosis of left ventricular hypertrophy using convolutional neural network

2020 ◽  
Author(s):  
Zini Jian ◽  
Xianpei Wang ◽  
Jingzhe Zhang ◽  
Xinyu Wang ◽  
Youbin Deng
Keyword(s):  
Neural Network ◽  
Left Ventricle ◽  
Convolutional Neural Network ◽  
Posterior Wall ◽  
Video Stream ◽  
Detection Algorithm ◽  
Left Ventricular ◽  
Diagnostic Significance ◽  
Design Algorithm ◽  
Actual Measurement

Abstract Background: Clinically, doctors obtain the left ventricular posterior wall thickness (LVPWT) mainly by observing ultrasonic echocardiographic video stream to capture a single frame of images with diagnostic significance, and then mark two key points on both sides of the posterior wall of the left ventricle with their own experience for computer measurement. In the actual measurement, the doctor's selection point is subjective, and difficult to accurately locate the edge, which will bring errors to the measurement results.Material/Methods: In this paper, a convolutional neural network model of left ventricular posterior wall positioning was built under the TensorFlow framework, and the target region images were obtained after the positioning results were processed by non-local mean filtering and opening operation. Then the edge detection algorithm based on threshold segmentation is used. After the contour was extracted by adjusting the segmentation threshold through prior analysis and the OTSU algorithm, the design algorithm completed the computer selection point measurement of the thickness of the posterior wall of the left ventricle. Results: The proposed method can effectively extract the left ventricular posterior wall contour and measure its thickness. The experimental results show that the relative error between the measurement result and the hospital measurement value is less than 15%, which is less than 20% of the acceptable repeatability error in clinical practice. Conclusions: Therefore, the method proposed in this paper not only has the advantage of less manual intervention, but also can reduce the workload of doctors.

scholarly journals Predicting 15O-Water PET cerebral blood flow maps from multi-contrast MRI using a deep convolutional neural network with evaluation of training cohort bias

2019 ◽  
Vol 40 (11) ◽  
pp. 2240-2253 ◽  
Author(s):  
Jia Guo ◽  
Enhao Gong ◽  
Audrey P Fan ◽  
Maged Goubran ◽  
Mohammad M Khalighi ◽  
...  
Keyword(s):  
Neural Network ◽  
Blood Flow ◽  
Cerebral Blood Flow ◽  
Convolutional Neural Network ◽  
Mean Squared Error ◽  
Estimation Error ◽  
Similarity Index ◽  
Structural Similarity ◽  
Target Population ◽  
Deep Convolutional Neural Network

To improve the quality of MRI-based cerebral blood flow (CBF) measurements, a deep convolutional neural network (dCNN) was trained to combine single- and multi-delay arterial spin labeling (ASL) and structural images to predict gold-standard 15O-water PET CBF images obtained on a simultaneous PET/MRI scanner. The dCNN was trained and tested on 64 scans in 16 healthy controls (HC) and 16 cerebrovascular disease patients (PT) with 4-fold cross-validation. Fidelity to the PET CBF images and the effects of bias due to training on different cohorts were examined. The dCNN significantly improved CBF image quality compared with ASL alone (mean ± standard deviation): structural similarity index (0.854 ± 0.036 vs. 0.743 ± 0.045 [single-delay] and 0.732 ± 0.041 [multi-delay], P <  0.0001); normalized root mean squared error (0.209 ± 0.039 vs. 0.326 ± 0.050 [single-delay] and 0.344 ± 0.055 [multi-delay], P <  0.0001). The dCNN also yielded mean CBF with reduced estimation error in both HC and PT ( P <  0.001), and demonstrated better correlation with PET. The dCNN trained with the mixed HC and PT cohort performed the best. The results also suggested that models should be trained on cases representative of the target population.

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