scholarly journals Area-Preserving Mapping of 3D Carotid Ultrasound Images Using Density-Equalizing Reference Map

2020 ◽  
Vol 67 (9) ◽  
pp. 2507-2517 ◽  
Author(s):  
Gary P. T. Choi ◽  
Bernard Chiu ◽  
Chris H. Rycroft
Keyword(s):  
Ultrasound Images ◽  
Carotid Ultrasound ◽  
Reference Map ◽  
Area Preserving

scholarly journals Should clinicians be given hard copies of duplex carotid ultrasound images?

1991 ◽  
Vol 54 (10) ◽  
pp. 936-937
Author(s):  
S Gunatilake ◽  
P Sandercock ◽  
J Slattery
Keyword(s):  
Ultrasound Images ◽  
Carotid Ultrasound

Early-stage atherosclerosis detection using deep learning over carotid ultrasound images

2016 ◽  
Vol 49 ◽  
pp. 616-628 ◽  
Author(s):  
Rosa-María Menchón-Lara ◽  
José-Luis Sancho-Gómez ◽  
Andrés Bueno-Crespo
Keyword(s):  
Deep Learning ◽  
Early Stage ◽  
Ultrasound Images ◽  
Carotid Ultrasound

Segmentation of intima media complex from carotid ultrasound images using wind driven optimization technique

2018 ◽  
Vol 40 ◽  
pp. 462-472 ◽  
Author(s):  
Y. Nagaraj ◽  
Pardhu Madipalli ◽  
Jeny Rajan ◽  
P. Krishna Kumar ◽  
A.V. Narasimhadhan
Keyword(s):  
Optimization Technique ◽  
Ultrasound Images ◽  
Carotid Ultrasound

scholarly journals Creating a Simulated Dataset for Training Deep Convolutional Neural Networks for Use in Cardiovascular Photoacoustic Tomography

2020 ◽  
Vol 3 ◽  
Author(s):  
Hayley Chan ◽  
Craig Goergen ◽  
Katherine Leyba
Keyword(s):  
Data Augmentation ◽  
Ex Vivo ◽  
Initial Pressure ◽  
Image Resolution ◽  
Sensor Data ◽  
Photoacoustic Tomography ◽  
Multidimensional Space ◽  
Imaging Method ◽  
Ultrasound Images ◽  
Carotid Ultrasound

Background/Objective: Photoacoustic tomography possesses increasing potential as a non-invasive imaging method that combines optical and acoustic imaging to maximize the visualization of tissue. Determining the composition, orientation, and location of anatomical structures in multidimensional space requires maximizing image resolution and differentiation from noise and reflection artifacts. Using simulations to develop and improve methods for image resolution allows for flexibility and variation of numerous variables.    Methods: Binary masks were created from mouse common carotid ultrasound images using a graphical user interface for MATLAB. With the k-Wave toolbox, we performed time-reversal photoacoustic simulations using the masks. Medium properties for the simulations were assigned for sound speed and density for connective tissue (1540 m/s, 1027 kg/m3) and arterial walls (1569 m/s, 1102 kg/m3). The dataset was augmented through rotational and mirrored transformations and the addition of noise and reflection artifacts via Python open-source software.    Results: A set of 87 binary masks was generated from common carotid ultrasound images. These masks were used to simulate initial pressure distributions through the k-Wave toolbox to reconstruct the structure of the common carotid. Each simulation yielded graphs for initial pressure and sensor distribution, simulated sensor data, reconstructed initial pressure, and a comparison profile between the original and reconstructed pressure. Data augmentation was implemented using the reconstructed pressure output from the 87 simulations, each producing 12 distinct images from rotations and mirroring with the addition of noise and reflection artifacts. The final dataset yielded 1044 images.    Conclusion and Potential Impact: Future work will involve applying this dataset to a neural network to improve photoacoustic quality such that transfer learning can be applied on ex vivo and in vivo datasets. Thus, there is potential for use in diagnostic applications in patients with cardiovascular disease states like atherosclerosis and aneurysms that require high resolution visualization of tissue structure and composition. 

Abstract 509: Accurate Measurement of Carotid Lumen Diameter and Narrowing Utilizing Ultrasound

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Aditya M Sharma ◽  
Tadashi Araki ◽  
Krishna Kumar ◽  
Nobutaka Ikeda ◽  
Francesco Lavra ◽  
...  
Keyword(s):  
Medical Center ◽  
Duplex Ultrasound ◽  
Lumen Diameter ◽  
Ultrasound Images ◽  
Carotid Ultrasound ◽  
Computed Tomographic ◽  
Non Invasive ◽  
Wide Range ◽  
Lumen Narrowing ◽  
Higher Order Derivative

Introduction: Ultrasound is often used for monitoring of carotid disease. In current clinical practice, degree of stenosis is an important predictor to assess stroke risk. Lumen narrowing from plaque is currently measured via techniques such as computed tomographic scan, magnetic resonance angiogram or conventional angiogram. Duplex ultrasound measures degree of stenosis based on peak systolic velocities and other parameters and only provides a wide range of level of stenosis. Pursuing lumen size measurement in ultrasound via manual quantification of lumen diameter is tedious. Furthermore, non-uniformity in plaque growth makes it more challenging and time-consuming. There has been an increasing interest in the automatic and robust delineation of the lumen boundaries of the carotids and to measure the lumen diameter via ultrasound given its non-invasive and safe approach. Methods: Deidentified carotid ultrasound images were obtained on patients retrospectively who underwent carotid ultrasound at Toho University Ohashi Medical Center, Tokyo, Japan. A higher order derivative Gaussian filter is applied on these images to highlight the edges. Using pixel classification, lumen region is detected and lumen boundaries are estimated. Results: Of the 202 patients with common carotid artery images, 155 were males and 47 were females.Mean age 69 ± 15.9 years. Mean HbA1c, LDL, HDL and Cholesterol of patients were 6.28±1.1 mg/dl, 101.27±31.6 mg/dl, 50.26±14.8 mg/dl and 175.04±38 mg/dl, respectively. Specialist trained in carotid ultrasound manually traced lumen diameter. Automated tracing and lumen measurements were obtained. The coefficient of correlation between automated diameter and manual diameter was: 0.88, 0.91 and 0.93. The mean diameter error between automated and manual tracing were: 0.50±0.37 mm, 0.36±0.34 mm and 0.30±0.28 mm. Precision of merit between automated diameter and manual diameter was: 93.28%, 95.33% and 96.32% corresponding to manual tracers. Conclusions: The automated lumen diameter measurement is near real time, quick, accurate, fully automated and reliable to assess carotid lumen diameter and narrowing.

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