Dataset Information

Simulation-based validation for four- dimensional multi-channel ultrasound current source density imaging.

ABSTRACT: Ultrasound current source density imaging (UCSDI), which has application to the heart and brain, exploits the acoustoelectric (AE) effect and Ohm's law to detect and map an electrical current distribution. In this study, we describe 4-D UCSDI simulations of a dipole field for comparison and validation with bench-top experiments. The simulations consider the properties of the ultrasound pulse as it passes through a conductive medium, the electric field of the injected dipole, and the lead field of the detectors. In the simulation, the lead fields of detectors and electric field of the dipole were calculated by the finite element (FE) method, and the convolution and correlation in the computation of the detected AE voltage signal were accelerated using 3-D fast Fourier transforms. In the bench-top experiment, an electric dipole was produced in a bath of 0.9% NaCl solution containing two electrodes, which injected an ac pulse (200 Hz, 3 cycles) ranging from 0 to 140 mA. Stimulating and recording electrodes were placed in a custom electrode chamber made on a rapid prototype printer. Each electrode could be positioned anywhere on an x-y grid (5 mm spacing) and individually adjusted in the depth direction for precise control of the geometry of the current sources and detecting electrodes. A 1-MHz ultrasound beam was pulsed and focused through a plastic film to modulate the current distribution inside the saline-filled tank. AE signals were simultaneously detected at a sampling frequency of 15 MHz on multiple recording electrodes. A single recording electrode is sufficient to form volume images of the current flow and electric potentials. The AE potential is sensitive to the distance from the dipole, but is less sensitive to the angle between the detector and the dipole. Multi-channel UCSDI potentially improves 4-D mapping of bioelectric sources in the body at high spatial resolution, which is especially important for diagnosing and guiding treatment of cardiac and neurologic disorders, including arrhythmia and epilepsy.

SUBMITTER: Wang Z

PROVIDER: S-EPMC4406770 | biostudies-other | 2014 Mar

REPOSITORIES: biostudies-other

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Publications

Simulation-based validation for four- dimensional multi-channel ultrasound current source density imaging.

Wang Zhaohui Z Witte Russell S RS

IEEE transactions on ultrasonics, ferroelectrics, and frequency control 20140301 3

Ultrasound current source density imaging (UCSDI), which has application to the heart and brain, exploits the acoustoelectric (AE) effect and Ohm's law to detect and map an electrical current distribution. In this study, we describe 4-D UCSDI simulations of a dipole field for comparison and validation with bench-top experiments. The simulations consider the properties of the ultrasound pulse as it passes through a conductive medium, the electric field of the injected dipole, and the lead field o ...[more]

PMID: 24569247

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Project description:Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

Dataset Information

Simulation-based validation for four- dimensional multi-channel ultrasound current source density imaging.

Publications

Simulation-based validation for four- dimensional multi-channel ultrasound current source density imaging.

Similar Datasets

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