Dataset Information

Estimating cell concentration in three-dimensional engineered tissues using high frequency quantitative ultrasound.

ABSTRACT: Histology and biochemical assays are standard techniques for estimating cell concentration in engineered tissues. However, these techniques are destructive and cannot be used for longitudinal monitoring of engineered tissues during fabrication processes. The goal of this study was to develop high-frequency quantitative ultrasound techniques to nondestructively estimate cell concentration in three-dimensional (3-D) engineered tissue constructs. High-frequency ultrasound backscatter measurements were obtained from cell-embedded, 3-D agarose hydrogels. Two broadband single-element transducers (center frequencies of 30 and 38 MHz) were employed over the frequency range of 13-47 MHz. Agarose gels with cell concentrations ranging from 1 × 10(4) to 1 × 10(6) cells mL(-1) were investigated. The integrated backscatter coefficient (IBC), a quantitative ultrasound spectral parameter, was calculated and used to estimate cell concentration. Accuracy and precision of this technique were analyzed by calculating the percent error and coefficient of variation of cell concentration estimates. The IBC increased linearly with increasing cell concentration. Axial and lateral dimensions of regions of interest that resulted in errors of less than 20% were determined. Images of cell concentration estimates were employed to visualize quantitatively regional differences in cell concentrations. This ultrasound technique provides the capability to rapidly quantify cell concentration within 3-D tissue constructs noninvasively and nondestructively.

SUBMITTER: Mercado KP

PROVIDER: S-EPMC4418440 | biostudies-literature | 2014 Jun

REPOSITORIES: biostudies-literature

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Estimating cell concentration in three-dimensional engineered tissues using high frequency quantitative ultrasound.

Mercado Karla P KP Helguera María M Hocking Denise C DC Dalecki Diane D

Annals of biomedical engineering 20140314 6

Histology and biochemical assays are standard techniques for estimating cell concentration in engineered tissues. However, these techniques are destructive and cannot be used for longitudinal monitoring of engineered tissues during fabrication processes. The goal of this study was to develop high-frequency quantitative ultrasound techniques to nondestructively estimate cell concentration in three-dimensional (3-D) engineered tissue constructs. High-frequency ultrasound backscatter measurements w ...[more]

PMID: 24627179

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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

Estimating cell concentration in three-dimensional engineered tissues using high frequency quantitative ultrasound.

Publications

Estimating cell concentration in three-dimensional engineered tissues using high frequency quantitative ultrasound.

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