Estimation of cell concentration using high-frequency ultrasonic backscattering

Abstract Cell concentration is a crucial quantity for both clinical diagnostic examinations and cell culture studies. However, typical modalities for cell concentration measurements are either time-consuming or not cost-effective. In the present study, cell concentration is estimated using high-frequency ultrasonic backscattering. Validation tests indicate that the proposed method can differentiate red blood cells (RBCs) of various hematocrits. A 50-MHz ultrasound system with appropriate sensitivity is utilized to estimate cell concentrations from a small volume of RBCs suspended in saline, with hematocrits ranging from 1.66 × 10 -4 to 10%, and fibroblasts, with concentrations ranging from 2 × 10 4 to 128 × 10 4 cells/mL. The backscatter strength and statistical distribution, characterized by the Nakagami parameter, are calculated from gated signals for quantitatively assessing the samples. Results show that the backscatter strength of RBCs linearly increases with increasing hematocrit level in the hematocrit range of 3 to 10%, which agrees well with results of previous studies. The backscatter strength of RBCS has an exponential relationship with the hematocrit level in the hematocrit range of 1.66 × 10 -4 to 3%. The corresponding Nakagami parameter is sensitive to electronic noise as long as the signal-to-noise ratio decreasing follows with the decrease of RBC hematocrits at the concentration lower than 0.85%. The backscatter strength of fibroblasts exponentially increases with increasing fibroblasts concentration, which is consistent with results obtained from typical optical density measurements. A linear relationship, with correlation coefficient of 0.99, between the results of ultrasonic backscattering and those of the optical density measurements is established. High-frequency ultrasonic backscattering can be applied to sensitively estimate the concentrations of small volumes of cells

Window-Modulated Compounding Nakagami Parameter Ratio Approach for Assessing Muscle Perfusion with Contrast-Enhanced Ultrasound Imaging

The assessment of microvascular perfusion is essential for the diagnosis of a specific muscle disease. In comparison with the current available medical modalities, the contrast-enhanced ultrasound imaging is the simplest and fastest means for probing the tissue perfusion. Specifically, the perfusion parameters estimated from the ultrasound time-intensity curve (TIC) and statistics-based time–Nakagami parameter curve (TNC) approaches were found able to quantify the perfusion. However, due to insufficient tolerance on tissue clutters and subresolvable effects, these approaches remain short of reproducibility and robustness. Consequently, the window-modulated compounding (WMC) Nakagami parameter ratio imaging was proposed to alleviate these effects, by taking the ratio of WMC Nakagami parameters corresponding to the incidence of two different acoustic pressures from an employed transducer. The time–Nakagami parameter ratio curve (TNRC) approach was also developed to estimate perfusion parameters. Measurements for the assessment of muscle perfusion were performed from the flow phantom and animal subjects administrated with a bolus of ultrasound contrast agents. The TNRC approach demonstrated better sensitivity and tolerance of tissue clutters than those of TIC and TNC. The fusion image with the WMC Nakagami parameter ratio and B-mode images indicated that both the tissue structures and perfusion properties of ultrasound contrast agents may be better discerned

1st Global Conference on Biomedical Engineering & 9th Asian-Pacific Conference on Medical and Biological Engineering

This volume presents the proceedings of the 9th Asian-Pacific Conference on Medical and Biological Engineering (APCMBE 2014). The proceedings address a broad spectrum of topics from Bioengineering and Biomedicine, like Biomaterials, Artificial Organs, Tissue Engineering, Nanobiotechnology and Nanomedicine, Biomedical Imaging, Bio MEMS, Biosignal Processing, Digital Medicine, BME Education. It helps medical and biological engineering professionals to interact and exchange their ideas and experiences

Integrated Image Sensor and Light Convolutional Neural Network for Image Classification

Deep learning has accomplished huge success in computer vision applications such as self-driving vehicles, facial recognition, and controlling robots. A growing need for deploying systems on resource-limited or resource-constrained environments such as smart cameras, autonomous vehicles, robots, smartphones, and smart wearable devices drives one of the current mainstream developments of convolutional neural networks: reducing model complexity but maintaining fine accuracy. In this study, the proposed efficient light convolutional neural network (ELNet) comprises three convolutional modules which perform ELNet using fewer computations, which is able to be implemented in resource-constrained hardware equipment. The classification task using CIFAR-10 and CIFAR-100 datasets was used to verify the model performance. According to the experimental results, ELNet reached 92.3% and 69%, respectively, in CIFAR-10 and CIFAR-100 datasets; moreover, ELNet effectively lowered the computational complexity and parameters required in comparison with other CNN architectures

Interval Type-2 Neural Fuzzy Controller-Based Navigation of Cooperative Load-Carrying Mobile Robots in Unknown Environments

In this paper, a navigation method is proposed for cooperative load-carrying mobile robots. The behavior mode manager is used efficaciously in the navigation control method to switch between two behavior modes, wall-following mode (WFM) and goal-oriented mode (GOM), according to various environmental conditions. Additionally, an interval type-2 neural fuzzy controller based on dynamic group artificial bee colony (DGABC) is proposed in this paper. Reinforcement learning was used to develop the WFM adaptively. First, a single robot is trained to learn the WFM. Then, this control method is implemented for cooperative load-carrying mobile robots. In WFM learning, the proposed DGABC performs better than the original artificial bee colony algorithm and other improved algorithms. Furthermore, the results of cooperative load-carrying navigation control tests demonstrate that the proposed cooperative load-carrying method and the navigation method can enable the robots to carry the task item to the goal and complete the navigation mission efficiently

Multiple Convolutional Neural Networks Fusion Using Improved Fuzzy Integral for Facial Emotion Recognition

Facial expressions are indispensable in human cognitive behaviors since it can instantly reveal human emotions. Therefore, in this study, Multiple Convolutional Neural Networks using Improved Fuzzy Integral (MCNNs-IFI) were proposed for recognizing facial emotions. Since effective facial expression features are difficult to design; deep learning CNN is used in the study. Each CNN has its own advantages and disadvantages, thus combining multiple CNNs can yield superior results. Moreover, multiple CNNs combined with improved fuzzy integral, in which its fuzzy density value is optimized through particle swarm optimization (PSO), overcomes the majority decision drawback in the traditional voting method. Two Multi-PIE and CK+ databases and three main CNN structures, namely AlexNet, GoogLeNet, and LeNet, were used in the experiments. To verify the results, a cross-validation method was used, and experimental results indicated that the proposed MCNNs-IFI exhibited 12.84% higher accuracy than that of the three CNNs

Quantitative Assessment of First Annular Pulley and Adjacent Tissues Using High-Frequency Ultrasound

Due to a lack of appropriate image resolution, most ultrasound scanners are unable to sensitively discern the pulley tissues. To extensively investigate the properties of the A1 pulley system and the surrounding tissues for assessing trigger finger, a 30 MHz ultrasound system was implemented to perform in vitro experiments using the hypodermis, A1 pulley, and superficial digital flexor tendon (SDFT) dissected from cadavers. Ultrasound signals were acquired from both the transverse and sagittal planes of each tissue sample. The quantitative ultrasonic parameters, including sound speed, attenuation coefficient, integrated backscatter (IB) and Nakagami parameter (m), were subsequently estimated to characterize the tissue properties. The results demonstrated that the acquired ultrasound images have high resolution and are able to sufficiently differentiate the variations of tissue textures. Moreover, the attenuation slope of the hypodermis is larger than those of the A1 pulley and SDFT. The IB of A1 pulley is about the same as that of the hypodermis, and is very different from SDFT. The m parameter of the A1 pulley is also very different from those of hypodermis and SDFT. This study demonstrated that high-frequency ultrasound images in conjunction with ultrasonic parameters are capable of characterizing the A1 pulley system and surrounding tissues

Cross-Sectional Nakagami Images in Passive Stretches Reveal Damage of Injured Muscles

Muscle strain is still awanting a noninvasive quantitatively diagnosis tool. High frequency ultrasound (HFU) improves image resolution for monitoring changes of tissue structures, but the biomechanical factors may influence ultrasonography during injury detection. We aim to illustrate the ultrasonic parameters to present the histological damage of overstretched muscle with the consideration of biomechanical factors. Gastrocnemius muscles from mice were assembled and ex vivo passive stretching was performed before or after injury. After injury, the muscle significantly decreased mechanical strength. Ultrasonic images were obtained by HFU at different deformations to scan in cross and longitudinal orientations of muscle. The ultrasonography was quantified by echogenicity and Nakagami parameters (NP) for structural evaluation and correlated with histological results. The injured muscle at its original length exhibited decreased echogenicity and NP from HFU images. Cross-sectional ultrasonography revealed a loss of correlation between NP and passive muscle stretching that suggested a special scatterer pattern in the cross section of injured muscle. The independence of NP during passive stretching of injured muscle was confirmed by histological findings in ruptured collagen fibers, decreased muscle density, and increased intermuscular fiber space. Thus, HFU analysis of NP in cross section represents muscle injury that may benefit the clinical diagnosis

Role of Excessive Autophagy Induced by Mechanical Overload in Vein Graft Neointima Formation: Prediction and Prevention.

Little is known regarding the interplays between the mechanical and molecular bases for vein graft restenosis. We elucidated the stenosis initiation using a high-frequency ultrasonic (HFU) echogenicity platform and estimated the endothelium yield stress from von-Mises stress computation to predict the damage locations in living rats over time. The venous-arterial transition induced the molecular cascades for autophagy and apoptosis in venous endothelial cells (ECs) to cause neointimal hyperplasia, which correlated with the high echogenicity in HFU images and the large mechanical stress that exceeded the yield strength. The ex vivo perfusion of arterial laminar shear stress to isolated veins further confirmed the correlation. EC damage can be rescued by inhibiting autophagy formation using 3-methyladenine (3-MA). Pretreatment of veins with 3-MA prior to grafting reduced the pathological increases of echogenicity and neointima formation in rats. Therefore, this platform provides non-invasive temporal spatial measurement and prediction of restenosis after venous-arterial transition as well as monitoring the progression of the treatments