Ultrasonic backscatter characterization of cancellous bone using a general Nakagami statistical model

The goal of this study is to analyze the statistics of the backscatter signal from bovine cancellous bone using a Nakagami model and to evaluate the feasibility of Nakagami-model parameters for cancellous bone characterization. Ultrasonic backscatter measurements were performed on 24 bovine cancellous bone specimens in vitro and the backscatter signals were compensated for the frequency-dependent attenuation prior to the envelope detection. The statistics of the backscatter envelope were modeled using the Nakagami distribution. Our results reveal that the backscatter envelope mainly followed pre-Rayleigh distributions, and the deviations of the backscatter envelope from Rayleigh distribution decreased with increasing bone density. The Nakagami shape parameter(i.e., m) was significantly correlated with bone densities(R = 0.78–0.81, p < 0.001) and trabecular microstructures(|R| = 0.46–0.78, p < 0.05). The scale parameter(i.e.,?) and signal-to-noise ratio(SNR) also yielded significant correlations with bone density and structural features. Multiple linear regressions showed that bone volume fraction(BV/TV) was the main predictor of the Nakagami parameters,and microstructure produced significantly independent contribution to the prediction of Nakagami distribution parameters,explaining an additional 10.2% of the variance at most. The in vitro study showed that statistical parameters derived with Nakagami model might be useful for cancellous bone characterization, and statistical analysis has potential for ultrasonic backscatter bone evaluation.

Deep learning method for cell count from transmitted-light microscope

Automatic cell counting provides an effective tool for medical research and diagnosis.Currently,cell counting can be completed by transmitted-light microscope,however,it requires expert knowledge and the counting accuracy which is unsatisfied for overlapped cells.Further,the image-translation-based detection method has been proposed and the potential has been shown to accomplish cell counting from transmitted-light microscope,automatically and effectively.In this work,a new deep-learning(DL)-based two-stage detection method(cGAN-YOLO)is designed to further enhance the performance of cell counting,which is achieved by combining a DL-based fluorescent image translation model and a DL-based cell detection model.The various results show that cGAN-YOLO can effectively detect and count some different types of cells from the acquired transmitted-light microscope images.Compared with the previously reported YOLO-based one-stage detection method,high recognition accuracy(RA)is achieved by the cGAN-YOLO method,with an improvement of 29.80%.Furthermore,we can also observe that cGAN-YOLO obtains an improvement of 12.11%in RA compared with the previously reported image-translation-based detection method.In a word,cGAN-YOLO makes it possible to implement cell counting directly from the experimental acquired transmitted-light microscopy images with high flexibility and performance,which extends the applicability in clinical research.

The Protocol of Ultrasonic Backscatter Measurements of Musculoskeletal Properties

This study aims to introduce the protocol for ultrasonic backscatter measurements of musculoskeletal properties based on a novel ultrasonic backscatter bone diagnostic(UBBD)instrument.Dual-energy X-ray absorptiometry(DXA)can be adopted to measure bone mineral density(BMD)in the hip,spine,legs and the whole body.The muscle and fat mass in the legs and the whole body can be also calculated by DXA body composition analysis.Based on the proposed protocol for backscatter measurements by UBBD,ultrasonic backscatter signals can be measured in vivo,deriving three backscatter parameters[apparent integral backscatter(AIB),backscatter signal peak amplitude(BSPA)and the corresponding arrival time(BSPT)].AIB may provide important diagnostic information about bone properties.BSPA and BSPT may be important indicators of muscle and fat properties.The standardized backscatter measurement protocol of the UBBD instrument may have the potential to evaluate musculoskeletal characteristics,providing help for promoting the application of the backscatter technique in the clinical diagnosis of musculoskeletal disorders(MSDs),such as osteoporosis and muscular atrophy.

An Ultrasonic Backscatter Instrument for Cancellous Bone Evaluation in Neonates

Ultrasonic backscatter technique has shown promise as a noninvasive cancellous bone assessment tool. A novel ultrasonic backscatter bone diagnostic(UBBD) instrument and an in vivo application for neonatal bone evaluation are introduced in this study. The UBBD provides several advantages, including noninvasiveness, nonionizing radiation, portability, and simplicity. In this study, the backscatter signal could be measured within 5 s using the UBBD. Ultrasonic backscatter measurements were performed on 467 neonates(268 males and 199 females) at the left calcaneus. The backscatter signal was measured at a central frequency of 3.5 MHz. The delay(T_1) and duration(T_2) of the backscatter signal of interest(SOI) were varied, and the apparent integrated backscatter(AIB), frequency slope of apparent backscatter(FSAB), zero frequency intercept of apparent backscatter(FIAB), and spectral centroid shift(SCS) were calculated. The results showed that the SOI selection had a direct influence on cancellous bone evaluation. The AIB and FIAB were positively correlated with the gestational age(|R| up to 0.45, P < 0.001) when T_1 was short(< 8 μs), while negative correlations(|R| up to 0.56, P < 0.001) were commonly observed for T_1 > 10 μs. Moderate positive correlations(|R| up to 0.45, P < 0.001) were observed for FSAB and SCS with gestational age when T_1 was long(> 10 μs). The T_2 mainly introduced fluctuations in the observed correlation coefflcients. The moderate correlations observed with UBBD demonstrate the feasibility of using the backscatter signal to evaluate neonatal bone status. This study also proposes an explicit standard for in vivo SOI selection and neonatal cancellous bone assessment.

High-resolution bone microstructure imaging based on ultrasonic frequency-domain full-waveform inversion

The main challenge in bone ultrasound imaging is the large acoustic impedance contrast and sound velocity differences between the bone and surrounding soft tissue. It is difficult for conventional pulse-echo modalities to give accurate ultrasound images for irregular bone boundaries and microstructures using uniform sound velocity assumption rather than getting a prior knowledge of sound speed. To overcome these limitations, this paper proposed a frequency-domain fullwaveform inversion(FDFWI) algorithm for bone quantitative imaging utilizing ultrasonic computed tomography(USCT).The forward model was calculated in the frequency domain by solving the full-wave equation. The inverse problem was solved iteratively from low to high discrete frequency components via minimizing a cost function between the modeled and measured data. A quasi-Newton method called the limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm(L-BFGS) was utilized in the optimization process. Then, bone images were obtained based on the estimation of the velocity and density. The performance of the proposed method was verified by numerical examples, from tubular bone phantom to single distal fibula model, and finally with a distal tibia-fibula pair model. Compared with the high-resolution peripheral quantitative computed tomography(HR-p QCT), the proposed FDFWI can also clearly and accurately presented the wavelength scaled pores and trabeculae in bone images. The results proved that the FDFWI is capable of reconstructing high-resolution ultrasound bone images with sub-millimeter resolution. The parametric bone images may have the potential for the diagnosis of bone disease.

Three-dimensional ultrasound subwavelength arbitrary focusing with broadband sparse metalens

Ultrasound focusing in three-dimensional(3 D)space is of crucial and enduring significance in a variety of biomedical and industrial applications.Conventional ultrasound focusing based on active phase array or passive geometry of bulky size is unable to realize the 3 D arbitrary focusing with subwavelength resolution.Acoustic metamaterial of complex deep-subwavelength microstructure has facilitated the advanced airborne-sound-focusing but is inevitably not applicable for underwater ultrasound,restricted by the law between the multi-modes coupling/thermal viscosity and the feature size of the structure.Here,we aim to circumvent the restriction by increasing the feature size of the metamaterial while keeping the compact overall geometry,and realize the robust subwavelength ultrasound focusing with the sparse metalens of the wavelength-scale meta-atom.We theoretically propose and demonstrate numerically and experimentally the broadband arbitrary ultrasound focusing in 3 D space.The axial and off-axis ultrasound focusing with the subwavelength resolution(FWHM<0.58λ)are achieved by the spatially sparse and compact metalens within one-octave bandwidth.With advantages of 3 D freewheeling focusing,subwavelength resolution,spatial sparsity,geometric simplicity,and broadband,the sparse metalens would offer more initiatives to advanced researches in ultrasound focusing and empower applications such as precise biomedical imaging and therapy,nondestructive evaluation,integrated and multiplexed ultrasound devices.

光声骨检测研究进展

光声成像及检测技术兼具生物组织对光的选择吸收特性及超声波的穿透深度优势,可提供生物组织的宏观生理结构和微观分子层面的影像信息,在医学检测中有着巨大的应用潜力.其中,光声骨成像及检测技术是光声成像临床应用领域的重要分支.然而,骨组织是固液两相复杂介质(固相为矿化的骨小梁网络,液相为嵌入的骨髓),这种各向异性的非均匀结构使得基于光声技术的骨组织表征方法面临很大挑战.本文系统讨论了面向复杂骨组织的光声检测及成像机制,包括光声在骨组织中的产生及传播理论、光声谱分析方法、光声骨成像方法及临床应用研究.针对现有的技术研究现状,分析了光声骨成像及检测领域当前所面临的挑战,展望了未来的发展趋势.

Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network

Fluorescence labeling and imaging provide an opportunity to observe the structure of biological tissues,playing a crucial role in the field of histopathology.However,when labeling and imaging biological tissues,there are still some challenges,e.g.,time-consuming tissue preparation steps,expensive reagents,and signal bias due to photobleaching.To overcome these limitations,we present a deep-learning-based method for fluorescence translation of tissue sections,which is achieved by conditional generative adversarial network(cGAN).Experimental results from mouse kidney tissues demonstrate that the proposed method can predict the other types of fluorescence images from one raw fluorescence image,and implement the virtual multi-label fluorescent staining by merging the generated different fluorescence images as well.Moreover,this proposed method can also effectively reduce the time-consuming and laborious preparation in imaging processes,and further saves the cost and time.

Feasibility study for bone health assessment based on photoacoustic imaging method

This study investigated the feasibility of photoacoustic(PA) imaging of bone and characterization of bone features. By conducting the experiments on bovine femoral heads ex vivo, the light and ultrasonic penetration in bones was studied, together with the depth of PA imaging and measurement in bones. Then, the possibility of three-dimensional(3 D) PA imaging of bones by raster scanning of the focusing transducer was studied. The micro-computerized tomography images of the bovine ribs with and without ethylenediaminetetraacetic acid(EDTA) treatment indicated that the 3 D PA images could present the changes of bone microstructure resulting from the EDTA treatment. By using PA spectral analysis, the bone samples with and without the treatment of EDTA solution can be distinguished, and the microstructures can be characterized. This study was based on the bovine bone whose size is comparable to human bones, suggesting that PA technology can be used as a novel bone diagnostic technique.