High Frequency Ultrasound Imaging Research Articles

Murine cardiac fibrosis localization using adaptive Bayesian cardiac strain imaging in vivo

An adaptive Bayesian regularized cardiac strain imaging (ABR-CSI) algorithm for in vivo murine myocardial function assessment is presented. We report on 31 BALB/CJ mice (n = 17 females, n = 14 males), randomly stratified into three surgical groups: myocardial infarction (MI, n = 10), ischemia–reperfusion (IR, n = 13) and control (sham, n = 8) imaged pre-surgery (baseline- BL), and 1, 2, 7 and 14 days post-surgery using a high frequency ultrasound imaging system (Vevo 2100). End-systole (ES) radial and longitudinal strain images were used to generate cardiac fibrosis maps using binary thresholding. Percentage fibrotic myocardium (PFM) computed from regional fibrosis maps demonstrated statistically significant differences post-surgery in scar regions. For example, the MI group had significantly higher PFMRadial (%) values in the anterior mid region (p = 0.006) at Day 14 (n = 8, 42.30 ± 14.57) compared to BL (n = 12, 1.32 ± 0.85). A random forest classifier automatically detected fibrotic regions from ground truth Masson’s trichrome stained histopathology whole slide images. Both PFMRadial (r = 0.70) and PFMLongitudinal (r = 0.60) results demonstrated strong, positive correlation with PFMHistopathology (p < 0.001).

Genetic mouse models of autism spectrum disorder present subtle heterogenous cardiac abnormalities.

Autism spectrum disorder (ASD) and congenital heart disease (CHD) are linked on a functional and genetic level. Most work has investigated CHD‐related neurodevelopmental abnormalities. Cardiac abnormalities in ASD have been less studied. We investigated the prevalence of cardiac comorbidities relative to ASD genetic contributors. Using high frequency ultrasound imaging, we screened 9 ASD‐related genetic mouse models (Arid1b (+/−), Chd8 (+/−), 16p11.2 (deletion), Sgsh (+/−), Sgsh (−/−), Shank3 Δexon 4–9 (+/−), Shank3 Δexon 4–9 (−/−), Fmr1 (−/−), Vps13b (+/−)), and pooled wild‐type littermates (WTs). We measured heart rate (HR), aorta diameter (AoD), thickness and thickening of the left‐ventricular (LV) anterior and posterior walls, LV chamber diameter, fractional shortening, stroke volume and cardiac output, mitral inflow Peak E and A velocity ratio, ascending aorta velocity time integral (VTI). Mutant groups presented small‐scale alterations in cardiac structure and function compared to WTs (LV anterior wall thickness and thickening, chamber diameter and fractional shortening, HR). A greater number of significant differences was observed among mutant groups than between mutant groups and WTs. Mutant groups differed primarily in structural measures (LV chamber diameter and anterior wall thickness, HR, AoD). The mutant groups with most differences to WTs were 16p11.2 (deletion), Fmr1 (−/−), Arid1b (+/−). The mutant groups with most differences from other mutant groups were 16p11.2 (deletion), Sgsh (+/−), Fmr1 (−/−). Our results recapitulate the associated clinical findings. The characteristic ASD heterogeneity was recapitulated in the cardiac phenotype. The type of abnormal measures (morphological, functional) can highlight common underlying mechanisms. Clinically, knowledge of cardiac abnormalities in ASD can be essential as even non‐lethal abnormalities impact normal development.Lay SummaryAutism spectrum disorder (ASD) and congenital heart disease (CHD) are linked functionally and genetically. ASD cardiac phenotyping is limited. We assessed the cardiac phenotype of 9 ASD‐related mouse models. We found subtle heterogenous cardiac abnormalities compared to controls, with more differences within ASD than between ASD and controls, mirroring clinical findings. Clinically, knowing the cardiac abnormalities in ASD is vital as even non‐lethal cardiac abnormalities can impact development.

Anatomy and ultrasound imaging of the tibial collateral ligament: A narrative review.

Medial knee pain is commonplace in clinical practice and ultrasound assessment of the tibial collateral ligament-medial meniscus complex is increasingly becoming a valuable examination tool in the outpatient setting. In the pertinent literature, basic sonographic protocols have been proposed to evaluate the medial compartment of the knee joint. Using high-frequency ultrasound probes and high-level ultrasound machines; we matched the histo-anatomical features of the tibial collateral ligament-medial meniscus complex and its different sonographic patterns in physiological/pathological conditions to define a standardized (layer-by-layer) sonographic approach. Moreover, high-sensitive power Doppler assessments have also been performed to evaluate the nearby microcirculation. Modern ultrasound equipment appears to provide optimal "sonographic dissection" of the tibial collateral ligament-medial meniscus complex for its various physiological/pathological patterns. Likewise, high-sensitive power Doppler allows clear visualization of the microcirculation as regards the local ligamentous and capsular structures. In clinical practice, using adequate technological equipment, a detailed sonographic assessment of the tibial collateral ligament-medial meniscus complex can be performed. High-frequency B-mode ultrasound imaging and high-sensitive power Doppler perfusion patterns can be matched/integrated with the clinical findings to optimize the management of patients with medial knee pain.

Design of a Barometer-Based Pulse-Taking Device With In Vivo Validation Against High-Frequency Ultrasound Pulse Wave Imaging

Wrist-site pulse-sensing devices have drawn increasing attention for daily monitoring of human body conditions in the context of western medicine and modernization of traditional Chinese medicine (TCM) palpation. Existing tactile pulse sensors are deemed to only access the superficial tissue layer as surface detectors but have not been validated against medical imaging of the radial artery and its pulsation. We hereby proposed to employ high-frequency (15.6MHz) and high frame-rate (1000fps) ultrasound radial pulse wave imaging (US-rPWI) to substantiate human radial pulse-taking. We first presented an easy-to-fabricate, cost-effective pulse-sensing prototype based on a commercial off-the-shelf (COTS) barometer. The prototype performance was evaluated using a homemade pressurized vessel-mimicking phantom and 30 different <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vitro</i> pulses outputted from a TCM palpation training machine. Phantom experiments evidenced accurate portrayal of dynamic pressure waveforms (mean distortion ratio: 3.38±0.07%, compared with the ground truth). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In vivo</i> validation on human pre- and post- prandial pulses was further performed. US-rPWI revealed significantly diminished pulse amplitudes and gradual waveform deformation from the arterial wall to the skin layer. Nevertheless, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in vivo</i> pulse waveforms detected at the skin surface by the prototype were highly correlated (correlation coefficient: 0.926±0.07) with those obtained from the radial arterial wall by US-rPWI. Moreover, the changes in post-prandial pulse waveforms detected by both the prototype and US-rPWI were in excellent agreement with those in TCM literature, thus demonstrating the sensitivity of human pulse-taking in quantitative assessment of health conditions at the point of care and providing more clinical evidence for the modernization of TCM sphygmology.

Fully Convolutional Neural Network Deep Learning Model Fully in Patients with Type 2 Diabetes Complicated with Peripheral Neuropathy by High-Frequency Ultrasound Image.

This study was aimed at exploring the diagnostic value of high-frequency ultrasound imaging based on a fully convolutional neural network (FCN) for peripheral neuropathy in patients with type 2 diabetes (T2D). A total of 70 patients with T2D mellitus were selected and divided into a lesion group (n = 31) and a nonlesion group (n = 39) according to the type of peripheral neuropathy. In addition, 30 healthy people were used as controls. Hypervoxel-based and FCN-based high-frequency ultrasound images were used to examine the three groups of patients to evaluate their diagnostic performance and to compare the changes of peripheral nerves and ultrasound characteristics. The results showed that the Dice coefficient (92.7) and mean intersection over union (mIOU) (82.6) of the proposed algorithm after image segmentation were the largest, and the Hausdorff distance (7.6) and absolute volume difference (AVD) (8.9) were the smallest. The high-frequency ultrasound based on the segmentation algorithm showed higher diagnostic accuracy (94.0% vs. 86.0%), sensitivity (87.1% vs. 67.7%), specificity (97.1% vs. 94.2%), positive predictive value (93.1% vs. 86.7%), and negative predictive value (94.4% vs. 84.0%) (P < 0.05). There were significant differences in the detection values of the three major nerve segments of the upper limbs in the control group, the lesion group, and the nonlesion group (P < 0.05). Compared with the nonlesion group, the patients in the lesion group were more likely to have reduced nerve bundle echo, blurred reticular structure, thickened epineurium, and unclear borders of adjacent tissues (P < 0.05). In summary, the high-frequency ultrasound processed by the algorithm proposed in this study showed a high diagnostic value for peripheral neuropathy in T2D patients, and high-frequency ultrasound can be used to evaluate the morphological changes of peripheral nerves in T2D patients.

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images.

The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.

Low frequency nanobubble-enhanced ultrasound mechanotherapy for noninvasive cancer surgery.

Scaling down the size of microbubble contrast agents to the nanometer level holds the promise for noninvasive cancer therapy. However, the small size of nanobubbles limits the obtained bioeffects as a result of ultrasound cavitation, when operating near the nanobubble resonance frequency. Here we show that coupled with low energy insonation at a frequency of 80 kHz, well below the resonance frequency of these agents, nanobubbles serve as noninvasive therapeutic warheads that trigger potent mechanical effects in tumors following a systemic injection. We demonstrate these capabilities in tissue mimicking phantoms, where a comparison of the acoustic response of micro- and nano-bubbles after insonation at a frequency of 250 or 80 kHz revealed that higher pressures were needed to implode the nanobubbles compared to microbubbles. Complete nanobubble destruction was achieved at a mechanical index of 2.6 for the 250 kHz insonation vs. 1.2 for the 80 kHz frequency. Thus, the 80 kHz insonation complies with safety regulations that recommend operation below a mechanical index of 1.9. In vitro in breast cancer tumor cells, the cell viability was reduced to 17.3 ± 1.7% of live cells. In vivo, in a breast cancer tumor mouse model, nanobubble tumor distribution and accumulation were evaluated by high frequency ultrasound imaging. Finally, nanobubble-mediated low frequency insonation of breast cancer tumors resulted in effective mechanical tumor ablation and tumor tissue fractionation. This approach provides a unique theranostic platform for safe, noninvasive and low energy tumor mechanotherapy.

Clinical Value and Imaging Features of Bedside High-Frequency Ultrasound Imaging in the Diagnosis of Neonatal Pneumonia.

The aim is to solve the problem of the urgent need of a nonradiation, noninvasive, and simple-to-operate diagnostic method for neonatal pneumonia that can indicate the severity of the disease and dynamically monitor the outcome of the disease. The authors propose a bedside high-frequency ultrasound technique based on methods for evaluation in the detection and treatment of neonatal pneumonia. The results obtained are as follows: the sensitivity of neonatal lung ultrasound in the diagnosis of neonatal pneumonia was 96.6%, the specificity was 93.3%, the positive predictive value was 93.5%, and the negative predictive value was 96.5%. The sensitivity of chest X-ray in the diagnosis of neonatal pneumonia was 93.3%. Compared with the lung ultrasound and chest X-ray in the diagnosis of neonatal pneumonia, the two had a good correlation. The neonatal respiratory score was positively correlated with the lung ultrasound score, and the higher the lung ultrasound score, the more severe the disease. The score decreased by 35% after 3 days of treatment and 68% after 7 days of treatment, indicating that the lung high-frequency ultrasound score can be very effective in characterizing the treatment situation. It has been demonstrated that the lung ultrasound can be used as an imaging method for the diagnosis of neonatal pneumonia. The higher the lung ultrasound score, the more severe the disease, and the lung ultrasound score was positively correlated with the disease severity. With dynamic monitoring of the lung ultrasound and the gradual improvement of clinical symptoms after treatment, the lung ultrasound score gradually decreased; therefore, the lung ultrasound can be used for re-examination of neonatal pneumonia to evaluate the treatment effect and guidance.

Abstract LBA051: Importance of drug osmotic pressure and viscosity for efficient drug delivery using lymphatic drug delivery system

Abstract Metastasis is known to be the prime cause of cancer associated fatality. It is therefore imperative to treat and prevent metastasis in order to curb cancer-associated morbidity and mortality. Metastatic lymph nodes (MLNs) are known to be crucial diagnostic and prognostic indicators. Furthermore, as per the lymph node mediated hematogenous theory, due to extant communication between the lymphatic network and the venous system, the lymphatic network facilitates the passage and subsequent colonization of tumor cells to distant sites, thus, serving as a starting point for distant metastasis even during the initial stages. Treatment of MLNs, therefore, is imperative. Proposed in 2015, the Lymphatic Drug Delivery System (LDDS) is a novel and promising drug delivery system for the treatment of MLNs that overcomes the limitations of conventional chemotherapy. Unlike systemic chemotherapy, LDDS entails a direct intranodal drug administration to sentinel lymph node, post its identification using radioisotopes and fluorescent molecules, in order to treat it and its downstream lymph nodes. Previous studies have documented the superior efficacy of LDDS as a MLN management strategy as compared to conventional therapy. Higher drug delivery to target sites and prolonged retention with lower systemic toxicity, and thereby stronger anti-tumor effect have been reported to have been achieved upon drug administration via LDDS. In the present study, MXH10/Mo/lpr mice and FM3A-Luc, mouse mammary carcinoma cells expressing the firefly luciferase gene, have been utilized for the establishment of a mouse model of Lymph node metastasis (LNM) to investigate the effect of drug osmotic pressure and viscosity on therapeutic efficacy of the LDDS. Two lymph nodes, exhibiting systemic lymphadenopathy, in the subiliac and axillary region, the subiliac lymph node (SiLN) and proper axillary lymph node (PALN) have been used to model a network of interconnected human lymph nodes. Drug solution of varying osmotic pressure and viscosity were administered via LDDS to the murine model of LNM and subsequently therapeutic efficacy was monitored using the high frequency ultrasound system, VEVO and bioluminescent imaging system, IVIS, and confirmed through histology. The study revealed an optimized window of drug osmotic pressure and viscosity (π = 1000 - 2000 kPa; µ = 0.9 - 11.5 mPa·s.) where a significantly stronger anti-tumor response was observed. Venturing below or beyond the optimized range results in decreased therapeutic efficacy. The study therefore concluded that drug osmotic pressure and viscosity are important fluid parameters that require careful consideration prior to preparation of drug cocktails as they can severely impact the prognosis. The findings of this study have the potential for translation to clinics and we anticipate that these results can be replicated in clinical trials, positively altering the fate of many cancer patients. Citation Format: Radhika Mishra, Ariunbuyan Sukhbaatar, Sora Shouta, Maya Sakamoto, Shiro Mori, Tetsuya Kodama. Importance of drug osmotic pressure and viscosity for efficient drug delivery using lymphatic drug delivery system [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr LBA051.

Acoustic patterning of endothelial cells in vivo to produce microvascular networks

Previous work from our lab developed an ultrasound standing wave field (USWF) technology to noninvasively pattern endothelial cells within collagen hydrogels to fabricate vascularized engineered tissue constructs. Here, we translate this technology towards acoustic patterning and microvascular network formation in vivo. To enable acoustic patterning in vivo, we developed a two-transducer ultrasound system with intersecting beams to produce an USWF at the intersection of the crossed beams. Acoustic radiation forces associated with the USWF patterned cells into planar bands located at nodal planes. Distance between planar bands was controlled through design of acoustic frequency and angle between ultrasound beams. To demonstrate acoustic patterning in vivo, solutions of collagen and human umbilical vein endothelial cells (HUVEC) were injected subcutaneously into flanks of mice and then exposed to an USWF. High-frequency ultrasound imaging was employed to visualize initial acoustic patterning in tissues. Ten days after USWF-mediated cell patterning, tissues were excised, and vessels derived from USWF-patterned cells were identified using an antibody specific to human von Willebrand factor (hVWF). Microvessels expressing hVWF were present in USWF-exposed tissues and not in sham-exposed controls. Furthermore, blood vessels expressing hVWF contained red blood cells, indicating anastomosis of ultrasound-fabricated microvessels with host vasculature and tissue perfusion.

Acoustic and Elastic Properties of a Blood Clot during Microbubble-Enhanced Sonothrombolysis: Hardening of the Clot with Inertial Cavitation.

Stroke is the second leading cause of death worldwide. Existing therapies present limitations, and other therapeutic alternatives are sought, such as sonothrombolysis with microbubbles (STL). The aim of this study was to evaluate the change induced by STL with or without recombinant tissue-type plasminogen activator (rtPA) on the acoustic and elastic properties of the blood clot by measuring its sound speed (SoS) and shear wave speed (SWS) with high frequency ultrasound and ultrafast imaging, respectively. An in-vitro setup was used and human blood clots were submitted to a combination of microbubbles and rtPA. The results demonstrate that STL induces a raise of SoS in the blood clot, specifically when combined with rtPA (p < 0.05). Moreover, the combination of rtPA and STL induces a hardening of the clot in comparison to rtPA alone (p < 0.05). This is the first assessment of acoustoelastic properties of blood clots during STL. The combination of rtPA and STL induce SoS and hardening of the clot, which is known to impair the penetration of thrombolytic drugs and their efficacy.

Deep Learning-Based High-Frequency Ultrasound Skin Image Classification with Multicriteria Model Evaluation

This study presents the first application of convolutional neural networks to high-frequency ultrasound skin image classification. This type of imaging opens up new opportunities in dermatology, showing inflammatory diseases such as atopic dermatitis, psoriasis, or skin lesions. We collected a database of 631 images with healthy skin and different skin pathologies to train and assess all stages of the methodology. The proposed framework starts with the segmentation of the epidermal layer using a DeepLab v3+ model with a pre-trained Xception backbone. We employ transfer learning to train the segmentation model for two purposes: to extract the region of interest for classification and to prepare the skin layer map for classification confidence estimation. For classification, we train five models in different input data modes and data augmentation setups. We also introduce a classification confidence level to evaluate the deep model’s reliability. The measure combines our skin layer map with the heatmap produced by the Grad-CAM technique designed to indicate image regions used by the deep model to make a classification decision. Moreover, we propose a multicriteria model evaluation measure to select the optimal model in terms of classification accuracy, confidence, and test dataset size. The experiments described in the paper show that the DenseNet-201 model fed with the extracted region of interest produces the most reliable and accurate results.

Wavelet Bilateral Filter Algorithm-Based High-Frequency Ultrasound Image Analysis on Effects of Skin Scar Repair

It aimed to explore the application value of high-frequency ultrasound image based on the wavelet bilateral filtering algorithm in the repair evaluation of hypertrophic scar and keloid, as well as the expression change of nerve growth factor (NGF). 72 patients with scars admitted to hospital from October 2018 to October 2019 were chosen and classified as hypertrophic scar patients (group A) and keloid patients (group B), with 36 cases in each group. All patients received scar repair treatment, and another 30 normal skin volunteers (group C) were selected. High-frequency ultrasound images based on the wavelet bilateral filter algorithm were utilized for skin examination. The expression differences of NGF and pro-NGF in each group were measured by immunohistochemical staining and Western blot. The results showed that the denoising effect (signal-to-noise ratio (PSNR) = 33.2762), structural similarity (SSIM = 0.8963), and edge similarity (FoM = 0.2975) of the proposed algorithm were better than those of bilateral filtering and wavelet soft-threshold algorithms. The skin thickness of groups A and B was considerably higher relative to that of group C ( P &lt; 0.05), but the echo intensity of the skin dermis of groups A and B was evidently inferior to that of group C ( P &lt; 0.05). The results of immunohistochemistry showed that in contrast to group C, the average optical density of NGF in groups A and B increased remarkably ( P &lt; 0.01). In contrast to group C, the positive expression of pro-NGF in groups A and B was notably reduced ( P &lt; 0.05). Western blot results showed that relative to group C, the expression of NGF protein in groups A and B was increased greatly ( P &lt; 0.01), but the expression of pro-NGF protein in groups A and B was reduced ( P &lt; 0.01). The results suggested that the wavelet bilateral filter algorithm can be adopted to evaluate pathological skin scars, and pathological skin scar formation was closely related to the expression levels of NGF and pro-NGF.

A Deep Learning Approach for Segmentation, Classification, and Visualization of 3-D High-Frequency Ultrasound Images of Mouse Embryos.

Segmentation and mutant classification of high-frequency ultrasound (HFU) mouse embryo brain ventricle (BV) and body images can provide valuable information for developmental biologists. However, manual segmentation and identification of BV and body requires substantial time and expertise. This article proposes an accurate, efficient and explainable deep learning pipeline for automatic segmentation and classification of the BV and body. For segmentation, a two-stage framework is implemented. The first stage produces a low-resolution segmentation map, which is then used to crop a region of interest (ROI) around the target object and serve as the probability map of the autocontext input for the second-stage fine-resolution refinement network. The segmentation then becomes tractable on high-resolution 3-D images without time-consuming sliding windows. The proposed segmentation method significantly reduces inference time (102.36-0.09 s/volume ≈ 1000× faster) while maintaining high accuracy comparable to previous sliding-window approaches. Based on the BV and body segmentation map, a volumetric convolutional neural network (CNN) is trained to perform a mutant classification task. Through backpropagating the gradients of the predictions to the input BV and body segmentation map, the trained classifier is found to largely focus on the region where the Engrailed-1 (En1) mutation phenotype is known to manifest itself. This suggests that gradient backpropagation of deep learning classifiers may provide a powerful tool for automatically detecting unknown phenotypes associated with a known genetic mutation.

High-frequency ultrasound as a scientific tool for skin imaging analysis.

Ultrasonic imaging is one of the most important diagnostic tools in clinical medicine due to its cost, availability and good correlation with pathological results. High-frequency ultrasound (HFUS) is a technique used in skin science that has been little explored, especially in comparison with other sites and imaging techniques. HFUS shows real-time images of the skin layers, appendages and skin lesions in vivo and can significantly contribute to advances in skin science. This review summarizes the potential applications of HFUS in dermatology and cosmetology, with a focus on quantitative tools that can be used to assess various skin conditions. Our findings showed that HFUS imaging is a reproducible and powerful tool for the diagnosis, clinical management and therapy monitoring of skin conditions. It is also a helpful tool for assessing the performance of dermatological products. This technique may eventually become essential for evaluating the performance of dermatological and cosmetic products.

Characterization of skin aging through high‐frequency ultrasound imaging as a technique for evaluating the effectiveness of anti‐aging products and procedures: A review

High-frequency ultrasound skin imaging analysis (HFUS) is a non-invasive technique that allows a unique approach to the analysis of skin aging, as well as in evaluating the effectiveness of dermatological and cosmetic products, especially for skin rejuvenation. To describe the impact of skin aging and different anti-aging strategies from the perspective of high-frequency ultrasound. A bibliographic survey was carried out, selecting relevant articles that evaluated the characterization of the skin features from different points of view such as gender (male and female), age (young skin and mature skin), and ethnicity, in addition to individual variations between body regions and daily variations. Some studies also evaluated the impact of cosmetic treatments and esthetic procedures in the skin. Parameters such as dermal thickness, echogenicity, skin texture, and subepidermal low-echogenic band (SLEB) were analyzed. It can be concluded that there is a trend, although not unanimous in the consequences of aging on the skin, being different between men and women, plus the individual nuances resulted from each one's lifestyle and exposure to the sun. As for the technique, it is concluded that high-frequency ultrasound is an important evaluative alternative for dermatological studies and the effectiveness of anti-aging products and treatments.

Diagnosis of Waist Muscle Injury after Exercise Based on High-Frequency Ultrasound Image.

The low back is the weight-bearing part of the human trunk and has a complex anatomy with a variety of lesions. The traditional diagnostic imaging methods include X-ray, CT, and magnetic resonance imaging (MRI). With the rapid development of ultrasound diagnostic instruments and diagnostic techniques, high-frequency ultrasonography plays a more important role in the diagnosis of musculoskeletal disorders by virtue of its advantages of being safe, noninvasive, inexpensive, repeatable in a short period of time, and real-time imaging, especially for the display of fine structures of soft tissues, which is superior to CT and MRI. In this paper, the puncture needle was punctured near the intervertebral foramen, anti-inflammatory analgesics were injected under ultrasound surveillance. The anti-inflammatory and analgesic drugs mainly consist of local anesthetics and glucocorticoids. Local anesthetics can immediately block the nociceptive transmission of the diseased nerve, improve muscle tension, and eliminate muscle spasm, and glucocorticoids can effectively eliminate edema and inflammation of the lumbar nerve root and its surrounding tissues. The results showed that high-frequency ultrasound, as a noninvasive imaging examination, can clearly display the structure and spatial hierarchy of the skin, superficial fascia, deep fascia, and muscles of the low back, can provide real-time dynamic bilateral contrast observation, which can be the preferred imaging examination method for soft tissue disorders of the low back, and can compensate with general X-ray, CT, and magnetic resonance imaging to provide richer clinical diagnosis and treatment. It can provide richer imaging information for clinical diagnosis and treatment.

Synthetic Aperture Imaging Using High-Frequency Convex Array for Ophthalmic Ultrasound Applications.

High-frequency ultrasound (HFUS) imaging has emerged as an essential tool for pre-clinical studies and clinical applications such as ophthalmic and dermatologic imaging. HFUS imaging systems based on array transducers capable of dynamic receive focusing have considerably improved the image quality in terms of spatial resolution and signal-to-noise ratio (SNR) compared to those by the single-element transducer-based one. However, the array system still suffers from low spatial resolution and SNR in out-of-focus regions, resulting in a blurred image and a limited penetration depth. In this paper, we present synthetic aperture imaging with a virtual source (SA-VS) for an ophthalmic application using a high-frequency convex array transducer. The performances of the SA-VS were evaluated with phantom and ex vivo experiments in comparison with the conventional dynamic receive focusing method. Pre-beamformed radio-frequency (RF) data from phantoms and excised bovine eye were acquired using a custom-built 64-channel imaging system. In the phantom experiments, the SA-VS method showed improved lateral resolution (>10%) and sidelobe level (>4.4 dB) compared to those by the conventional method. The SNR was also improved, resulting in an increased penetration depth: 16 mm and 23 mm for the conventional and SA-VS methods, respectively. Ex vivo images with the SA-VS showed improved image quality at the entire depth and visualized structures that were obscured by noise in conventional imaging.

Development of a semi-automated segmentation tool for high frequency ultrasound image analysis of mouse echocardiograms

Echocardiography is a widely used and clinically translatable imaging modality for the evaluation of cardiac structure and function in preclinical drug discovery and development. Echocardiograms are among the first in vivo diagnostic tools utilized to evaluate the heart due to its relatively low cost, high throughput acquisition, and non-invasive nature; however lengthy manual image analysis, intra- and inter-operator variability, and subjective image analysis presents a challenge for reproducible data generation in preclinical research. To combat the image-processing bottleneck and address both variability and reproducibly challenges, we developed a semi-automated analysis algorithm workflow to analyze long- and short-axis murine left ventricle (LV) ultrasound images. The long-axis B-mode algorithm executes a script protocol that is trained using a reference library of 322 manually segmented LV ultrasound images. The short-axis script was engineered to analyze M-mode ultrasound images in a semi-automated fashion using a pixel intensity evaluation approach, allowing analysts to place two seed-points to triangulate the local maxima of LV wall boundary annotations. Blinded operator evaluation of the semi-automated analysis tool was performed and compared to the current manual segmentation methodology for testing inter- and intra-operator reproducibility at baseline and after a pharmacologic challenge. Comparisons between manual and semi-automatic derivation of LV ejection fraction resulted in a relative difference of 1% for long-axis (B-mode) images and 2.7% for short-axis (M-mode) images. Our semi-automatic workflow approach reduces image analysis time and subjective bias, as well as decreases inter- and intra-operator variability, thereby enhancing throughput and improving data quality for pre-clinical in vivo studies that incorporate cardiac structure and function endpoints.

A clinical decision support system for predicting cirrhosis stages via high frequency ultrasound images

Many visceral organs contain two important structures: capsule and parenchyma. As a non-invasive examination method, ultrasound is widely used. In this study, we develop a computational framework that consists of capsule localization and parenchyma assessment for disease diagnosis.Clinical decision support system helps providing standard, objective and timely diagnosis. However, although current cirrhosis diagnosis methods are mainly based on the images produced from medical imaging technique and experienced clinicians subjective analysis, many imaging techniques are invasive, and experienced clinicians are in short supply, especially in underdeveloped regions. The proposed system employs an incremental classification model for grading parenchyma patch stages in predicting cirrhosis stages from high frequency ultrasound images. The incremental classification model is based on auto-extracted membrane structures along with patch-ensemble model, using a severe-first strategy. The framework firstly applies multi-scale capsule extraction automatically. The lesions of the capsule and parenchyma show increasing changes in the first three stages. In the final stage, due to the liver ascites, partial lesions restore to normal. To handle the inconsecutive change, the proposed patch-ensemble model applies two layers. We firstly recognize images belong to the severe stage via capsule-specific CIFAR in the first layer. Parenchyma-aimed Resnet is then applied to classify rest images into rest stages. In each layer a data split and aggregation scheme is proposed to evaluate the cirrhosis stage for liver images.The experimental results demonstrate that the proposed method achieves high precision and effectiveness and can be effectively applied to the auxiliary diagnosis of cirrhosis. Some integral parts of the system are also available for visceral organs with similar structure.