Ultrasound Imaging Techniques Research Articles

A concise history of echocardiography: timeline, pioneers, and landmark publications.

Echocardiography is less than 70 years old, and many major advances have occurred within living memory, but already some pioneering contributions may be overlooked. In order to consider what circumstances have been common to the most successful innovations, we have studied and here provide a timeline and summary of the most important developments in transthoracic and transoesophageal ultrasound imaging and Doppler techniques, as well as in intravascular ultrasound and imaging in paediatric cardiology. The entries are linked to a comprehensive list of first publications and to a collection of first-hand historical accounts published by early investigators. Review of the original manuscripts highlights that it is difficult to establish unequivocal precedence for many new imaging methods, since engineers were often working independently but simultaneously on similar problems. Many individuals who are prominently linked with particular developments were not the first in their field. Developments in echocardiography have been highly dependent on technological advances, and most likely to be successful when engineers and clinicians were able to collaborate with open exchange between centres and disciplines. As with many other new medical technologies, initial responses were sceptical and introduction into clinical practice required persistence and substantial energy from the first adopters. Current developments involve advances in software as much as in equipment, and progress will depend on continuing collaborations between engineers and clinical scientists, for example to identify unmet needs and to investigate the clinical impact of particular imaging approaches.

Bioengineering in the Development of Artificial Hips, Knees, and other joints. Ultrasound, MRI, and other Medical Imaging Techniques

The bioengineering technique is developed as a method that is essential to combine different dead and inorganic materials with a living elements. Artificial organs are human-made organs that are helpful for providing a specific function for the patients to reach normal life soon. Improvement in healthcare can be possible to reach with properly aligned different artificial organs that are helping to overcome serious situations in life. In this research, the secondary qualitative method is used which is beneficial for completing the study in an effective way. Different previous published research in different journals, articles, and books are referred to for completing the research which is important for gaining proper growth for the research. Different aspects of bioengineering in the field of providing artificial body parts for humans are discussed in this research which is helping them to recover from any disease or impact of an accident. The overall impact on society and growth in the life developed due to artificial organs are also discussed in this research. Medical imaging techniques are helpful to create growth for research by providing the right track to growth. A proper understanding of diseases can be possible with the help of these technological advancements that are able to eliminate difficulties that are growing in human health. Keywords : artificial organs, Bioengineering, biomedical engineering development, MRI, prosthetics, replacing organs.

FN3 linked nanobubbles as a targeted contrast agent for US imaging of cancer-associated human PD-L1.

PD-L1 (programmed death-ligand 1) targeted therapies may be useful for several cancers. The use of non-invasive diagnostic and prognostic molecular imaging platforms could improve clinical assessment of PD-L1 tumor status during these therapies. Contrast enhanced ultrasound molecular imaging (CE-USMI) techniques may offer versatile and cost-effective ways to detect and quantify the expression levels of cellular targets in vivo. However, conventional use of microbubbles as a blood pool contrast agent for CE-USMI is limited to accessing intravascular biomarkers rather than reflecting the tumor molecular status. Using a microfluidic based reconstruction process we therefore developed ultra-stable nanobubbles (NBs) as a contrast agent for molecular imaging of vascular and extravascular cell surface markers. We then functionalized these NBs by covalently linking to nanobody (FN3hPD-L1) targeting human (h)PD-L1 to measure the expression of human PD-L1 in the tumor microenvironment (TME) in vivo. We showed the specific binding of hPD-L1 targeted NBs in cell culture, and in xenografted mouse models of hPD-L1 expressing CT26 tumors. CE-USMI of hPD-L1 in the TME in vivo showed ~3-fold increase in contrast signal compared to non-targeted NBs. Overall, in vivo use of CE-USMI with hPD-L1 targeted NBs has the potential for clinical translation and imaging of human cancers during immunotherapy, and for prognostic evaluation of patient response to PD-L1 targeted immunotherapy.

Probing the Mechanical Properties of Large Arteries by Measuring Their Deformation In Vivo with Ultrasound

Aging and cardiovascular diseases (CVDs) may alter the microstructures of arteries and hence their mechanical properties. Therefore, the measurement of intrinsic artery mechanical properties in vivo can provide valuable information in understanding aging and CVDs and is of clinical significance. The accuracy of advanced ultrasound imaging techniques in measuring the deformation of large arteries under blood pressure is good. However, the assessment of arterial stiffness in vivo remains a challenge. An inverse method to infer the constitutive parameters of arteries in vivo from the blood pressure-arterial radius relationship (P-r curve) is proposed here. The stability analysis reveals that a key constitutive parameter, bθ, which measures the circumferential hardening of an artery, can be reliably identified. An in vivo experiment was performed on the common carotid arteries of 41 healthy volunteers (age: 37 ± 17 y). The value of bθ varies significantly (from 0.55 ± 0.15 for the young group to 0.93 ± 0.29 for the older group, p < 0.01) and is positively correlated with age (r=0.673, p < 0.01). Furthermore, our theoretical analysis and experimental study have revealed a strong correlation between the clinic-used stiffness index β and bθ. This study shows that the arterial material parameter bθ can be measured in vivo, which makes it promising as a new biomarker in the diagnosis of CVDs.

Effect of Age and BMI on Sonographic Findings of Plantar Fascia

Biomechanical dysfunction of the foot is most common cause of plantar fascia disorder and obesity and increasing age are well-known risk factors. Due to being inexpensive and quick, ultrasound imaging techniques are considered the modality of choice to assess plantar fascia. The aim of this study was to investigate the effect of age and body mass index (BMI) on sonographic findings of plantar fascia in normal population. Ultrasonography was used to measure the plantar fascia thickness of 148 healthy adults (54 males, 36.5% and 94 females, 63.5%) during a period of one year. The age, BMI, and walking distance of each participant were recorded and statistically analyzed. The mean plantar fascia thicknesses at 0.5, 1.0, and 2.0 cm distal to the insertion of the plantar fascia were 1.76 ± 0.32 mm, 2.50 ± 0.50 mm and 2.11 ± 0.41 mm respectively. The mean plantar fascia thickness for individuals ≥45 years and BMI ≥25 were significantly higher (p < .001) compared to individuals <45 years and BMI <25. In a normal population, the thickness of the plantar fascia determined by ultrasound measurement was less than 3 mm. We recommend using the position 1.0 cm distal to the calcaneal insertion of the plantar fascia as the reference point for diagnosing plantar fasciitis. The thickness of plantar fascia was significantly increased with age and BMI whereas gender, walking activity, exercise and running did not seem to affect the plantar fascia thickness.

Contribution of UltraFast™ Ultrasound and Shear Wave Elastography in the Imaging of Carotid Artery Disease.

Carotid artery disease is one of the main global causes of disability and premature mortality in the spectrum of cardiovascular diseases. One of its main consequences, stroke, is the second biggest global contributor to disability and burden via Disability Adjusted Life Years after ischemic heart disease. In the last decades, B-mode and Doppler-based ultrasound imaging techniques have become an indispensable part of modern medical imaging of carotid artery disease. However, they have limited abilities in carotid artery plaque and wall characterization and are unable to provide simultaneous quantitative and qualitative flow information while the images are burdened by low framerates. UltraFast™ ultrasound is able to overcome these obstacles by providing simultaneous quantitative and qualitative flow analysis information in high frame rates via UltraFast™ Doppler. Another newly developed ultrasound technique, shear wave elastography, is based on the visualization of induced shear waves and the measurement of the shear wave propagation speed in the examined tissues which enables real-time carotid plaque and wall analysis. These newly developed ultrasound modalities have potential to significantly improve workflow efficiency and are able to provide a plethora of additional imaging information of carotid artery disease in comparison to conventional ultrasound techniques.

Angular spectrum method for curvilinear arrays: Theory and application to Fourier beamforming.

Fourier beamforming techniques for medical ultrasound imaging have largely been limited to linear transducer arrays. This work extends the angular spectrum method to curvilinear arrays and demonstrates a migration-based Fourier beamforming technique that has implications for sound speed estimation and distributed aberration correction for abdominal imaging applications. When compared to Field II simulations, the proposed angular spectrum method simulates the pressure field from a focused transmission to within 3.7% normalized root mean square error. The resulting Fourier beamforming technique is then compared to virtual source synthetic aperture using in vivo abdominal imaging examples where resolution and imaging quality improvements are observed.

Ultrasound high-definition microvasculature imaging with novel quantitative biomarkers improves breast cancer detection accuracy

ObjectivesTo overcome the limitations of power Doppler in imaging angiogenesis, we sought to develop and investigate new quantitative biomarkers of a contrast-free ultrasound microvasculature imaging technique for differentiation of benign from malignant pathologies of breast lesion.MethodsIn this prospective study, a new high-definition microvasculature imaging (HDMI) was tested on 521 patients with 527 ultrasound-identified suspicious breast masses indicated for biopsy. Four new morphological features of tumor microvessels, microvessel fractal dimension (mvFD), Murray’s deviation (MD), bifurcation angle (BA), and spatial vascularity pattern (SVP) as well as initial biomarkers were extracted and analyzed, and the results correlated with pathology. Multivariable logistic regression analysis was used to study the performance of different prediction models, initial biomarkers, new biomarkers, and combined new and initial biomarkers in differentiating benign from malignant lesions.ResultsThe new HDMI biomarkers, mvFD, BA, MD, and SVP, were statistically significantly different in malignant and benign lesions, regardless of tumor size. Sensitivity and specificity of the new biomarkers in lesions > 20 mm were 95.6% and 100%, respectively. Combining the new and initial biomarkers together showed an AUC, sensitivity, and specificity of 97% (95% CI: 95–98%), 93.8%, and 89.2%, respectively, for all lesions regardless of mass size. The classification was further improved by adding the Breast Imaging Reporting and Data System (BI-RADS) score to the prediction model, showing an AUC, sensitivity, and specificity of 97% (95% CI: 95–98%), 93.8%, and 89.2%, respectively.ConclusionThe addition of new quantitative HDMI biomarkers significantly improved the accuracy in breast lesion characterization when used as a complementary imaging tool to the conventional ultrasound.Key Points• Novel quantitative biomarkers extracted from tumor microvessel images increase the sensitivity and specificity in discriminating malignant from benign breast masses.• New HDMI biomarkers Murray’s deviation, bifurcation angles, microvessel fractal dimension, and spatial vascularity pattern outperformed the initial biomarkers.• The addition of BI-RADS scores based on US descriptors to the multivariable analysis using all biomarkers remarkably increased the sensitivity, specificity, and AUC in all size groups.

Super-Resolution Ultrasound Imaging Scheme Based on a Symmetric Series Convolutional Neural Network.

In this paper, we propose a symmetric series convolutional neural network (SS-CNN), which is a novel deep convolutional neural network (DCNN)-based super-resolution (SR) technique for ultrasound medical imaging. The proposed model comprises two parts: a feature extraction network (FEN) and an up-sampling layer. In the FEN, the low-resolution (LR) counterpart of the ultrasound image passes through a symmetric series of two different DCNNs. The low-level feature maps obtained from the subsequent layers of both DCNNs are concatenated in a feed forward manner, aiding in robust feature extraction to ensure high reconstruction quality. Subsequently, the final concatenated features serve as an input map to the latter 2D convolutional layers, where the textural information of the input image is connected via skip connections. The second part of the proposed model is a sub-pixel convolutional (SPC) layer, which up-samples the output of the FEN by multiplying it with a multi-dimensional kernel followed by a periodic shuffling operation to reconstruct a high-quality SR ultrasound image. We validate the performance of the SS-CNN with publicly available ultrasound image datasets. Experimental results show that the proposed model achieves a high-quality reconstruction of the ultrasound image over the conventional methods in terms of peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), while providing compelling SR reconstruction time.

Test-Retest Reliability of Ultrasonographic Measurements from the Rectus Femoris Muscle 1-5 Years after Anterior Cruciate Ligament Reconstruction in the Ipsilateral and Contralateral Legs: An Observational, Case-Control Study.

About 40% of traumatic injuries in sports are related to the knee. Of these, 33% require arthroscopic surgery. The rehabilitative ultrasound imaging technique is a simple method to obtain objective real-time results on the state and measurement of the musculoskeletal tissue and its use can represent an important change in the process of functional diagnosis and recovery of these injuries. The aim was to quantify the differences in the thickness, muscle contraction time, and muscle relaxation time of the rectus femoris muscle between individuals with knee arthroscopy and healthy individuals and to verify the reliability of the inter-examiner measurements in these ultrasound variables. An observational case-control study with individuals (18–60 years aged) who underwent surgery for anterior cruciate ligament through knee arthroscopy a year or more before. A total of 38 subjects were divided into 2 groups, case and control. Ultrasound measurements were taken of the following outcomes: thickness at rest and contraction, muscle contraction time, and muscle relaxation time of the rectus femoris muscle. Excellent inter-examiner reliability was obtained for all ultrasound measurements (ICC3.3 > 0.90). No significant changes were found in the rate of contraction or rest of the rectus femoris muscle. On the other hand, if significant changes in the thickness of the rectus femoris muscle were found between control and case group. Arthroscopic surgery for anterior cruciate ligament reconstruction does not appear to modify function but does modify the thickness of the rectus femoris muscle on ultrasound examination. Ultrasound appears to be a reliable tool for the study of these measurements in the rectus femoris muscle.

Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images

Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images

Shear wave elastography of lymph nodes in dogs with head and neck cancer: A pilot study.

Shear wave elastography (SWE) is a novel ultrasound imaging technique, used in human medicine to differentiate metastatic from non-metastatic lymph nodes (LNs) based on higher tissue stiffness. The objective of this pilot study was to evaluate whether canine metastatic LNs were stiffer compared to non-metastatic LNs. SWE of sentinel or regional LNs in dogs with head and neck cancer was performed. Ten elastograms of each LN were acquired. In each elastogram, mean, median, and maximum shear wave velocities (SWVs) were calculated in the most elastic region, the stiffest region, and the entire LN. The means of those SWVs for each region of each LN were subsequently calculated. Furthermore, a stepwise subsampling was performed to assess the effect of the number of acquired elastograms on the consistency of the classification of a LN as metastatic or not. Twenty-four LNs in 15 dogs with head and neck cancer were included, of which 10 LNs were metastatic. Metastatic medial retropharyngeal and mandibular LNs were successfully distinguished from non-metastatic LNs based on higher mean and median SWVs in the stiffest region and higher maximum SWVs in entire LNs. Furthermore, maximum SWVs in the stiffest region of mandibular LNs were higher in metastatic LNs compared to non-metastatic LNs. Downstream analyses demonstrated that at least eight elastograms were necessary to perform reliable analyses. This pilot trial demonstrated that SWE has potential to discriminate metastatic from non-metastatic LNs; however, LN status should be based on at least eight elastograms in future clinical trials.

Ultrasound-Mediated Release of Gaseous Signaling Molecules for Biomedical Applications.

Although nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2 S) have been considered as notorious gas pollutants for decades, they are considered as endogenous gaseous signaling molecules (GSMs), which have been widely recognized for their important signaling functions and prominent medical applications in human physiology. To achieve local delivery of GSMs to optimize therapeutic efficacy and reduce systemic side effects, stimuli-responsive nanocarriers have been successfully developed. Among them, ultrasound is considered as an attractive theranostic modality that can be used to track drug carriers, trigger drug release, and improve drug deposition, etc. In this minireview, recent achievements in designing ultrasound-responsive nanocarriers for the controlled delivery of GSMs and their biomedical applications are summarized. This emerging research direction enables the controlled delivery of GSMs to deep tissues, and the combination of ultrasound imaging techniques offers many possibilities for the fabrication of new theranostic platforms.

Non-invasive tests of non-alcoholic fatty liver disease.

For the detection of steatosis, quantitative ultrasound imaging techniques have achieved great progress in past years. Magnetic resonance imaging proton density fat fraction is currently the most accurate test to detect hepatic steatosis. Some blood biomarkers correlate with non-alcoholic steatohepatitis, but the accuracy is modest. Regarding liver fibrosis, liver stiffness measurement by transient elastography (TE) has high accuracy and is widely used across the world. Magnetic resonance elastography is marginally better than TE but is limited by its cost and availability. Several blood biomarkers of fibrosis have been used in clinical trials and hold promise for selecting patients for treatment and monitoring treatment response. This article reviews new developments in the non-invasive assessment of non-alcoholic fatty liver disease (NAFLD). Accumulating evidence suggests that various non-invasive tests can be used to diagnose NAFLD, assess its severity, and predict the prognosis. Further studies are needed to determine the role of the tests as monitoring tools. We cannot overemphasize the importance of context in selecting appropriate tests.

Development of Preclinical Ultrasound Imaging Techniques to Identify and Image Sentinel Lymph Nodes in a Cancerous Animal Model.

Simple SummaryBowel cancer is the fourth most common cancer in the UK. Treatment is dominated by major surgery because current imaging modalities cannot accurately determine lymph node involvement or vascular invasion. Although potentially curative, surgery carries a high risk of short- and long-term morbidity, including stoma formation. Optimized pre-treatment imaging would decrease the number of bowel cancer patients requiring major surgery. Such imaging would also be equally applicable to other cancers where local resection could significantly improve patient quality of life without compromising long-term outcomes (e.g., melanoma, head and neck cancers, gastro-esophageal, bladder). In this study, we created two mouse models (tumor and control) and used the resolution of high-frequency ultrasound imaging and parameters calculated from dynamically contrast-enhanced ultrasound to predict the likelihood of draining lymph nodes to be involved in the disease.Lymph nodes (LNs) are believed to be the first organs targeted by colorectal cancer cells detached from a primary solid tumor because of their role in draining interstitial fluids. Better detection and assessment of these organs have the potential to help clinicians in stratification and designing optimal design of oncological treatments for each patient. Whilst highly valuable for the detection of primary tumors, CT and MRI remain limited for the characterization of LNs. B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) can improve the detection of LNs and could provide critical complementary information to MRI and CT scans; however, the European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) guidelines advise that further evidence is required before US or CEUS can be recommended for clinical use. Moreover, knowledge of the lymphatic system and LNs is relatively limited, especially in preclinical models. In this pilot study, we have created a mouse model of metastatic cancer and utilized 3D high-frequency ultrasound to assess the volume, shape, and absence of hilum, along with CEUS to assess the flow dynamics of tumor-free and tumor-bearing LNs in vivo. The aforementioned parameters were used to create a scoring system to predict the likelihood of a disease-involved LN before establishing post-mortem diagnosis with histopathology. Preliminary results suggest that a sum score of parameters may provide a more accurate diagnosis than the LN size, the single parameter currently used to predict the involvement of an LN in disease.

Cerebral Blood Flow Monitoring in High-Risk Fetal and Neonatal Populations.

Cerebrovascular pressure autoregulation promotes stable cerebral blood flow (CBF) across a range of arterial blood pressures. Cerebral autoregulation (CA) is a developmental process that reaches maturity around term gestation and can be monitored prenatally with both Doppler ultrasound and magnetic resonance imaging (MRI) techniques. Postnatally, there are key advantages and limitations to assessing CA with Doppler ultrasound, MRI, and near-infrared spectroscopy. Here we review these CBF monitoring techniques as well as their application to both fetal and neonatal populations at risk of perturbations in CBF. Specifically, we discuss CBF monitoring in fetuses with intrauterine growth restriction, anemia, congenital heart disease, neonates born preterm and those with hypoxic-ischemic encephalopathy. We conclude the review with insights into the future directions in this field with an emphasis on collaborative science and precision medicine approaches.

Principal Component Analysis Based Hybrid Speckle Noise Reduction Technique for Medical Ultrasound Imaging

Ultrasound imaging is the safest and most widely used medical imaging technique available today. The main disadvantage of ultrasound imaging is the presence of speckle noise in its images that may obscure pathological changes in the body and makes diagnosis more challenging. Therefore, many techniques were proposed to reduce speckle and improve image quality. Unfortunately, variations of their performance with different scan parameters and due to their methodologies make it hard to choose which one to adopt in clinical practice. In this work, we consider the problem of combining the information from multiple speckle filters and propose the use of principal component analysis to find the optimal set of weights that would retain the most information and hence would better represent the data in the final image. The new technique is implemented to process ultrasound images collected from a research system and the outcomes are compared to the individual techniques and their average using quantitative image quality metrics. The proposed technique has potential for utilization in clinical settings to provide consistently better-quality combined images that may help improve diagnostic accuracy.

Onicopatías

Nail disorders are a common reason for dermatologic consultation. Nails may show different findings that can arise from nail unit diseases, dermatoses or systemic diseases. Several techniques are used for nail diagnosis, such as dermoscopy, ultrasound or other imaging techniques, microbiological tests, and histological analysis. This article review the features, diagnosis and treatment of different nail disorders, including nail semiology, inflammatory dermatoses, infections, systemic diseases and nail tumors.

Adversarial Neural Network Classifiers for COVID-19 Diagnosis in Ultrasound Images

The novel Coronavirus disease 2019 (COVID-19) pandemic has begun in China and is still affecting thousands of patient lives worldwide daily. Although Chest X-ray and Computed Tomography are the gold standard medical imaging modalities for diagnosing potentially infected COVID-19 cases, applying Ultrasound (US) imaging technique to accomplish this crucial diagnosing task has attracted many physicians recently. In this article, we propose two modified deep learning classifiers to identify COVID-19 and pneumonia diseases in US images, based on generative adversarial neural networks (GANs). The proposed image classifiers are a semi-supervised GAN and a modified GAN with auxiliary classifier. Each one includes a modified discriminator to identify the class of the US image using semi-supervised learning technique, keeping its main function of defining the “realness” of tested images. Extensive tests have been successfully conducted on public dataset of US images acquired with a convex US probe. This study demonstrated the feasibility of using chest US images with two GAN classifiers as a new radiological tool for clinical check of COVID-19 patients. The results of our proposed GAN models showed that high accuracy values above 91.0% were obtained under different sizes of limited training data, outperforming other deep learning-based methods, such as transfer learning models in the recent studies. Consequently, the clinical implementation of our computer-aided diagnosis of US-COVID-19 is the future work of this study.

A novel dictionary learning-based approach for Ultrasound Elastography denoising.

Ultrasound Elastography is a late-model Ultrasound imaging technique mainly used to diagnose tumors and diffusion diseases that can't be detected by traditional Ultrasound imaging. However, artifact noise, speckle noise, low contrast and low signal-to-noise ratio in images make disease diagnosing a challenging task. Medical images denoising, as the first step in the follow-up processing of medical images, has been concerned by many people. With the widespread use of deep learning technique in the research field, dictionary learning method are once again receiving attention. Dictionary learning, as a traditional machine learning method, requires less sample size, has high training efficiency, and can describe images well. In this work, we present a novel strategy based on K-clustering with singular value decomposition (K-SVD) and principal component analysis (PCA) to reduce noise in Ultrasound Elastography images. At this stage of dictionary training, we implement a PCA method to transform the way dictionary atoms are updated in K-SVD. Finally, we reconstructed the image based on the dictionary atoms and sparse coefficients to obtain the denoised image. We applied the presented method on datasets of clinical Ultrasound Elastography images of lung cancer from Nanjing First Hospital, and compared the results of the presented method and the original method. The experimental results of subjective and objective evaluation demonstrated that presented approach reached a satisfactory denoising effect and this research provides a new technical reference for computer aided diagnosis.