Simultaneous Assessment of the Whole Eye Biomechanics Using Ultrasonic Elastography.

OBJECTIVE: Current elastography techniques in the field of ophthalmology usually target one specific tissue, such as the cornea or the sclera. However, the eye is an inter-related organ, and some ocular diseases can alter the biomechanical properties of multiple anatomical structures. Hence, there is a need to develop an imaging tool that can non-invasively, quantitatively, and accurately characterize dynamic changes among these biomechanical properties. METHODS: A high resolution ultrasound elastography system was developed to achieve this goal. The efficacy and accuracy of the system was first validated on tissue-mimicking phantoms while mechanical testing measurements served as the gold standard. Next, an in vivo elevated intraocular pressure (IOP) model was established in rabbits to further test our system. In particular, elastography measurements were obtained at 5 IOP levels, ranging from 10 mmHg to 30 mmHg in 5 mmHg increments. Spatial-temporal maps of the multiple ocular tissues (cornea, lens, iris, optic nerve head, and peripapillary sclera) were obtained. RESULTS: The spatial-temporal maps were acquired simultaneously for the ocular tissues at the 5 different IOP levels. The statistical analysis of the elastic wave speed was presented for ocular tissues. Finally, the mapping for the elastic wave speed of each ocular component was acquired at each IOP level. CONCLUSION: Our elastography system can concurrently assess the biomechanical properties of multiple ocular structures and detect changes in biomechanical properties associated with changes in IOP. SIGNIFICANCE: This system provides a novel tool to measure and quantify the biomechanical properties of the whole eye.

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning.

The clinical application of breast ultrasound for the assessment of cancer risk and of deep learning for the classification of breast-ultrasound images has been hindered by inter-grader variability and high false positive rates and by deep-learning models that do not follow Breast Imaging Reporting and Data System (BI-RADS) standards, lack explainability features and have not been tested prospectively. Here, we show that an explainable deep-learning system trained on 10,815 multimodal breast-ultrasound images of 721 biopsy-confirmed lesions from 634 patients across two hospitals and prospectively tested on 912 additional images of 152 lesions from 141 patients predicts BI-RADS scores for breast cancer as accurately as experienced radiologists, with areas under the receiver operating curve of 0.922 (95% confidence interval (CI) = 0.868-0.959) for bimodal images and 0.955 (95% CI = 0.909-0.982) for multimodal images. Multimodal multiview breast-ultrasound images augmented with heatmaps for malignancy risk predicted via deep learning may facilitate the adoption of ultrasound imaging in screening mammography workflows.

Current Ultrasound Technologies and Instrumentation in the Assessment and Monitoring of COVID-19 Positive Patients.

Since the emergence of the COVID-19 pandemic in December of 2019, clinicians and scientists all over the world have faced overwhelming new challenges that not only threaten their own communities and countries but also the world at large. These challenges have been enormous and debilitating, as the infrastructure of many countries, including developing ones, had little or no resources to deal with the crisis. Even in developed countries, such as Italy, health systems have been so inundated by cases that health care facilities became oversaturated and could not accommodate the unexpected influx of patients to be tested. Initially, resources were focused on testing to identify those who were infected. When it became clear that the virus mainly attacks the lungs by causing parenchymal changes in the form of multifocal pneumonia of different levels of severity, imaging became paramount in the assessment of disease severity, progression, and even response to treatment. As a result, there was a need to establish protocols for imaging of the lungs in these patients. In North America, the focus was on chest X-ray and computed tomography (CT) as these are widely available and accessible at most health facilities. However, in Europe and China, this was not the case, and a cost-effective and relatively fast imaging modality was needed to scan a large number of sick patients promptly. Hence, ultrasound (US) found its way into the hands of Chinese and European physicians and has since become an important imaging modality in those locations. US is a highly versatile, portable, and inexpensive imaging modality that has application across a broad spectrum of conditions and, in this way, is ideally suited to assess the lungs of COVID-19 patients in the intensive care unit (ICU). This bedside test can be done with little to no movement of the patients from the unit that keeps them in their isolated rooms, thereby limiting further exposure to other health personnel. This article presents a basic introduction to COVID-19 and the use of the US for lung imaging. It further provides a high-level overview of the existing US technologies that are driving development in current and potential future US imaging systems for lung, with a specific emphasis on portable and 3-D systems.

Quantitative Assessment of Thin-Layer Tissue Viscoelastic Properties Using Ultrasonic Micro-Elastography With Lamb Wave Model.

Characterizing the viscoelastic properties of thin-layer tissues with micro-level thickness has long remained challenging. Recently, several micro-elastography techniques have been developed to improve the spatial resolution. However, most of these techniques have not considered the medium boundary conditions when evaluating the viscoelastic properties of thin-layer tissues such as arteries and corneas; this might lead to estimation bias or errors. This paper aims to integrate the Lamb wave model with our previously developed ultrasonic micro-elastography imaging system for obtaining accurate viscoelastic properties in thin-layer tissues. A 4.5-MHz ring transducer was used to generate an acoustic radiation force for inducing tissue displacements to produce guided wave, and the wave propagation was detected using a confocally aligned 40-MHz needle transducer. The phase velocity and attenuation were obtained from k-space by both the impulse and the harmonic methods. The measured phase velocity was fit using the Lamb wave model with the Kelvin-Voigt model. Phantom experiments were conducted using 7% and 12% gelatin and 1.5% agar phantoms with different thicknesses (2, 3, and 4 mm). Biological experiments were performed on porcine cornea and rabbit carotid artery ex vivo. Thin-layer phantoms with different thicknesses were confirmed to have the same elasticity; this was consistent with the estimates of bulk phantoms from mechanical tests and the shear wave rheological model. The trend of the measured attenuations was also confirmed with the viscosity results obtained using the Lamb wave model. Through the impulse and harmonic methods, the shear viscoelasticity values were estimated to be 8.2 kPa for $0.9~\text {Pa}{\cdot} \text {s}$ and 9.6 kPa for $0.8~\text {Pa}{\cdot} \text {s}$ in the cornea and 27.9 kPa for $0.1~\text {Pa}\cdot \text {s}$ and 26.5 kPa for $0.1~\text {Pa}\cdot \text {s}$ in the artery.