In Vivo Assessment of Pulmonary Fibrosis and Pulmonary Edema in Rodents Using Ultrasound Multiple Scattering

Lung quantitative ultrasound to stage and monitor interstitial lung diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine quantitative ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate (p = 1e-4 for edema and p = 3e-7 for fibrosis) to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

A lung disease diagnosis algorithm based on 2D spectral features of ultrasound RF signals

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator's professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Evolutionary genetics of pulmonary anatomical adaptations in deep-diving cetaceans

BACKGROUND

Cetaceans, having experienced prolonged adaptation to aquatic environments, have undergone evolutionary changes in their respiratory systems. This process of evolution has resulted in the emergence of distinctive phenotypic traits, notably the abundance of elastic fibers and thickened alveolar walls in their lungs, which may facilitate alveolar collapse during diving. This structure helps selective exchange of oxygen and carbon dioxide, while minimizing nitrogen exchange, thereby reducing the risk of DCS. Nevertheless, the scientific inquiry into the mechanisms through which these unique phenotypic characteristics govern the diving behavior of marine mammals, including cetaceans, remains unresolved.

RESULTS

This study entails an evolutionary analysis of 42 genes associated with pulmonary fibrosis across 45 mammalian species. Twenty-one genes in cetaceans exhibited accelerated evolution, featuring specific amino acid substitutions in 14 of them. Primarily linked to the development of the respiratory system and lung morphological construction, these genes play a crucial role. Moreover, among marine mammals, we identified eight genes undergoing positive selection, and the evolutionary rates of three genes significantly correlated with diving depth. Specifically, the SFTPC gene exhibited convergent amino acid substitutions. Through in vitro cellular experiments, we illustrated that convergent amino acid site mutations in SFTPC contribute positively to pulmonary fibrosis in marine mammals, and the presence of this phenotype can induce deep alveolar collapse during diving, thereby reducing the risk of DCS during diving.

CONCLUSIONS

The study unveils pivotal genetic signals in cetaceans and other marine mammals, arising through evolution. These genetic signals may influence lung characteristics in marine mammals and have been linked to a reduced risk of developing DCS. Moreover, the research serves as a valuable reference for delving deeper into human diving physiology.

Lung Quantitative Ultrasound to Stage and Monitor Interstitial Lung Diseases

Chronic interstitial lung diseases (ILDs) require frequent point-of-care monitoring. X-ray-based methods lack resolution and are ionizing. Chest computerized tomographic (CT) scans are expensive and provide more radiation. Conventional ultrasound can detect severe lung damage via vertical artifacts (B-lines). However, this information is not quantitative, and the appearance of B-lines is operator- and system-dependent. Here we demonstrate novel ultrasound-based biomarkers to assess severity of ILDs. Lung alveoli scatter ultrasound waves, leading to a complex acoustic signature, which is affected by changes in alveolar density due to ILDs. We exploit ultrasound scattering in the lung and combine Quantitative Ultrasound (QUS) parameters, to develop ultrasound-based biomarkers that significantly correlate to the severity of pulmonary fibrosis and edema in rodent lungs. These innovative QUS biomarkers will be very significant for monitoring severity of chronic ILDs and response to treatment, especially in this new era of miniaturized and highly portable ultrasound devices.

Current Advances in Computational Lung Ultrasound Imaging: A Review

In the field of biomedical imaging, ultrasonography has become common practice, and used as an important auxiliary diagnostic tool with unique advantages, such as being non-ionizing and often portable. This article reviews the state-of-the-art in medical ultrasound (US) image processing and in particular its applications in the examination of the lungs. First, we briefly introduce the basis of lung US (LUS) examination. We focus on (i) the characteristics of lung ultrasonography and (ii) its ability to detect a variety of diseases through the identification of various artifacts exhibiting on LUS images. We group medical US image computing methods into two categories: 1) model-based methods and 2) data-driven methods. We particularly discuss inverse problem-based methods exploited in US image despeckling, deconvolution, and line artifacts detection for the former, while we exemplify various works based on deep/machine learning (ML), which exploit various network architectures through supervised, weakly supervised, and unsupervised learning for the data-driven approaches.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Detecting pulmonary nodules by using ultrasound multiple scattering

Although X-Ray Computed Tomography (CT) is widely used for detecting pulmonary nodules inside the parenchyma, it cannot be used during video-assisted surgical procedures. Real-time, non-ionizing, ultrasound-based techniques are an attractive alternative for nodule localization to ensure safe resection margins during surgery. Conventional ultrasound B-mode imaging of the lung is challenging due to multiple scattering. However, the multiple scattering contribution can be exploited to detect regions inside the lung containing no scatterers. Pulmonary nodules are homogeneous regions in contrast to the highly scattering parenchyma containing millions of air-filled alveoli. We developed a method relying on mapping the multiple scattering contribution inside the highly scattering lung to detect and localize pulmonary nodules. Impulse response matrices were acquired in ex-vivo pig and dog lungs using a linear array transducer to semi-locally investigate the backscattered field. Extracting the multiple-scattering contribution using singular-value decomposition and combining it with a depression detection algorithm allowed us to detect and localize regions with less multiple scattering, associated with the nodules. The feasibility of this method was demonstrated in five ex-vivo lungs containing a total of 20 artificial nodules. Ninety-five percent of the nodules were detected. Nodule depth and diameter significantly correlated with their ex-vivo CT-estimated counterparts (R = 0.960, 0.563, respectively).

In vivo assessment of pulmonary fibrosis and edema in rodents using the backscatter coefficient and envelope statistics

Quantitative ultrasound methods based on the backscatter coefficient (BSC) and envelope statistics have been used to quantify disease in a wide variety of tissues, such as prostate, lymph nodes, breast, and thyroid. However, to date, these methods have not been investigated in the lung. In this study, lung properties were quantified by BSC and envelope statistical parameters in normal, fibrotic, and edematous rat lungs in vivo. The average and standard deviation of each parameter were calculated for each lung as well as the evolution of each parameter with acoustic propagation time within the lung. The transport mean free path and backscattered frequency shift, two parameters that have been successfully used to assess pulmonary fibrosis and edema in prior work, were evaluated in combination with the BSC and envelope statistical parameters. Multiple BSC and envelope statistical parameters were found to provide contrast between control and diseased lungs. BSC and envelope statistical parameters were also significantly correlated with fibrosis severity using the modified Ashcroft fibrosis score as the histological gold standard. These results demonstrate the potential for BSC and envelope statistical parameters to improve the diagnosis of pulmonary fibrosis and edema as well as monitor pulmonary fibrosis.

Clinical Impact of Vertical Artifacts Changing with Frequency in Lung Ultrasound

Background: This study concerns the application of lung ultrasound (LUS) for the evaluation of the significance of vertical artifact changes with frequency and pleural line abnormalities in differentiating pulmonary edema from pulmonary fibrosis. Study Design and Methods: The study was designed as a diagnostic test. Having qualified patients for the study, an ultrasound examination was performed, consistent with a predetermined protocol, and employing convex and linear transducers. We investigated the possibility of B-line artifact conversion depending on the set frequency (2 MHz and 6 MHz), and examined pleural line abnormalities. Results: The study group comprised 32 patients with interstitial lung disease (ILD) (and fibrosis) and 30 patients with pulmonary edema. In total, 1941 cineloops were obtained from both groups and analyzed. The employment of both types of transducers (linear and convex) was most effective (specificity 91%, specificity 97%, positive predictive value (PPV) 97%, negative predictive value (NPV) 91%, LR(+) 27,19, LR(−) 0.097, area under curve (AUC) = 0.936, p = 7 × 10⁻⁶). Interpretation: The best accuracy in differentiating the etiology of B-line artifacts was obtained with the use of both types of transducers (linear and convex), complemented with the observation of the conversion of B-line artifacts to Z-line.