On the influence of imaging parameters on lung ultrasound B-line artifacts, in vitro study

Impact of ultrasound settings on lung vertical artefacts: an observational study in mechanically ventilated patients

Introduction

The number of vertical artefacts (VAs) in lung ultrasound (LUS) impacts patients' clinical management. This study aimed to demonstrate the influence of ultrasound settings on the number of VAs in patients under invasive mechanical ventilation (IMV).

Methods

Patients under IMV were recruited for LUS, including three breathing cycles with a motionless curvilinear probe on the thoracic region with the most VAs. Three experts in LUS were asked about the number of VAs at random, and blinded after altering the settings for a total of 20 test recordings per patient. The correlation between expert classifications was tested after grading the classifications. The number of VAs across clinicians was compared between baseline recordings and test condition recordings to determine statistical differences.

Results

29 patients were enrolled with a median Sequential Organ Failure Assessment score of 6 (interquartile range (IQR) 3). IMV was mainly due to stroke (n=10) and pneumonia (n=6). LUS was made between days 1 and 6 (IQR). Baseline recordings showed a median of 2±2 VAs in inspiration and a median of 1±2 in expiration from a total of 3636 expert classifications, with a strong agreement within patients. A probe frequency of 8 MHz, artefact filtering, speckle reduction and frame average reduced the median VA number by one. A power of -20 dB and dynamic range of 32 dB abolished the VAs. A gain above 90% increased the median number of VAs by one.

Conclusion

In this in vivo study, the LUS settings influenced the VA number in IMV patients, after controlling for physiological and operator confounders.

Ten Questions on Using Lung Ultrasonography to Diagnose and Manage Pneumonia in the Hospital-at-Home Model: Part I-Techniques and Patterns

The hospital-at-home (HaH) model delivers hospital-level acute care, including diagnostics, monitoring, and treatments, in a patient's home. It is particularly effective for managing conditions such as pneumonia. Point-of-care ultrasonography (PoCUS) is a key diagnostic tool in the HaH model, and it often serves as a substitute for imaging-based diagnosis in the HaH setting. Both standard and handheld ultrasound equipment are suitable for lung ultrasound (LUS) evaluation. Curvelinear and linear probes are typically used. Patient positioning depends on their clinical condition and specific diagnostic protocols. To enhance sensitivity, we recommend using at least 10-point protocols supported by studies for pneumonia. Five essential LUS patterns should be identified, including A-line, multiple B-lines (alveolar-interstitial syndrome), confluent B-lines, subpleural consolidation, and consolidation with air bronchogram. Pleural effusion is common, and its internal echogenicity can indicate severity and the need for invasive procedures. The current evidence on various etiologies and types of pneumonia is limited, but LUS demonstrates good sensitivity in detecting abnormal sonographic patterns in atypical pneumonia, tuberculosis, and ventilator-associated pneumonia. Further LUS studies in the HaH setting are required to validate and generalize the findings.

Lung Ultrasound Spectroscopy Applied to the Differential Diagnosis of Pulmonary Diseases: An In Vivo Multicenter Clinical Study

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. However, current LUS approaches are based on subjective interpretation of imaging artifacts, which results in poor specificity as quantitative evaluation lacks. The latter could be improved by adopting LUS spectroscopy of vertical artifacts. Indeed, parameterizing these artifacts with native frequency, bandwidth, and total intensity ( [Formula: see text]) already showed potentials in differentiating pulmonary fibrosis (PF). In this study, we acquired radio frequency (RF) data from 114 patients. These data (representing the largest LUS RF dataset worldwide) were acquired by utilizing a multifrequency approach, implemented with an ULtrasound Advanced Open Platform (ULA-OP). Convex (CA631) and linear (LA533) probes (Esaote, Florence, Italy) were utilized to acquire RF data at three (2, 3, and 4 MHz), and four (3, 4, 5, and 6 MHz) imaging frequencies. A multifrequency analysis was conducted on vertical artifacts detected in patients having cardiogenic pulmonary edema (CPE), pneumonia, or PF. These artifacts were characterized by the three abovementioned parameters, and their mean values were used to project each patient into a feature space having up to three dimensions. Binary classifiers were used to evaluate the performance of these three mean features in differentiating patients affected by CPE, pneumonia, and PF. Acquisitions of multifrequency data performed with linear probe lead to accuracies up to 85.43% in the differential diagnosis of these diseases (convex probes' maximum accuracy was 74.51%). Moreover, the results showed high potentials of mean [Formula: see text] (by itself or combined with other features) in improving LUS specificity.

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.

Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound

Lung ultrasound (LUS) has emerged as a safe and cost-effective modality for assessing lung health, particularly during the COVID-19 pandemic. However, interpreting LUS images remains challenging due to its reliance on artefacts, leading to operator variability and limiting its practical uptake. To address this, we propose a deep learning pipeline for multi-class segmentation of objects (ribs, pleural line) and artefacts (A-lines, B-lines, B-line confluence) in ultrasound images of a lung training phantom. Lightweight models achieved a mean Dice Similarity Coefficient (DSC) of 0.74, requiring fewer than 500 training images. Applying this method in real-time, at up to 33.4 frames per second in inference, allows enhanced visualisation of these features in LUS images. This could be useful in providing LUS training and helping to address the skill gap. Moreover, the segmentation masks obtained from this model enable the development of explainable measures of disease severity, which have the potential to assist in the triage and management of patients. We suggest one such semi-quantitative measure called the B-line Artefact Score, which is related to the percentage of an intercostal space occupied by B-lines and in turn may be associated with the severity of a number of lung conditions. Moreover, we show how transfer learning could be used to train models for small datasets of clinical LUS images, identifying pathologies such as simple pleural effusions and lung consolidation with DSC values of 0.48 and 0.32 respectively. Finally, we demonstrate how such DL models could be translated into clinical practice, implementing the phantom model alongside a portable point-of-care ultrasound system, facilitating bedside assessment and improving the accessibility of LUS.

Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound

Objective.Accurate assessment of pleural line is crucial for the application of lung ultrasound (LUS) in monitoring lung diseases, thereby aim of this study is to develop a quantitative and qualitative analysis method for pleural line.Approach.The novel cascaded deep learning model based on convolution and multilayer perceptron was proposed to locate and segment the pleural line in LUS images, whose results were applied for quantitative analysis of textural and morphological features, respectively. By using gray-level co-occurrence matrix and self-designed statistical methods, eight textural and three morphological features were generated to characterize the pleural lines. Furthermore, the machine learning-based classifiers were employed to qualitatively evaluate the lesion degree of pleural line in LUS images.Main results.We prospectively evaluated 3770 LUS images acquired from 31 pneumonia patients. Experimental results demonstrated that the proposed pleural line extraction and evaluation methods all have good performance, with dice and accuracy of 0.87 and 94.47%, respectively, and the comparison with previous methods found statistical significance (P< 0.001 for all). Meanwhile, the generalization verification proved the feasibility of the proposed method in multiple data scenarios.Significance.The proposed method has great application potential for assessment of pleural line in LUS images and aiding lung disease diagnosis and treatment.

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.

Sonography of the pleura

The CME review presented here is intended to explain the significance of pleural sonography to the interested reader and to provide information on its application. At the beginning of sonography in the 80 s of the 20th centuries, with the possible resolution of the devices at that time, the pleura could only be perceived as a white line. Due to the high impedance differences, the pleura can be delineated particularly well. With the increasing high-resolution devices of more than 10 MHz, even a normal pleura with a thickness of 0.2 mm can be assessed. This article explains the special features of the examination technique with knowledge of the pre-test probability and describes the indications for pleural sonography. Pleural sonography has a high value in emergency and intensive care medicine, preclinical, outpatient and inpatient, in the general practitioner as well as in the specialist practice of pneumologists. The special features in childhood (pediatrics) as well as in geriatrics are presented. The recognition of a pneumothorax even in difficult situations as well as the assessment of pleural effusion are explained. With the high-resolution technology, both the pleura itself and small subpleural consolidations can be assessed and used diagnostically. Both the direct and indirect sonographic signs and accompanying symptoms are described, and the concrete clinical significance of sonography is presented. The significance and criteria of conventional brightness-encoded B-scan, colour Doppler sonography (CDS) with or without spectral analysis of the Doppler signal (SDS) and contrast medium ultrasound (CEUS) are outlined. Elastography and ultrasound-guided interventions are also mentioned. A related further paper deals with the diseases of the lung parenchyma and another paper with the diseases of the thoracic wall, diaphragm and mediastinum.

Effects of Lung Ultrasound Technique and Pleural Line Depth on In Vitro and In Vivo Measurements of Pleural Line Thickness

OBJECTIVE

The aim of the work described here was to determine the effects of imaging protocol, technique and pleural line depth on measured pleural line thickness (PLT).

METHODS

Sonograms were performed on a phantom and healthy volunteers. In vitro, pleural line depth, transducer type (5-1 MHz phased array vs. 13-6 MHz linear array), angle of the pleural line relative to the transducer and distance between the pleural line and focal length were explicitly modified. PLT was measured using electronic calipers. Regression equations described the effects of independent variables on PLT. Factors influencing PLT in vitro were tested in vivo.

RESULTS

In vitro (n = 250 sonograms), PLT was 3.8 (standard error: ±0.24) mm greater when using the phased array compared with the linear transducer (p < 0.001). For every additional centimeter of pleural line depth, PLT increased by 0.96 (±0.081) mm for the phased array (p < 0.001) and 0.26 (±0.019) mm for the linear transducer (p < 0.001). Neither pleural angle nor focal length altered PLT. In vivo (n = 160 sonograms), PLT was 2.56 (±0.06) mm greater when using the phased array (p < 0.001) compared with the linear transducer. PLT increased by 0.67 (±0.060) mm with the phased array (p < 0.001) and 0.25 mm (±0.019) with the linear transducer (p < 0.001) for every additional centimeter between the transducer and the pleura. Together the variables explained 93% of PLT variance in vivo (p < 0.001).

CONCLUSION

PLT measurements are affected by transducer type and pleural line depth. Future studies evaluating PLT as a disease marker should account for confounding by these variables.

Can Artificial Intelligence Aid Diagnosis by Teleguided Point-of-Care Ultrasound? A Pilot Study for Evaluating a Novel Computer Algorithm for COVID-19 Diagnosis Using Lung Ultrasound

With the 2019 coronavirus disease (COVID-19) pandemic, there is an increasing demand for remote monitoring technologies to reduce patient and provider exposure. One field that has an increasing potential is teleguided ultrasound, where telemedicine and point-of-care ultrasound (POCUS) merge to create this new scope. Teleguided POCUS can minimize staff exposure while preserving patient safety and oversight during bedside procedures. In this paper, we propose the use of teleguided POCUS supported by AI technologies for the remote monitoring of COVID-19 patients by non-experienced personnel including self-monitoring by the patients themselves. Our hypothesis is that AI technologies can facilitate the remote monitoring of COVID-19 patients through the utilization of POCUS devices, even when operated by individuals without formal medical training. In pursuit of this goal, we performed a pilot analysis to evaluate the performance of users with different clinical backgrounds using a computer-based system for COVID-19 detection using lung ultrasound. The purpose of the analysis was to emphasize the potential of the proposed AI technology for improving diagnostic performance, especially for users with less experience.

Ultrasound multifrequency strategy to estimate the lung surface roughness, in silico and in vitro results

Lung ultrasound (LUS) is an important imaging modality to assess the state of the lung surface. Nevertheless, LUS is limited to the visual evaluation of imaging artifacts, especially the vertical ones. These artifacts are observed in pathologies characterized by a reduction of dimensions of air-spaces (alveoli). In contrast, there exist pathologies, such as chronic obstructive pulmonary disease (COPD), in which an enlargement of air-spaces can occur, which causes the lung surface to behave essentially as a perfect reflector, thus not allowing ultrasound penetration. This characteristic high reflectivity could be exploited to characterize the lung surface. Specifically, air-spaces of different sizes could cause the lung surface to have a different roughness, whose estimation could provide a way to assess the state of the lung surface. In this study, we present a quantitative multifrequency approach aiming at estimating the lung surface's roughness by measuring image intensity variations along the lung surface as a function of frequency. This approach was tested both in silico and in vitro, and it showed promising results. For the in vitro experiments, radiofrequency (RF) data were acquired from a novel experimental model. The results showed consistency between in silico and in vitro experiments.

Ultra-high frequency lung ultrasound in preterm neonates: a test validation study on interpretation agreement and reliability

OBJECTIVE

To verify if increasing frequency, through the use of ultra-high frequency transducers, has an impact on lung ultrasound pattern recognition.

DESIGN

Test validation study.

SETTING

Tertiary academic referral neonatal intensive care unit.

PATIENTS

Neonates admitted with respiratory distress signs.

INTERVENTIONS

Lung ultrasound performed with four micro-linear probes (10, 15, 20 and 22 MHz), in random order. Anonymised images (600 dpi) were randomly included in a pictorial database: physicians with different lung ultrasound experience (beginners (n=7), competents (n=6), experts (n=5)) blindly assessed it. Conformity and reliability of interpretation were analysed using intraclass correlation coefficient (ICC), area under the curve (AUC) of the multi-class ROC analysis, correlation and multivariate linear regressions (adjusting for frequency, expertise and their interaction).

OUTCOME MEASURES

A (0-3) score based on classical lung ultrasound semiology was given to each image as done in the clinical routine.

RESULTS

ICC (0.902 (95% CI: 0.862 to 0.936), p<0.001) and AUC (0.948, p<0.001) on the whole pictorial database (48 images acquired on 12 neonates), and irrespective of the frequency and physicians' expertise, were excellent. Physicians detected more B-lines with increasing frequency: there was a positive correlation between score and frequency (ρ=0.117, p=0.001); multivariate analysis confirmed the score to be higher using 22 MHz-probes (β=0.36 (0.02-0.7), p=0.041).

CONCLUSION

Overall conformity and reliability of interpretations of lung ultrasound patterns were excellent. There were differences in the identification of the B-patterns and severe B-patterns as increasing probe frequency is associated with higher score given to these patterns.

Ultrasound imaging of lung disease and its relationship to histopathology: An experimentally validated simulation approach

Lung ultrasound (LUS) is a widely used technique in clinical lung assessment, yet the relationship between LUS images and the underlying disease remains poorly understood due in part to the complexity of the wave propagation physics in complex tissue/air structures. Establishing a clear link between visual patterns in ultrasound images and underlying lung anatomy could improve the diagnostic accuracy and clinical deployment of LUS. Reverberation that occurs at the lung interface is complex, resulting in images that require interpretation of the artifacts deep in the lungs. These images are not accurate spatial representations of the anatomy due to the almost total reflectivity and high impedance mismatch between aerated lung and chest wall. Here, we develop an approach based on the first principles of wave propagation physics in highly realistic maps of the human chest wall and lung to unveil a relationship between lung disease, tissue structure, and its resulting effects on ultrasound images. It is shown that Fullwave numerical simulations of ultrasound propagation and histology-derived acoustical maps model the multiple scattering physics at the lung interface and reproduce LUS B-mode images that are comparable to clinical images. However, unlike clinical imaging, the underlying tissue structure model is known and controllable. The amount of fluid and connective tissue components in the lung were gradually modified to model disease progression, and the resulting changes in B-mode images and non-imaging reverberation measures were analyzed to explain the relationship between pathological modifications of lung tissue and observed LUS.

Influence of Ultrasound Settings on Laboratory Vertical Artifacts

OBJECTIVE

The aim of the work described here was to analyze the relationship between the change in ultrasound (US) settings and the vertical artifacts' number, visual rating and signal intensity METHODS: An in vitro phantom consisting of a damp sponge and gelatin mix was created to simulate vertical artifacts. Furthermore, several US parameters were changed sequentially (i.e., frequency, dynamic range, line density, gain, power and image enhancement) and after image acquisition. Five US experts rated the artifacts for number and quality. In addition, a vertical artifact visual score was created to determine the higher artifact rating ("optimal") and the lower artifact rating ("suboptimal"). Comparisons were made between the tested US parameters and baseline recordings.

RESULTS

The expert intraclass correlation coefficient for the number of vertical artifacts was 0.694. The parameters had little effect on the "optimal" vertical artifacts but changed their number. Dynamic range increased the number of discernible vertical artifacts to 3 from 36 to 102 dB.

CONCLUSION

The intensity did not correlate with the visual rating score. Most of the available US parameters did not influence vertical artifacts.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Human-to-AI Interrater Agreement for Lung Ultrasound Scoring in COVID-19 Patients

OBJECTIVES

Lung ultrasound (LUS) has sparked significant interest during COVID-19. LUS is based on the detection and analysis of imaging patterns. Vertical artifacts and consolidations are some of the recognized patterns in COVID-19. However, the interrater reliability (IRR) of these findings has not been yet thoroughly investigated. The goal of this study is to assess IRR in LUS COVID-19 data and determine how many LUS videos and operators are required to obtain a reliable result.

METHODS

A total of 1035 LUS videos from 59 COVID-19 patients were included. Videos were randomly selected from a dataset of 1807 videos and scored by six human operators (HOs). The videos were also analyzed by artificial intelligence (AI) algorithms. Fleiss' kappa coefficient results are presented, evaluated at both the video and prognostic levels.

RESULTS

Findings show a stable agreement when evaluating a minimum of 500 videos. The statistical analysis illustrates that, at a video level, a Fleiss' kappa coefficient of 0.464 (95% confidence interval [CI] = 0.455-0.473) and 0.404 (95% CI = 0.396-0.412) is obtained for pairs of HOs and for AI versus HOs, respectively. At prognostic level, a Fleiss' kappa coefficient of 0.505 (95% CI = 0.448-0.562) and 0.506 (95% CI = 0.458-0.555) is obtained for pairs of HOs and for AI versus HOs, respectively.

CONCLUSIONS

To examine IRR and obtain a reliable evaluation, a minimum of 500 videos are recommended. Moreover, the employed AI algorithms achieve results that are comparable with HOs. This research further provides a methodology that can be useful to benchmark future LUS studies.

Lung Ultrasound Artifacts Interpreted as Pathology Footprints

Background: The original observation that lung ultrasound provides information regarding the physical state of the organ, rather than the anatomical details related to the disease, has reinforced the idea that the observed acoustic signs represent artifacts. However, the definition of artifact does not appear adequate since pulmonary ultrasound signs have shown valuable diagnostic accuracy, which has been usefully exploited by physicians in numerous pathologies. Method: A specific method has been used over the years to analyze lung ultrasound data and to convert artefactual information into anatomical information. Results: A physical explanation of the genesis of the acoustic signs is provided, and the relationship between their visual characteristics and the surface histopathology of the lung is illustrated. Two important sources of potential signal alteration are also highlighted. Conclusions: The acoustic signs are generated by acoustic traps that progressively release previously trapped energy. Consequently, the acoustic signs highlight the presence of acoustic traps and quantitatively describe their distribution on the lung surface; they are not artifacts, but pathology footprints and anatomical information. Moreover, the impact of the dynamic focusing algorithms and the impact of different probes on the visual aspect of the acoustic signs should not be neglected.

New International Guidelines and Consensus on the Use of Lung Ultrasound

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

Vertical Artifacts as Lung Ultrasound Signs: Trick or Trap? Part 2- An Accademia di Ecografia Toracica Position Paper on B-Lines and Sonographic Interstitial Syndrome

Although during the last few years the lung ultrasound (LUS) technique has progressed substantially, several artifacts, which are currently observed in clinical practice, still need a solid explanation of the physical phenomena involved in their origin. This is particularly true for vertical artifacts, conventionally known as B-lines, and for their use in clinical practice. A wider consensus and a deeper understanding of the nature of these artifactual phenomena will lead to a better classification and a shared nomenclature, and, ultimately, result in a more objective correlation between anatomo-pathological data and clinical scenarios. The objective of this review is to collect and document the different signs and artifacts described in the history of chest ultrasound, with a particular focus on vertical artifacts (B-lines) and sonographic interstitial syndrome (SIS). By reviewing the possible physical and anatomical interpretation of the signs and artifacts proposed in the literature, this work also aims to bring order to the available studies and to present the AdET (Accademia di Ecografia Toracica) viewpoint in terms of nomenclature and clinical approach to the SIS.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

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.

B-Lines Lung Ultrasonography Simulation Using Finite Element Method

Introduction: Lung Ultrasonography (LUS) is a fast technique for the diagnosis of patients with respiratory syndromes. B-lines are seen in response to signal reverberations and amplifications into sites with peripheral lung fluid concentration or septal thickening. Mathematical models are commonly applied in biomedicine to predict biological responses to specific signal parameters. Objective: This study proposes a Finite-Element numerical model to simulate radio frequency ultrasonic lines propagated from normal and infiltrated lung structures. For tissue medium, a randomized inhomogeneous data method was used. The simulation implemented in COMSOL® used Acoustic Pressure and Time-Explicit models, which are based on the discontinuous Galerkin method (dG). Results: The RF signals, processed in MATLAB®, resulted in images of horizontal A-lines and vertical B-lines, which were reasonably similar to real images. Discussion: The use of inhomogeneous materials in the model was good enough to simulate the scattering response, similar to others in the literature. The model is useful to study the impact of the lung infiltration characteristics on the appearance of LUS images.

The effect of attenuation inside the acoustic traps on the configuration of vertical artifacts in lung ultrasound: an experimental study with simple models

Purpose

Using simple experimental models for lung ultrasound, we evaluated the relationship of the attenuation inside the sources of vertical artifacts to the echo intensity and attenuation of artifacts.

Methods

As sources of artifacts, we made 10 different hemispherical gel objects with two different mediums (pure agar or agar containing graphite with an attenuation coefficient of 0.5 dB/cm · MHz) and five different diameters (3.6, 5.6, 7.5, 9.5, or 11.4 mm). Ten of each hemispherical gel object were prepared for the statistical analyses. Each object was placed onto a chest wall phantom as the plane of the hemisphere was placed in an upward position. The echo intensity and attenuation of the artifact generated from each object was measured and compared.

Results

For all sizes, the intensity and attenuation of the artifacts in the objects made of agar containing graphite were significantly lower and larger, respectively, than those in the objects made of pure agar. In the objects containing graphite, the intensity decreased when the frequency was changed from 5 to 9 MHz.

Conclusion

Based on this experiment, assessing the intensity and attenuation of vertical artifacts may help estimate the physical composition of sources of vertical artifacts in lung ultrasound.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10396-022-01244-0.

All B-lines are equal, but some B-lines are more equal than others

In this pictorial essay the theme of the differential diagnosis between the different causes of lung interstitial disease will be discussed, which can be detected on lung ultrasound as B lines. In particular, from the experience obtained during the covid-19 pandemic, the term B line may appear too simplified, and new data in the literature show that it is necessary to update the terminology and the differential diagnosis of this ultrasound sign.

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19

Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts (such as A- and B-lines) are of importance. In our framework, we integrate domain knowledge into deep neural networks by inputting anatomical features and LUS artifacts in the form of additional channels containing pleural and vertical artifacts masks along with the raw LUS frames. By explicitly supplying this domain knowledge, standard off-the-shelf neural networks can be rapidly and efficiently finetuned to accomplish various tasks on LUS data, such as frame classification or semantic segmentation. Our framework allows for a unified treatment of LUS frames captured by either convex or linear probes. We evaluated our proposed framework on the task of COVID-19 severity assessment using the ICLUS dataset. In particular, we finetuned simple image classification models to predict per-frame COVID-19 severity score. We also trained a semantic segmentation model to predict per-pixel COVID-19 severity annotations. Using the combined raw LUS frames and the detected lines for both tasks, our off-the-shelf models performed better than complicated models specifically designed for these tasks, exemplifying the efficacy of our framework.

Phenotyping congestion in patients with acutely decompensated heart failure with preserved and reduced ejection fraction: The Decongestion duRing therapY for acute decOmpensated heart failure in HFpEF vs HFrEF- DRY-OFF study

AIMS

To evaluate pulmonary and intravascular congestion at admission and repeatedly during hospitalization for acute decompensated heart failure (ADHF) in HFrEF and HFpEF patients using lung (LUS) and inferior vena cava (IVC) ultrasound.

METHODS AND RESULTS

Three-hundred-fourteen patients (82±9 years; HFpEF =172; HFrEF=142) admitted to Internal Medicine wards for ADHF were enrolled in a multi-center prospective study. At admission HFrEF presented higher indexes of pulmonary and intravascular congestion (LUS-score: 0.9 ± 0.4 vs 0.7 ± 0.4; p<0.01; IVC end-expiratory diameter: 21.6 ± 5.1 mm vs 20±5.5 mm, p<0.01; IVC collapsibility index 24.4 ± 17.4% vs 30.9 ± 21.1% p<0.01) and higher Nt-proBNP values (8010 vs 3900 ng/l; p<0.001). At discharge, HFrEF still presented higher B-scores (0.4 ± 4 vs 0.3 ± 0.4; p = 0.023), while intravascular congestion improved to a greater extent, thus IVC measurements were similar in the two groups. No differences in diuretic doses, urine output, hemoconcentration, worsening renal function were found. At 90-days follow up HF readmission/death did not differ in HFpEF and HFrEF (28% vs 31%, p = 0,48). Residual congestion was associated with HF readmission/death considering the whole population; while intravascular congestion predicted readmission/death in the HFrEF, no association between sonographic indexes and the outcome was found in HFpEF.

CONCLUSIONS

Serial assessment of pulmonary and intravascular congestion revealed a higher burden of fluid overload in HFrEF and, conversely, a greater reduction in intravascular venous congestion with diuretic treatment. Although other factors beyond EF could play a role in congestion/decongestion patterns, our data may be relevant for further phenotyping HF patients, considering the importance of decongestion optimization in the clinical approach.

The Mechanisms Underlying Vertical Artifacts in Lung Ultrasound and Their Proper Utilization for the Evaluation of Cardiogenic Pulmonary Edema

The recent advances in lung ultrasound for the diagnosis of cardiogenic pulmonary edema are outstanding; however, the mechanism of vertical artifacts known as B-lines used for the diagnosis has not yet been fully elucidated. The theory of “acoustic trap” is useful when considering the generation of vertical artifacts. Basic research in several studies supports the theory. Published studies with pilot experiments indicate that clarification of the relationship between the length and intensity of vertical artifacts and physical or acoustic composition of sources may be useful for differentiating cardiogenic pulmonary edema from lung diseases. There is no international consensus with regard to the optimal settings of ultrasound machines even though their contribution to the configuration of vertical artifacts is evident. In the clinical setting, the configuration is detrimentally affected by the use of spatial compound imaging, the placement of the focal point at a deep level, and the use of multiple focus. Simple educational materials using a glass microscope slide also show the non-negligible impact of the ultrasound machine settings on the morphology of vertical artifacts.

Dependence of lung ultrasound vertical artifacts on frequency, bandwidth, focus and angle of incidence: An in vitro study

Lung ultrasound (LUS) is nowadays widely adopted by clinicians to evaluate the state of the lung surface. However, being mainly based on the evaluation of vertical artifacts, whose genesis is still unclear, LUS is affected by qualitative and subjective analyses. Even though semi-quantitative approaches supported by computer aided methods can reduce subjectivity, they do not consider the dependence of vertical artifacts on imaging parameters, and could not be classified as fully quantitative. They are indeed mainly based on scoring LUS images, reconstructed with standard clinical scanners, through the sole evaluation of visual patterns, whose visualization depends on imaging parameters. To develop quantitative techniques is therefore fundamental to understand which parameters influence the vertical artifacts' intensity. In this study, we quantitatively analyzed the dependence of nine vertical artifacts observed in a thorax phantom on four parameters, i.e., center frequency, focal point, bandwidth, and angle of incidence. The results showed how the vertical artifacts are significantly affected by these four parameters, and confirm that the center frequency is the most impactful parameter in artifacts' characterization. These parameters should hence be carefully considered when developing a LUS quantitative approach.

Simple Experimental Models for Elucidating the Mechanism Underlying Vertical Artifacts in Lung Ultrasound: Tools for Revisiting B-Lines

Using simple experimental models, we evaluated the generation, configuration and echo intensity of vertical artifacts by varying the point or plane of contact and height of objects that correspond to sources of vertical artifacts in the subpleural space. We used an ultrasound gel spot to imitate the source and a block of bacon as a chest wall phantom. As the size of the point of contact between the gel spot on the polypropylene sheet and the phantom decreased by peeling the sheet, a vertical artifact measuring ≤1 cm was generated and/or extended deeper, finally reaching 10 cm in depth. Next, objects of different shapes made using gel balls were used to observe the generation of artifacts and measure and compare the echo intensity. For a given shape, the intensity was markedly higher in one model with the point of contact than in the other model with the plane of contact. With the same point or plane of contact, the echo intensity was higher in the taller model. The size of the point or plane of contact and height of the source were observed to be key factors in the generation, length and echo intensity of the artifacts.

Introduction to the special issue on lung ultrasound

The potential of lung ultrasound (LUS) has become manifest in the light of the recent COVID-19 pandemic. The need for a point-of care, quantitative, and widely available assessment of lung condition is critical. However, conventional ultrasound imaging was never designed for lung assessment. This limits LUS to the subjective and qualitative interpretation of artifacts and imaging patterns visible on ultrasound images. A number of research groups have begun to tackle this limitation, and this special issue reports on their most recent findings. Through in silico, in vitro, and in vivo studies (preclinical animal studies and pilot clinical studies on human subjects), the research presented aims at understanding and modelling the physical phenomena involved in ultrasound propagation, and at leveraging these phenomena to extract semi-quantitative and quantitative information relevant to estimate changes in lung structure. These studies are the first steps in unlocking the full potential of lung ultrasound as a relevant tool for lung assessment.

Diagnostic ultrasound imaging of the lung: A simulation approach based on propagation and reverberation in the human body

Although ultrasound cannot penetrate a tissue/air interface, it images the lung with high diagnostic accuracy. Lung ultrasound imaging relies on the interpretation of "artifacts," which arise from the complex reverberation physics occurring at the lung surface but appear deep inside the lung. This physics is more complex and less understood than conventional B-mode imaging in which the signal directly reflected by the target is used to generate an image. Here, to establish a more direct relationship between the underlying acoustics and lung imaging, simulations are used. The simulations model ultrasound propagation and reverberation in the human abdomen and at the tissue/air interfaces of the lung in a way that allows for direct measurements of acoustic pressure inside the human body and various anatomical structures, something that is not feasible clinically or experimentally. It is shown that the B-mode images beamformed from these acoustical simulations reproduce primary clinical features that are used in diagnostic lung imaging, i.e., A-lines and B-lines, with a clear relationship to known underlying anatomical structures. Both the oblique and parasagittal views are successfully modeled with the latter producing the characteristic "bat sign," arising from the ribs and intercostal part of the pleura. These simulations also establish a quantitative link between the percentage of fluid in exudative regions and the appearance of B-lines, suggesting that the B-mode may be used as a quantitative imaging modality.

The impact of multiple concurrent factors on the length of the ultrasound pulmonary vertical artifacts as illustrated through the experimental and numerical analysis of simple models

Nowadays, the diagnostic value of the artefactual information provided by lung ultrasound images is widely recognized by physicians. By carefully observing each individual artifact, an expert physician can derive important information on the distribution of the aerated spaces at the pleural level and, consequently, on the nature of the pulmonary disease. In this paper, a specific visual characteristic of the vertical artifacts (their length) is addressed. The impact of the acoustic properties of the interstitial medium, of the imaging parameters, and of the trap geometry on the length of the vertical artifacts is illustrated through experimental results and through the theoretical analysis of a simple model.

Lungensonographie bei COVID‑19

Die medizinische Versorgung von Patienten, die im Zusammenhang mit der pandemischen Coronaviruserkrankung 2019 („coronavirus disease 2019“, COVID-19) erkrankt sind, stellt für die staatlichen Gesundheitssysteme weltweit eine große Herausforderung dar. Das Virus mit dem Namen „severe acute respiratory syndrome coronavirus 2“ (SARS-CoV-2) zeigt eine hohe Organspezifität zu den unteren Atemwegen. Da bislang weder eine wirksame Therapie noch Impfung gegen das Virus existieren, kommt der diagnostischen Früherkennung eine große Bedeutung zu. Durch den spezifischen Aspekt der überwiegend im peripheren Lungenparenchym beginnenden Infektion ist die Lungensonographie als bildgebende Diagnostikmethode geeignet, Verdachtsfälle bereits im Frühstadium der Erkrankung als solche zu identifizieren. Serielle Ultraschalluntersuchungen an Patienten mit bestätigter Infektion können bettseitig und zeitnah Veränderungen im betroffenen Lungengewebe nachweisen. Dieser Artikel fasst das diagnostische Potenzial der Lungensonographie im Hinblick auf Screening und therapeutische Entscheidungsfindung bei Patienten mit vermuteter oder bestätigter SARS-CoV-2-Pneumonie zusammen.

Quantitative Analysis and Automated Lung Ultrasound Scoring for Evaluating COVID-19 Pneumonia With Neural Networks

As being radiation-free, portable, and capable of repetitive use, ultrasonography is playing an important role in diagnosing and evaluating the COVID-19 Pneumonia (PN) in this epidemic. By virtue of lung ultrasound scores (LUSS), lung ultrasound (LUS) was used to estimate the excessive lung fluid that is an important clinical manifestation of COVID-19 PN, with high sensitivity and specificity. However, as a qualitative method, LUSS suffered from large interobserver variations and requirement for experienced clinicians. Considering this limitation, we developed a quantitative and automatic lung ultrasound scoring system for evaluating the COVID-19 PN. A total of 1527 ultrasound images prospectively collected from 31 COVID-19 PN patients with different clinical conditions were evaluated and scored with LUSS by experienced clinicians. All images were processed via a series of computer-aided analysis, including curve-to-linear conversion, pleural line detection, region-of-interest (ROI) selection, and feature extraction. A collection of 28 features extracted from the ROI was specifically defined for mimicking the LUSS. Multilayer fully connected neural networks, support vector machines, and decision trees were developed for scoring LUS images using the fivefold cross validation. The model with 128×256 two fully connected layers gave the best accuracy of 87%. It is concluded that the proposed method could assess the ultrasound images by assigning LUSS automatically with high accuracy, potentially applicable to the clinics.

Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study

In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.

Numerical study on lung ultrasound B-line formation as a function of imaging frequency and alveolar geometries

Lung ultrasound (LUS) has become a widely adopted diagnostic method for several lung diseases. However, the presence of air inside the lung does not allow the anatomical investigation of the organ. Therefore, LUS is mainly based on the interpretation of vertical imaging artifacts, called B-lines. These artifacts correlate with several pathologies, but their genesis is still partly unknown. Within this framework, this study focuses on the factors affecting the artifacts' formation by numerically simulating the ultrasound propagation within the lungs through the toolbox k-Wave. Since the main hypothesis behind the generation of B-lines relies on multiple scattering phenomena occurring once acoustic channels open at the lung surface, the impact of changing alveolar size and spacing is of interest. The tested domain is of size 4 cm × 1.6 cm, the investigated frequencies vary from 1 to 5 MHz, and the explored alveolar diameters and spacing range from 100 to 400 μm and from 20 to 395 μm, respectively. Results show the strong and entangled relation among the wavelength, the domain geometries, and the artifact visualization, allowing for better understanding of propagation in such a complex medium and opening several possibilities for future studies.

[Lung ultrasound in COVID-19]

Die medizinische Versorgung von Patienten, die im Zusammenhang mit der pandemischen Coronaviruserkrankung 2019 („coronavirus disease 2019“, COVID-19) erkrankt sind, stellt für die staatlichen Gesundheitssysteme weltweit eine große Herausforderung dar. Das Virus mit dem Namen „severe acute respiratory syndrome coronavirus 2“ (SARS-CoV-2) zeigt eine hohe Organspezifität zu den unteren Atemwegen. Da bislang weder eine wirksame Therapie noch Impfung gegen das Virus existieren, kommt der diagnostischen Früherkennung eine große Bedeutung zu. Durch den spezifischen Aspekt der überwiegend im peripheren Lungenparenchym beginnenden Infektion ist die Lungensonographie als bildgebende Diagnostikmethode geeignet, Verdachtsfälle bereits im Frühstadium der Erkrankung als solche zu identifizieren. Serielle Ultraschalluntersuchungen an Patienten mit bestätigter Infektion können bettseitig und zeitnah Veränderungen im betroffenen Lungengewebe nachweisen. Dieser Artikel fasst das diagnostische Potenzial der Lungensonographie im Hinblick auf Screening und therapeutische Entscheidungsfindung bei Patienten mit vermuteter oder bestätigter SARS-CoV-2-Pneumonie zusammen.

Lung ultrasound: The future ahead and the lessons learned from COVID-19

Lung ultrasound (LUS) is a rapidly evolving field of application for ultrasound technologies. Especially during the current pandemic, many clinicians around the world have employed LUS to assess the condition of the lung for patients suspected and/or affected by COVID-19. However, LUS is currently performed with standard ultrasound imaging, which is not designed to cope with the high air content present in lung tissues. Nowadays LUS lacks standardization and suffers from the absence of quantitative approaches. To elevate LUS to the level of other ultrasound imaging applications, several aspects deserve attention from the technical and clinical world. This overview piece tries to provide the reader with a forward-looking view on the future for LUS.