Lung Ultrasound Imaging, a Technical Review

Photoactivated Gas-Generating Nanocontrast Agents for Long-Term Ultrasonic Imaging-Guided Combined Therapy of Tumors

To date, long-term and continuous ultrasonic imaging for guiding the puncture biopsy remains a challenge. In order to address this issue, a multimodality imaging and therapeutic method was developed in the present study to facilitate long-term ultrasonic and fluorescence imaging-guided precision diagnosis and combined therapy of tumors. In this regard, certain types of photoactivated gas-generating nanocontrast agents (PGNAs), capable of exhibiting both ultrasonic and fluorescence imaging ability along with photothermal and sonodynamic function, were designed and fabricated. The advantages of these fabricated PGNAs were then utilized against tumors in vivo, and high therapeutic efficacy was achieved through long-term ultrasonic imaging-guided treatment. In particular, the as-prepared multifunctional PGNAs were applied successfully for the fluorescence-based determination of patient tumor samples collected through puncture biopsy in clinics, and superior performance was observed compared to the clinically used SonoVue contrast agents that are incapable of specifically distinguishing the tumor in ex vivo tissues.

Assessing primary care veterinarians' use of and confidence in performing point-of-care ultrasound

BACKGROUND

Point-of-care ultrasound (POCUS) is gaining popularity in the veterinary field, but there is little information on operator confidence.

METHODS

A survey was distributed to primary care veterinarians (PCVs) via social media between May and July 2020. Details of participants' training in and use of POCUS were recorded. Participants' confidence in using thoracic and abdominal POCUS was also assessed using a five-point Likert scale.

RESULTS

Two hundred and one PCVs used POCUS, of which 32% reported using a non-standardised protocol. Fifty percent of PCVs were self-taught and 17.4% had attended a specific practical course. The median confidence score was 4 out of 5 (interquartile range [IQR] 2-5) for identifying abdominal abnormalities, irrespective of the training method. The median confidence score for thoracic abnormalities was 3 out of 5 (IQR 1-4) for those taught by a colleague or who were self-taught using journal articles or videos.

LIMITATIONS

The survey-based nature of the study relies on self-reporting and is therefore liable to recall bias.

CONCLUSIONS

PCVs' confidence in using POCUS is lacking, particularly with thoracic POCUS. Standardised practical training for PCVs, particularly in thoracic POCUS, would be beneficial. Future studies should explore how best to deliver this training.

Role of autopsy in diagnosing asplenia and right bilobed lung in a fetus with dextrocardia

We present a unique case of a fetus with dextrocardia, asplenia and a right bilobed lung in a primigravida woman in her 20s at 21 weeks' gestation. Prenatal ultrasound examination revealed dextrocardia and other anomalies such as atrioventricular septal defect and situs ambiguous with the gallbladder on the left, leading to termination of the pregnancy. Fetal autopsy confirmed the diagnosis, detected additional findings such as asplenia and right bilobed lung missed on ultrasound and highlighted the importance of autopsy in prenatal diagnosis. This rare case emphasises the value of a comprehensive prenatal assessment, fetal autopsy and a multidisciplinary approach in diagnosing, managing and counselling families affected by congenital anomalies. Timely detection and appropriate genetic counselling can guide affected families in making informed decisions regarding future pregnancies while providing closure and support in their grieving process.

Sono-Elastography: An Ultrasound Quantitative Non-Invasive Measurement to Guide Bacterial Pneumonia Diagnosis in Children

Lung ultrasound (LUS) is, at present, a standard technique for the diagnosis of acute lower respiratory tract infections (ALRTI) and other lung pathologies. Its protocolised use has replaced chest radiography and has led to a drastic reduction in radiation exposure in children. Despite its undeniable usefulness, there are situations in which certain quantitative measurements could provide additional data to differentiate the etiology of some pulmonary processes and thus adapt the treatment. Our research group hypothesises that several lung processes such pneumonia may lead to altered lung tissue stiffness, which could be quantified with new diagnostic tests such as lung sono-elastography (SE). An exhaustive review of the literature has been carried out, concluding that the role of SE for the study of pulmonary processes is currently scarce and poorly studied, particularly in pediatrics. The aim of this review is to provide an overview of the technical aspects of SE and to explore its potential usefulness as a non-invasive diagnostic technique for ALRTI in children by implementing an institutional image acquisition protocol.

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.

Evaluation of Cholesterol Thickness of Blood Vessels Using Photoacoustic Technology

One of the primary indicators of plaque vulnerability is the lipid composition of atherosclerotic plaques. Therefore, the medical industry requires a method to evaluate necrotic nuclei in atherosclerosis imaging with sensitivity. In this regard, photoacoustic imaging is a plaque detection method that provides chemical information on lipids and cholesterol thickness in the arterial walls of the patient. This aspect aims to increase the low-frequency axial resolution by developing a new photoacoustic-based system. A photoacoustic system has been developed to detect the cholesterol thickness of the blood vessels to observe the progression of plaque in the heart's blood vessels. The application of the coherent photoacoustic discontinuous correlation tomography technique, which is based on a novel signal processing, significantly increased the cholesterol oleate's sensitivity to plaque necrosis. By enhancing the quality of thickness detection, the system for measuring the thickness of cholesterol in blood vessels has been reduced to approximately 23 microns. The results show that the phase spectrum peaked at 100 Hz at 58.66 degrees, and at 400 Hz, the phase spectrum was 46.37 degrees. The minimum amplitude is 1.95 at 100 Hz and 17.67 at 400 Hz. In conclusion, it can be stated that photoacoustic imaging as a method based on new technologies is of great importance in medical research, which is based on the use of nonionizing radiation to perform diagnostic processes and measure different types of body tissues.

Fluorescence, ultrasonic and photoacoustic imaging for analysis and diagnosis of diseases

Biomedical imaging technology, which allows us to peer deeply within living subjects and visually explore the delivery and distribution of agents in living things, is producing tremendous opportunities for the early diagnosis and precise therapy of diseases. In this feature article, based on reviewing the latest representative examples of progress together with our recent efforts in the bioimaging field, we intend to introduce three typical kinds of non-invasive imaging technologies, i.e., fluorescence, ultrasonic and photoacoustic imaging, in which optical and/or acoustic signals are employed for analyzing various diseases. In particular, fluorescence imaging possesses a series of outstanding advantages, such as high temporal resolution, as well as rapid and sensitive feedback. Hence, in the first section, we will introduce the latest studies on developing novel fluorescence imaging methods for imaging bacterial infections, cancer and lymph node metastasis in a long-term and real-time manner. However, the issues of imaging penetration depth induced by photon scattering and light attenuation of biological tissue limit their widespread in vivo imaging applications. Taking advantage of the excellect penetration depth of acoustic signals, ultrasonic imaging has been widely applied for determining the location, size and shape of organs, identifying normal and abnormal tissues, as well as confirming the edges of lesions in hospitals. Thus, in the second section, we will briefly summarize recent advances in ultrasonic imaging techniques for diagnosing diseases in deep tissues. Nevertheless, the absence of lesion targeting and dependency on a professional technician may lead to the possibility of false-positive diagnosis. By combining the merits of both optical and acoustic signals, newly-developed photoacoustic imaging, simultaneously featuring higher temporal and spatial resolution with good sensitivity, as well as deeper penetration depth, is discussed in the third secretion. In the final part, we further discuss the major challenges and prospects for developing imaging technology for accurate disease diagnosis. We believe that these non-invasive imaging technologies will introduce a new perspective for the precise diagnosis of various diseases in the future.

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.

B-line Elastography Measurement of Lung Parenchymal Elasticity

This paper proposes a method to determine the elasticity of the lung parenchyma from the B-line Doppler signal observed using continuous shear wave elastography, which uses a small vibrator placed on the tissue surface to propagate continuous shear waves with a vibration frequency of approximately 100 Hz. Since the B-line is generated by multiple reflections in fluid-storing alveoli near the lung surface, the ultrasonic multiple-reflection signal from the B-line is affected by the Doppler shift due to shear waves propagating in the lung parenchyma. When multiple B-lines are observed, the propagation velocity can be estimated by measuring the difference in propagation time between the B-lines. Therefore, continuous shear wave elastography can be used to determine the elasticity of the lung parenchyma by measuring the phase difference of shear wave between the B-lines. In this study, three elastic sponges (soft, medium, and hard) with embedded glass beads were used to simulate fluid-storing alveoli. Shear wave velocity measured using the proposed method was compared with that calculated using Young's modulus obtained from compression measurement. Using the proposed method, the measured shear wave velocities (mean ± S.D.) were 3.78 ± 0.23, 4.24 ± 0.12, and 5.06 ± 0.05 m/s for soft, medium, and hard sponges, respectively, which deviated by a maximum of 5.37% from the values calculated using the measured Young's moduli. The shear wave velocities of the sponge phantom were in a velocity range similar to the mean shear wave velocities of healthy and diseased lungs reported by magnetic resonance elastography (3.25 and 4.54 m/s, respectively). B-line elastography may enable emergency diagnoses of acute lung disease using portable ultrasonic echo devices.

Unsupervised landmark detection and classification of lung infection using transporter neural networks

Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of careful annotation by experts. In this paper, an unsupervised transporter neural network framework with an attention mechanism is proposed to automatically identify relevant landmarks with applications in lung ultrasound (LUS) imaging. The proposed framework identifies key points that provide a concise geometric representation highlighting regions with high structural variation in the LUS videos. In order for the landmarks to be clinically relevant, we have employed acoustic propagation physics driven feature maps and angle-controlled Radon Transformed frames at the input instead of directly employing the gray scale LUS frames. Once the landmarks are identified, the presence of these landmarks can be employed for classification of the given frame into various classes of severity of infection in lung. The proposed framework has been trained on 130 LUS videos and validated on 100 LUS videos acquired from multiple centres at Spain and India. Frames were independently assessed by experts to identify clinically relevant features such as A-lines, B-lines, and pleura in LUS videos. The key points detected showed high sensitivity of 99% in detecting the image landmarks identified by experts. Also, on employing for classification of the given lung image into normal and abnormal classes, the proposed approach, even with no prior training, achieved an average accuracy of 97% and an average F1-score of 95% respectively on the task of co-classification with 3-fold cross-validation.

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.

What Is COVID 19 Teaching Us about Pulmonary Ultrasound?

In lung ultrasound (LUS), the interactions between the acoustic pulse and the lung surface (including the pleura and a small subpleural layer of tissue) are crucial. Variations of the peripheral lung density and the subpleural alveolar shape and its configuration are typically connected to the presence of ultrasound artifacts and consolidations. COVID-19 pneumonia can give rise to a variety of pathological pulmonary changes ranging from mild diffuse alveolar damage (DAD) to severe acute respiratory distress syndrome (ARDS), characterized by peripheral bilateral patchy lung involvement. These findings are well described in CT imaging and in anatomopathological cases. Ultrasound artifacts and consolidations are therefore expected signs in COVID-19 pneumonia because edema, DAD, lung hemorrhage, interstitial thickening, hyaline membranes, and infiltrative lung diseases when they arise in a subpleural position, generate ultrasound findings. This review analyzes the structure of the ultrasound images in the normal and pathological lung given our current knowledge, and the role of LUS in the diagnosis and monitoring of patients with COVID-19 lung involvement.

Normalising lung ultrasound

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Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.

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.

A new standard scoring for interstitial pneumonia based on quantitative analysis of ultrasonographic data: A study on COVID-19 patients

OBJECTIVE

To assess the effectiveness of 3 novel lung ultrasound (LUS)-based parameters: Pneumonia Score and Lung Staging for pneumonia staging and COVID Index, indicating the probability of SARS-CoV-2 infection.

METHODS

Adult patients admitted to the emergency department with symptoms potentially related to pneumonia, healthy volunteers and clinical cases from online accessible databases were evaluated. The patients underwent a clinical-epidemiological questionnaire and a LUS acquisition, following a 14-zone protocol. For each zone, a Pneumonia score from 0 to 4 was assigned by the algorithm and by an expert operator (kept blind with respect to the algorithm results) on the basis of the identified imaging signs and the patient Lung Staging was derived as the highest observed score. The output of the operator was considered as the ground truth. The algorithm calculated also the COVID Index by combining the automatically identified LUS markers with the questionnaire answers and compared with the nasopharyngeal swab results.

RESULTS

Overall, 556 patients were analysed. A high agreement between the algorithm assignments and the expert operator evaluations was observed, both for Pneumonia Score and Lung Staging, with the latter having sensitivity and specificity over 92% both in the discrimination between healthy/sick patients and between sick patients with mild/severe pneumonia. Regarding the COVID Index, an area under the curve of 0.826 was observed for the classification of patients with/without SARS-CoV-2.

CONCLUSION

The proposed methodology allowed the identification and staging of patients suffering from pneumonia with high accuracy. Moreover, it provided the probability of being infected by SARS-CoV-2.

COVID-19 and Non-COVID-19 Classification using Multi-layers Fusion From Lung Ultrasound Images

COVID-19 or related viral pandemics should be detected and managed without hesitation, since the virus spreads very rapidly. Often with insufficient human and electronic resources, patients need to be checked from stable patients using vital signs, radiographic photographs, or ultrasound images. Vital signs do not often offer the right outcome, and radiographic photos have a variety of other problems. Lung ultrasound (LUS) images can provide good screening without a lot of complications. This paper suggests a model of a convolutionary neural network (CNN) that has fewer learning parameters but can achieve strong accuracy. The model has five main blocks or layers of convolution connectors. A multi-layer fusion functionality of each block is proposed to improve the efficiency of the COVID-19 screening method utilizing the proposed model. Experiments are conducted using freely accessible LUS photographs and video datasets. The proposed fusion method has 92.5% precision, 91.8% accuracy, and 93.2% retrieval using the data collection. These efficiency metric levels are considerably higher than those used in any of the state-of-the-art CNN versions.

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.

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.

Quantitative Lung Ultrasound Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study

The application of ultrasound imaging to the diagnosis of lung diseases is nowadays receiving growing interest. However, lung ultrasound (LUS) is mainly limited to the analysis of imaging artifacts, such as B-lines, which correlate with a wide variety of diseases. Therefore, the results of LUS investigations remain qualitative and subjective, and specificity is obviously suboptimal. Focusing on the development of a quantitative method dedicated to the lung, in this work, we present the first clinical results obtained with quantitative LUS spectroscopy when applied to the differentiation of pulmonary fibrosis. A previously developed specific multifrequency ultrasound imaging technique was utilized to acquire ultrasound images from 26 selected patients. The multifrequency imaging technique was implemented on the ULtrasound Advanced Open Platform (ULA-OP) platform and an LA533 (Esaote, Florence, Italy) linear-array probe was utilized. RF data obtained at different imaging frequencies (3, 4, 5, and 6 MHz) were acquired and processed in order to characterize B-lines based on their frequency content. In particular, B-line native frequencies (the frequency at which a B-line exhibits the highest intensity) and bandwidth (the range of frequencies over which a B-line shows intensities within -6 dB from its highest intensity), as well as B-line intensity, were analyzed. The results show how the analysis of these features allows (in this group of patients) the differentiation of fibrosis with a sensitivity and specificity equal to 92% and 92%, respectively. These promising results strongly motivate toward the extension of the clinical study, aiming at analyzing a larger cohort of patients and including a broader range of pathologies.

Automatic Pleural Line Extraction and COVID-19 Scoring From Lung Ultrasound Data

Recent works highlighted the significant potential of lung ultrasound (LUS) imaging in the management of subjects affected by COVID-19. In general, the development of objective, fast, and accurate automatic methods for LUS data evaluation is still at an early stage. This is particularly true for COVID-19 diagnostic. In this article, we propose an automatic and unsupervised method for the detection and localization of the pleural line in LUS data based on the hidden Markov model and Viterbi Algorithm. The pleural line localization step is followed by a supervised classification procedure based on the support vector machine (SVM). The classifier evaluates the healthiness level of a patient and, if present, the severity of the pathology, i.e., the score value for each image of a given LUS acquisition. The experiments performed on a variety of LUS data acquired in Italian hospitals with both linear and convex probes highlight the effectiveness of the proposed method. The average overall accuracy in detecting the pleura is 84% and 94% for convex and linear probes, respectively. The accuracy of the SVM classification in correctly evaluating the severity of COVID-19 related pleural line alterations is about 88% and 94% for convex and linear probes, respectively. The results as well as the visualization of the detected pleural line and the predicted score chart, provide a significant support to medical staff for further evaluating the patient condition.

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

Idiopathic pulmonary fibrosis (IPF) affects 200 000 patients in the United States of America. IPF is responsible for changes in the micro-architecture of the lung parenchyma, such as thickening of the alveolar walls, which reduces compliance and elasticity. In this study, we verify the hypothesis that changes in the microarchitecture of the lung parenchyma can be characterized by exploiting multiple scattering of ultrasound waves by the alveolar structure. Ultrasound propagation in a highly scattering regime follows a diffusion process, which can be characterized using the diffusion constant. We hypothesize that in a fibrotic lung, the thickening of the alveolar wall reduces the amount of air (compared with a healthy lung), thereby minimizing the scattering events. Pulmonary fibrosis is created in Sprague-Dawley rats by instilling bleomycin into the airway. The rats are studied within 3 weeks after bleomycin administration. Using a 128-element linear array transducer operating at 7.8 MHz, in vivo experimental data are obtained from Sprague-Dawley rats and the transport mean free path (L*) and backscatter frequency shift (BFS) are evaluated. Significant differences ( ) in the L* values between control and fibrotic rats and in the BFS values between fibrotic and edematous rats showcase the potential of these parameters for diagnosis and monitoring of IPF.

Lung Ultrasound May Support Diagnosis and Monitoring of COVID-19 Pneumonia

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease (COVID-19) is characterized by severe pneumonia and/or acute respiratory distress syndrome in about 20% of infected patients. Computed tomography (CT) is the routine imaging technique for diagnosis and monitoring of COVID-19 pneumonia. Chest CT has high sensitivity for diagnosis of COVID-19, but is not universally available, requires an infected or unstable patient to be moved to the radiology unit with potential exposure of several people, necessitates proper sanification of the CT room after use and is underutilized in children and pregnant women because of concerns over radiation exposure. The increasing frequency of confirmed COVID-19 cases is striking, and new sensitive diagnostic tools are needed to guide clinical practice. Lung ultrasound (LUS) is an emerging non-invasive bedside technique that is used to diagnose interstitial lung syndrome through evaluation and quantitation of the number of B-lines, pleural irregularities and nodules or consolidations. In patients with COVID-19 pneumonia, LUS reveals a typical pattern of diffuse interstitial lung syndrome, characterized by multiple or confluent bilateral B-lines with spared areas, thickening of the pleural line with pleural line irregularity and peripheral consolidations. LUS has been found to be a promising tool for the diagnosis of COVID-19 pneumonia, and LUS findings correlate fairly with those of chest CT scan. Compared with CT, LUS has several other advantages, such as lack of exposure to radiation, bedside repeatability during follow-up, low cost and easier application in low-resource settings. Consequently, LUS may decrease utilization of conventional diagnostic imaging resources (CT scan and chest X-ray). LUS may help in early diagnosis, therapeutic decisions and follow-up monitoring of COVID-19 pneumonia, particularly in the critical care setting and in pregnant women, children and patients in areas with high rates of community transmission.

Ultrasound Elastography for Lung Disease Assessment

Ultrasound elastography (US-E) is a noninvasive, safe, cost-effective and reliable technique to assess the mechanical properties of soft tissue and provide imaging biomarkers for pathological processes. Many lung diseases such as acute respiratory distress syndrome, chronic obstructive pulmonary disease, and interstitial lung disease are associated with dramatic changes in mechanical properties of lung tissues. Nevertheless, US-E is rarely used to image the lung because it is filled with air. The large difference in acoustic impedance between air and lung tissue results in the reflection of the ultrasound wave at the lung surface and, consequently, the loss of most ultrasound energy. In recent years, there has been an increasing interest in US-E applications in evaluating lung diseases. This article provides a comprehensive review of the technological advances of US-E research on lung disease diagnosis. We introduce the basic principles and major techniques of US-E and provide information on various applications in lung disease assessment. Finally, the potential applications of US-E to the diagnosis of COVID-19 pneumonia is discussed.

Artifactual Lung Ultrasonography: It Is a Matter of Traps, Order, and Disorder

When inspecting the lung with standard ultrasound B-mode imaging, numerous artifacts can be visualized. These artifacts are useful to recognize and evaluate several pathological conditions in Emergency and Intensive Care Medicine. More recently, the interest of the Pulmonologists has turned to the echographic study of the interstitial pathology of the lung. In fact, all lung pathologies which increase the density of the tissue, and do not consolidate the organ, are characterized by the presence of ultrasound artifacts. Many studies of the past have only assessed the number of vertical artifacts (generally known as B-Lines) as a sign of disease severity. However, recent observations suggest that the appearance of the individual artifacts, their variability, and their internal structure, may play a role for a non-invasive characterization of the surface of the lungs, directing the diagnoses and identifying groups of diseases. In this review, we discuss the meaning of lung ultrasound artifacts, and introduce hypothesis on the correlation between their presence and the structural variation of the sub-pleural tissue in light of current knowledge of the acoustic properties of the pleural plane.