Characterization of the Lung Parenchyma Using Ultrasound Multiple Scattering

The respective and dependent effects of scattering and bone matrix absorption on ultrasound attenuation in cortical bone

Cortical bone is characterized by a dense solid matrix permeated by fluid-filled pores. Ultrasound scattering has potential for the non-invasive evaluation of changes in bone porosity. However, there is an incomplete understanding of the impact of ultrasonic absorption in the solid matrix on ultrasound scattering. In this study, maps were derived from scanning acoustic microscopy images of human femur cross-sections. Finite-difference time domain ultrasound scatter simulations were conducted on these maps. Pore density, diameter distribution of the pores, and nominal absorption values in the solid and fluid matrices were controlled. Ultrasound pulses with a central frequency of 8.2 MHz were propagated, both in through-transmission and backscattering configurations. From these data, the scattering, bone matrix absorption, and attenuation extinction lengths were calculated. The results demonstrated that as absorption in the solid matrix was varied, the scattering, absorption, and attenuation extinction lengths were significantly impacted. It was shown that for lower values of absorption in the solid matrix (less than 2 dB mm-1), attenuation due to scattering dominates, whereas at higher values of absorption (more than 2 dB mm-1), attenuation due to absorption dominates. This will impact how ultrasound attenuation and scattering parameters can be used to extract quantitative information on bone microstructure.

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.

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.

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.

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.

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.

Weight of single and recurrent scattering in the reflection matrix of complex media

In a heterogeneous medium, the wave field can be decomposed as an infinite series known as the Born expansion. Each term of the Born expansion corresponds to a scattering order, it is thus theoretically possible to discriminate single and multiple scattering contribution to the field. Experimentally, what is actually measured is the total field in which all scattering orders interfere. Conventional imaging methods usually rely on the assumption that the multiple scattering contribution can be disregarded. In a back-scattering configuration, this assumption is valid for small depths, and begins to fail for depths larger than the scattering mean-free path ℓ_{s}. It is therefore a key issue to estimate the relative amount of single and multiple scattering in experimental data. To this end, a single-scattering estimator ρ[over ̂] computed from the reflection matrix has been introduced in order to assess the weight of single scattering in the backscattered wave field. In this paper, the meaning of this estimator is investigated and a particular attention is given to recurrent scattering. In a diffraction-limited experiment, a multiple scattering sequence is said to be recurrent if the first and last scattering events occur in the same resolution cell. Recurrent scattering is shown to be responsible for correlations between single scattering and higher scattering orders of the Born expansion, inducing a bias to the estimator ρ[over ̂] that should rather be termed confocal scattering ratio. Interestingly, a more robust estimator is built by projecting the reflection matrix in a focused basis. The argument is sustained by numerical simulations as well as ultrasonic data obtained around 1.5 MHz in a model medium made of nylon rods immersed in water. From a more general perspective, this work raises fundamental questions about the impact of recurrent scattering on wave imaging.

Quantitative Assessment of Lung Ultrasound Grayscale Images Based on Shannon Entropy for the Detection of Pulmonary Aeration: An Animal Study

OBJECTIVE

Lung ultrasound (LUS) is a radiation-free, affordable, and bedside monitoring method that can detect changes in pulmonary aeration before hypoxic damage. However, visual scoring methods of LUS only enable subjective diagnosis. Therefore, quantitative analysis of LUS is necessary for obtaining objective information on pulmonary aeration. Because raw data are not always available in conventional ultrasound systems, Shannon entropy (ShanEn) of information theory without the requirement of raw data is valuable. In this study, we explored the feasibility of ShanEn estimated through grayscale histogram (GSH) analysis of LUS images for the quantification of pulmonary aeration.

METHODS

Different degrees of pulmonary aeration caused by edema was induced in 32 male New Zealand rabbits intravenously injected with 0.1 mL/kg saline (the control group) and 0.025, 0.05, and 0.1 mL/kg oleic acid (mild, moderate, and severe groups, respectively). In vivo grayscale LUS images were acquired using a commercial point-of-care ultrasound system for estimation of GSH and corresponding ShanEn. Both lungs of each rabbit were dissected, weighed, and dried to determine the wet weight-to-dry weight ratio (W/D) through gravimetry.

RESULTS

The determination coefficients of linear correlations between ShanEn and W/D increased from 0.0487 to 0.7477 with gain and dynamic range (DR). In contrast to visual scoring methods of pulmonary aeration that use median gain and low DR, ShanEn for quantifying pulmonary aeration requires high gain and DR.

CONCLUSION

The current findings indicate that ShanEn estimated through GSH analysis of LUS images acquired using conventional ultrasonic imaging systems has great potential to provide objective information on pulmonary aeration.

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.

Operative Use of Thoracic Ultrasound in Respiratory Medicine: A Clinical Study

For over 15 years, thoracic ultrasound has been applied in the evaluation of numerous lung diseases, demonstrating a variable diagnostic predictive power compared to traditional imaging techniques such as chest radiography and CT. However, in unselected pulmonary patients, there are no rigorous scientific demonstrations of the complementarity of thoracic ultrasound with traditional and standardized imaging techniques that use radiation. In this study 101 unselected pulmonary patients were evaluated blindly with ultrasound chest examinations during their hospital stay. Other instrumental examinations, carried out during hospitalization, were standard chest radiography, computed tomography (CT), and, when needed, radioisotopic investigation and cardiac catheterization. The operator who performed the ultrasound examinations was unaware of the anamnestic and clinical data of the patients. Diffuse fibrosing disease was detected with a sensitivity, specificity and diagnostic accuracy of 100%, 95% and 97%, respectively. In pleural effusions, ultrasound showed a sensitivity, specificity and diagnostic accuracy of 100%. In consolidations, the sensitivity, specificity and diagnostic accuracy were 83%, 98% and 93%, respectively. Low values of sensitivity were recorded for surface nodulations of less than one centimeter. Isolated subpleural ground glass densities were identified as White Lung with a sensitivity of 72% and a specificity of 86%. Only the associations Diffuse ultrasound findings/Definitive fibrosing disease, Ultrasound Consolidation/Definitive consolidation and non-diffuse ultrasound artefactual features/Definitive vascular pathology (pulmonary hypertension, embolism) were statistically significant with adjusted residuals of 7.9, 7 and 4.1, respectively. The obtained results show how chest ultrasound is an effective complementary diagnostic tool for the pulmonologist. When performed, as a complement to the patient’s physical examination, it can restrict the diagnostic hypothesis in the case of pleural effusion, consolidation and diffuse fibrosing disease of the lung.

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.

Quantifying lung aeration in neonatal lambs at birth using lung ultrasound

BACKGROUND

Lung ultrasound (LUS) is a safe and non-invasive tool that can potentially assess regional lung aeration in newborn infants and reduce the need for X-ray imaging. LUS produces images with characteristic artifacts caused by the presence of air in the lung, but it is unknown if LUS can accurately detect changes in lung air volumes after birth. This study compared LUS images with lung volume measurements from high-resolution computed tomography (CT) scans to determine if LUS can accurately provide relative measures of lung aeration.

METHODS

Deceased near-term newborn lambs (139 days gestation, term ∼148 days) were intubated and the chest imaged using LUS (bilaterally) and phase contrast x-ray CT scans at increasing static airway pressures (0-50 cmH₂O). CT scans were analyzed to calculate regional air volumes and correlated with measures from LUS images. These measures included (i) LUS grade; (ii) brightness (mean and coefficient of variation); and (iii) area under the Fourier power spectra within defined frequency ranges.

RESULTS

All LUS image analysis techniques correlated strongly with air volumes measured by CT (p < 0.01). When imaging statistics were combined in a multivariate linear regression model, LUS predicted the proportion of air in the underlying lung with moderate accuracy (95% prediction interval ± 22.15%, r ² = 0.71).

CONCLUSION

LUS can provide relative measures of lung aeration after birth in neonatal lambs. Future studies are needed to determine if LUS can also provide a simple means to assess air volumes and individualize aeration strategies for critically ill newborns in real time.

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.

Detecting pulmonary nodules by using ultrasound multiple scattering

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

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.

Use of Lung Ultrasound for Assessment of Lung Recruitment Maneuvers in Patients with ARDS

BACKGROUND: Positive pressure mechanical ventilation is a non-physiological intervention that saves lives but is not free of important side effects. It invariably results in different degrees of collapse of small airways. Recruitment maneuver (RM) aims to resolve lung collapse by a brief and controlled increment in airway pressure while positive end-expiratory pressure (PEEP) afterward keeps the lungs open. Therefore, ideally RM and PEEP selection must be individualized and this can only be done when guided by specific monitoring tools since lung’s opening and closing pressures vary among patients with different lung conditions. AIM: The aim of this study was to explore the clinical value of ultrasonic monitoring in the assessment of pulmonary recruitment and the best PEEP. PATIENTS AND METHODS: This study was conducted on 120 patients, 30 were excluded as in whom lung collapse cannot be confirmed then the rest were 90 patients from whom another 25 patients excluded as they were hemodynamically unstable the rest 65 patients were divided into two groups: Group A: Included 50 mechanically ventilated patients with ARDS, underwent lung recruitment using lung ultrasound and Group B: Included 15 mechanically ventilated patients with ARDS, underwent lung recruitment using oxygenation index. This prospective study was held at many critical care departments around Egypt. RESULTS: We noticed that lung recruitment in both groups significantly increased Pao2/Fio2 ratio immediately after recruitment compared with basal state and also significantly increase dynamic compliance compared with basal state. The increase in PF ratio immediately was significantly more in ultrasound group than in oxygenation group. Furthermore, we noticed that that P/F ratio 12 h after recruitment decreased compared with P/F ratio immediately after recruitment but significantly increased compared with basal state before recruitment and also we found that the increase in P/F ratio 12 h after recruitment was more significantly in lung ultrasound group than in oxygenation group. Furthermore, we noticed that lung recruitment (both lung ultrasound and oxygenation group) significantly increase RV function using TAPSE compared with basal state. Both opening pressure and optimal PEEP were significantly higher in lung ultrasound group than in oxygenation group. In our study, opening pressure was 37.28 ± 1.25 in lung ultrasound group and was 36.67±0.98 in oxygenation group and optimal PEEP was 14.64 ± 1.08 in lung ultrasound group and was 13.13 ± 0.74 in oxygenation group. CONCLUSION: Lung US is an effective mean of evaluating and guiding alveolar recruitment in ARDS. Compared with the maximal oxygenation–guided method, the protocol for reaeration in US-guided lung recruitment achieved a higher opening pressure, resulted in greater improvements in lung aeration, and substantially reduced lung heterogeneity in ARDS.

Lung ultrasound: a narrative review and proposed protocol for patients admitted to Cardiac Rehabilitation Unit

Lung ultrasonography (LUS) has become in the last 10 years a technique that has reduced the need of second level diagnostic methods such as chest X-ray (CXR) and computerize tomography (CT) for the diagnostic imaging of lung and pleural space, throughout its diagnostic accuracy, radiation free, low cost, real time and bedside approach. The common use of LUS has been recently extend to cardiac and pulmonary disease even in context of Cardiac Rehabilitation Unit and it could be an additional tool for physiotherapist for the management of patients during Rehabilitation course. The authors performed a literature review in PubMed and suggested a new standardize protocol for LUS, based on guidelines and expert consensus document, for patients admitted to Cardiac Rehabilitation Unit. In this protocol, LUS should be performed in six scan each hemithorax, covering twelve imagine regions. For each scan will be noted a specific physiologic or pathological patterns. Furthermore, we suggest for each patient, the use of the Lung Ultrasound Score (LUS score) to obtain a global view of lung aeration and to monitor any changes during the hospitalization. An increase in score range indicates a more severe condition. This Lung Ultrasonography Protocol should be performed in all patients at the time of admission to Cardiac Rehabilitation Unit to monitoring the aeration of the lungs and the possible lung and/or pleura complications after a cardiac disease avoiding the use of second level surveys.

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.

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.

Lung Ultrasound Surface Wave Elastography for Assessing Patients With Pulmonary Edema

B-Mode ultrasound insonation of lungs that are dense with extravascular lung water (EVLW) produces characteristic reverberation artifacts termed B-lines. The number of B-lines present demonstrates reasonable correlation to the amount of EVLW. However, analysis of B-line artifacts generated by this modality is semi-quantitative relying on visual interpretation, and as a result, can be subject to inter-observer variability. The purpose of this study was to translate the use of a novel, quantitative lung ultrasound surface wave elastography technique (LUSWE) into the bedside assessment of pulmonary edema in patients admitted with acute congestive heart failure. B-mode lung ultrasound and LUSWE assessment of the lungs were performed using anterior and lateral intercostal spaces in the supine patient. 14 patients were evaluated at admission with reassessment performed 1-2 days after initiation of diuretic therapy. Each exam recorded the total lung B-lines, lung surface wave speeds (at 100, 150, and 200 Hz) and net fluid balance. The patient cohort experienced effective diuresis (average net fluid balance of negative 2.1 liters) with corresponding decrease in pulmonary edema visualized by B-mode ultrasound (average decrease of 13 B-Lines). In addition, LUSWE demonstrated a statistically significant reduction in the magnitude of wave speed from admission to follow-up. The reduction in lung surface wave speed suggests a decrease in lung stiffness (decreased elasticity) mediated by successful reduction of pulmonary edema. In summary, LUSWE is a noninvasive technique for quantifying elastic properties of superficial lung tissue that may prove useful as a diagnostic test, performed at the bedside, for the quantitative assessment of pulmonary edema.

Lung mass density prediction using machine learning based on ultrasound surface wave elastography and pulmonary function testing

OBJECTIVE

The objective of this study is to predict in vivo lung mass density for patients with interstitial lung disease using different gradient boosting decision tree (GBDT) algorithms based on measurements from lung ultrasound surface wave elastography (LUSWE) and pulmonary function testing (PFT).

METHODS

Age and weight of study subjects (57 patients with interstitial lung disease and 20 healthy subjects), surface wave speeds at three vibration frequencies (100, 150, and 200 Hz) from LUSWE, and predicted forced expiratory volume (FEV1% pre) and ratio of forced expiratory volume to forced vital capacity (FEV1%/FVC%) from PFT were used as inputs while lung mass densities based on the Hounsfield Unit from high resolution computed tomography (HRCT) were used as labels to train the regressor in three GBDT algorithms, XGBoost, CatBoost, and LightGBM. 80% (20%) of the dataset was used for training (testing).

RESULTS

The results showed that predictions using XGBoost regressor obtained an accuracy of 0.98 in the test dataset.

CONCLUSION

The obtained results suggest that XGBoost regressor based on the measurements from LUSWE and PFT may be able to noninvasively assess lung mass density in vivo for patients with pulmonary disease.

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.

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.

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

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

Lesion Imaging and Target Detection in Multiple Scattering (LITMUS) Media

We present an ultrasound algorithm [lesion imaging and target detection in multiple scattering (LITMUS)] suited to image lesions (hypoechoic) or targets (hyperechoic) in highly complex structures. In such media, standard ultrasound imaging techniques fail to detect lesions or targets due to aberrations and the loss of linearity between propagation distance and propagation time, caused by multiple scattering of ultrasound waves. The present algorithm (LITMUS) has the capability to predict the location as well as the size of such lesions/targets by using the multiple scattered ultrasound signals to its advantage. In this experimental and computational study, we use an ultrasound linear array. Lesions/targets are embedded at varying depths inside multiple scattering media with varying density of scatterers. In the simulations, plastic scatterers are used as the source of multiple scattering in a propagation medium (water). In the experiments, melamine sponges are used, with air alveoli as the scattering source. For multiple locations along the transducer, the incoherent backscattered intensity of the backscattered signals is extracted and the linear growth of the diffusive halo over time is tracked. Sudden changes in this growth indicate the presence of a region with reduced heterogeneity, indicative of the presence of a lesion/target. This methodology is combined with a depression detection algorithm to predict the size and location of the lesion/targets with high fidelity, despite the presence of strong heterogeneity and multiple scattering.

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.

Real-time multi-frequency ultrasound imaging for quantitative lung ultrasound - first clinical results

Lung ultrasound imaging is a fast-evolving field of application for ultrasound technologies. However, most diagnoses are currently performed with imaging protocols that assume a quasi-homogeneous speed of sound in the volume of interest. When applied to the lung, due to the presence of air, this assumption is unrealistic. Consequently, diagnoses are often based on imaging artifacts and thus qualitative and subjective. In this paper, we present an image formation protocol that is capable of capturing the frequency dependence of well-known artifacts (B-lines) and visualizing it in real time, ultimately providing a quantitative assessment of the signals received from the lung. Previous in vitro studies have shown the potential of B-lines native-frequency for the characterization of bubbly medium, but this paper presents the first results on clinical data. The image formation process has been designed to work on lung tissue, and ultrasound images generated with four orthogonal bands centered at 3, 4, 5 and 6 MHz can be acquired and displayed in real time. Results show that B-lines can be characterized on the basis of their native frequency in vivo and open the way toward real-time quantitative lung ultrasound imaging.

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

The clinical relevance of lung ultrasonography (LUS) has been rapidly growing since the 1990s. However, LUS is mainly based on the evaluation of visual artifacts (also called B-lines), leading to subjective and qualitative diagnoses. The formation of B-lines remains unknown and, hence, researchers need to study their origin to allow clinicians to quantitatively evaluate the state of lungs. This paper investigates an ambiguity about the formation of B-lines, leading to the formulation of two main hypotheses. The first hypothesis states that the visualization of these artifacts is linked only to the dimension of the emitted beam, whereas the second associates their appearance to specific resonance phenomena. To verify these hypotheses, the frequency spectrum of B-lines was studied by using dedicated lung-phantoms. A research programmable platform connected to an LA533 linear array probe was exploited both to implement a multifrequency approach and to acquire raw radio frequency data. The strength of each artifact was measured as a function of frequency, focal point, and transmitting aperture by means of the artifact total intensity. The results show that the main parameter that influences the visualization of B-lines is the frequency rather than the focal point or the number of transmitting elements.

Distortion matrix approach for ultrasound imaging of random scattering media

Focusing waves inside inhomogeneous media is a fundamental problem for imaging. Spatial variations of wave velocity can strongly distort propagating wave fronts and degrade image quality. Adaptive focusing can compensate for such aberration but is only effective over a restricted field of view. Here, we introduce a full-field approach to wave imaging based on the concept of the distortion matrix. This operator essentially connects any focal point inside the medium with the distortion that a wave front, emitted from that point, experiences due to heterogeneities. A time-reversal analysis of the distortion matrix enables the estimation of the transmission matrix that links each sensor and image voxel. Phase aberrations can then be unscrambled for any point, providing a full-field image of the medium with diffraction-limited resolution. Importantly, this process is particularly efficient in random scattering media, where traditional approaches such as adaptive focusing fail. Here, we first present an experimental proof of concept on a tissue-mimicking phantom and then, apply the method to in vivo imaging of human soft tissues. While introduced here in the context of acoustics, this approach can also be extended to optical microscopy, radar, or seismic imaging.

Physical Mechanisms Providing Clinical Information From Ultrasound Lung Images: Hypotheses and Early Confirmations

In standard B mode imaging, a set of ultrasound pulses is used to reconstruct a 2-D image even though some of the assumptions needed to do this are not fully satisfied. For this reason, ultrasound medical images show numerous artifacts which physicians recognize and evaluate as part of their diagnosis since even one artifact can provide clinical information. Understanding the physical mechanisms at the basis of the formation of an artifact is important to identify the physiopathological state of the biological medium which generated the artifact. Ultrasound lung images are a significant example of this challenge since everything that is represented beyond the thickness of the chest wall ( ≈ 2 cm) is artifactual information. A convincing physical explanation of the genesis of important ultrasound lung artifacts does not exist yet. Physicians simply base their diagnosis on a correlation observed over the years between the manifestation of some artifacts and the occurrence of particular lung pathologies. In this article, a plausible genesis of some important lung artifacts is suggested.

Lung Ultrasound Imaging, a Technical Review

Lung ultrasound (LUS) is a growing and fascinating field of application for ultrasound imaging. Despite the difficulties in imaging an organ largely filled with air, the potential benefits originating from an effective ultrasound method focusing on monitoring and diagnosing lung diseases represent a tremendous stimulus for research in this direction. This paper presents a technical review where, after a brief historical overview, the current limitations of LUS imaging are discussed together with a description of the physical phenomena at stake. Next, the paper focuses on the latest technical developments of LUS.

Toward Noninvasive Mapping of Diffuse Scattering in the Presence of Motion

Ultrasonic coda wave analysis techniques localize defects in fields such as seismography and nondestructive testing. In medical ultrasound, these techniques might provide novel mapping of tissue properties in diseases characterized by local fibrosis. In this work, we present an approach for localizing variation in scattering properties in the diffuse regime with an array transducer in medical ultrasound. This approach estimates coda wave decorrelation as the array is displaced by 0.5 mm, allowing data acquisition at two slightly different spatial locations. An inverse problem is solved as in nondestructive testing based on coda wave decorrelation estimates and a locally-estimated diffusion constant. The developed approach is demonstrated in a tissue-mimicking phantom to assess sensitivity to variation in scattering properties. Next, the ability of the approach for localizing regions of increased multiple scattering in biological tissues is assessed with a large multiple scattering bead in an ex vivo porcine cardiac sample. Through these experiments, the ability to map variation in multiple scattering is demonstrated for the first time, with a mean localization error of 1.42 ± 3.5 mm for this low-resolution mapping technique. While the goal of this technique is to map defects in the diffuse regime rather than to develop a conventional image, contrast ratios in the resulting images were in good agreement with scattering concentrations in phantom studies: 1.98 ± 0.05 for a 2× scattering target, 1.37 ± 0.02 for a 1.4× scattering target, 0.65 ± 0.02 for a 0.7× scattering target, and 0.49 ± 0.03 for a 0.5× scattering targets.

Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as pore diameter (φ), pore density (ρ) and porosity (ν) of cortical bone from ultrasound frequency dependent attenuation using an artificial neural network (ANN). First, heterogeneous structures with controlled pore diameters and pore densities (mono-disperse) were generated, to mimic simplified structure of cortical bone. Then, more realistic structures were obtained from high resolution CT scans of human cortical bone. 2-D finite-difference time-domain simulations were conducted to calculate the frequency-dependent attenuation in the 1-8 MHz range. An ANN was then trained with the ultrasonic attenuation at different frequencies as the input feature vectors while the output was set as the micro-architectural parameters (pore diameter, pore density and porosity). The ANN is composed of three fully connected dense layers with 24, 12 and 6 neurons, connected to the output layer. The dataset was trained over 6000 epochs with a batch size of 16. The trained ANN exhibits the ability to predict the micro-architectural parameters with high accuracy and low losses. ANN approaches could potentially be used as a tool to help inform physics-based modelling of ultrasound propagation in complex media such as cortical bone. This will lead to the solution of inverse-problems to retrieve bone micro-architectural parameters from ultrasound measurements for the non-invasive diagnosis and monitoring osteoporosis.

Localizing B-Lines in Lung Ultrasonography by Weakly Supervised Deep Learning, In-Vivo Results

Lung ultrasound (LUS) is nowadays gaining growing attention from both the clinical and technical world. Of particular interest are several imaging-artifacts, e.g., A- and B- line artifacts. While A-lines are a visual pattern which essentially represent a healthy lung surface, B-line artifacts correlate with a wide range of pathological conditions affecting the lung parenchyma. In fact, the appearance of B-lines correlates to an increase in extravascular lung water, interstitial lung diseases, cardiogenic and non-cardiogenic lung edema, interstitial pneumonia and lung contusion. Detection and localization of B-lines in a LUS video are therefore tasks of great clinical interest, with accurate, objective and timely evaluation being critical. This is particularly true in environments such as the emergency units, where timely decision may be crucial. In this work, we present and describe a method aimed at supporting clinicians by automatically detecting and localizing B-lines in an ultrasound scan. To this end, we employ modern deep learning strategies and train a fully convolutional neural network to perform this task on B-mode images of dedicated ultrasound phantoms in-vitro, and on patients in-vivo. An accuracy, sensitivity, specificity, negative and positive predictive value equal to 0.917, 0.915, 0.918, 0.950 and 0.864 were achieved in-vitro, respectively. Using a clinical system in-vivo, these statistics were 0.892, 0.871, 0.930, 0.798 and 0.958, respectively. We moreover calculate neural attention maps that visualize which components in the image triggered the network, thereby offering simultaneous weakly-supervised localization. These promising results confirm the capability of the proposed method to identify and localize the presence of B-lines in clinical lung ultrasonography.

Predicting Structural Properties of Cortical Bone by Combining Ultrasonic Attenuation and an Artificial Neural Network (ANN): 2-D FDTD Study

The goal of this paper is to predict the micro-architectural parameters of cortical bone such as pore diameter ( ϕ ) and porosity ( v ) from ultrasound attenuation measurements using an artificial neural network (ANN). Slices from a 3-D CT scan of human femur are obtained. The micro-architectural parameters of porosity such as average pore size and porosity are calculated using image processing. When ultrasound waves propagate in porous structures, attenuation is observed due to scattering. Two-dimensional finite-difference time-domain simulations are carried out to obtain frequency dependent attenuation in those 2D structures. An artificial neural network (ANN) is then trained with the input feature vector as the frequency dependent attenuation and output as pore diameter ( ϕ ) and porosity ( v ) . The ANN is composed of one input layer, 3 hidden layers and one output layer, all of which are fully connected. 340 attenuation data sets were acquired and trained over 2000 epochs with a batch size of 32. Data was split into train, validation and test. It was observed that the ANN predicted the micro-architectural parameters of the cortical bone with high accuracies and low losses with a minimum R2 (goodness of fit) value of 0.95. ANN approaches could potentially help inform the solution of inverse-problems to retrieve bone porosity from ultrasound measurements. Ultimately, those inverse-problems could be used for the non-invasive diagnosis and monitoring of osteoporosis.

Ultrasound multiple scattering with microbubbles can differentiate between tumor and healthy tissue in vivo

Most solid tumors are characterized by highly dense, isotropic vessel networks. Characterization of such features has shown promise for early cancer diagnosis. Ultrasound diffusion has been used to characterize the micro-architecture of complex media, such as bone and the lungs. In this work, we examine a non-invasive diffusion-based ultrasound technique to assess neo-vascularization. Because the diffusion constant reflects the density of scatterers in heterogeneous media, we hypothesize that by injecting microbubbles into the vasculature, ultrasound diffusivity can reflect vascular density (VD), thus differentiating the microvascular patterns between tumors and healthy tissue. The diffusion constant and its anisotropy are shown to be significantly different between fibrosarcoma tumors (n  =  16) and control tissue (n  =  18) in a rat animal model in vivo. The diffusion constant values for control and tumor were found to be 1.38  ±  0.51 mm² µs^-1 and 0.65  ±  0.27 mm² µs^-1, respectively. These results are corroborated with VD from acoustic angiography (AA) data, confirming increased vessel density in tumors compared to controls. The diffusion constant offers a promising way to quantitatively assess vascular networks when combined with contrast agents, which may allow early tumor detection and characterization.

The role of ultrasound lung artifacts in the diagnosis of respiratory diseases

INTRODUCTION

Thoracic ultrasound is employed for the diagnosis of many thoracic diseases and is an accepted detection tool of pleural effusions, atelectasis, pneumothorax, and pneumonia. However, the use of ultrasound for the evaluation of parenchymal lung disease, when the organ is still aerated, is a relatively new application. Areas covered: The diagnosis of a normal lung and the differentiation between a normally aerated lung and a lung with interstitial pathology is based on the interpretation of ultrasound artifacts universally known as A and B-Lines. Even though the practical role of lung ultrasound artifacts is accepted by many clinicians, their physical basis and the correlations between these signs and the causal pathology is not known in depth. Expert commentary: In this review, we discuss the meaning of A- and B-Lines in the diagnostic ultrasound imaging of the lung and the acoustic properties of the pleural plane which are at the basis of their generation.

Determination of a potential quantitative measure of the state of the lung using lung ultrasound spectroscopy

B-lines are ultrasound-imaging artifacts, which correlate with several lung-pathologies. However, their understanding and characterization is still largely incomplete. To further study B-lines, lung-phantoms were developed by trapping a layer of microbubbles in tissue-mimicking gel. To simulate the alveolar size reduction typical of various pathologies, 170 and 80 µm bubbles were used for phantom-type 1 and 2, respectively. A normal alveolar diameter is approximately 280 µm. A LA332 linear-array connected to the ULA-OP platform was used for imaging. Standard ultrasound (US) imaging at 4.5 MHz was performed. Subsequently, a multi-frequency approach was used where images were sequentially generated using orthogonal sub-bands centered at different frequencies (3, 4, 5, and 6 MHz). Results show that B-lines appear predominantly with phantom-type 2. Moreover, the multi-frequency approach revealed that the B-lines originate from a specific portion of the US spectrum. These results can give rise to significant clinical applications since, if further confirmed by extensive in-vivo studies, the native frequency of B-lines could provide a quantitative-measure of the state of the lung.

Ultrasonic monitoring in the assessment of pulmonary recruitment and the best positive end-expiratory pressure

The aim of this study was to explore the clinical value of ultrasonic monitoring in the assessment of pulmonary recruitment and the best positive end-expiratory pressure (PEEP).Between January 2015 and June 2017, 40 patients with acute respiratory distress syndrome in our hospital were randomly divided into 2 groups: ultrasound group (ULS group; n = 20) and oxygenation group (OXY group; n = 20). The PEEP incremental method was used to perform recruitment maneuvers. Ultrasound scoring and the oxygenation method were used to evaluate the pulmonary recruitment endpoint. The best PEEP was chosen by ultrasound scoring and the oxygenation method after achieving the pulmonary recruitment endpoint and sustaining it for 15 minutes.The oxygenation index, PEEP, peak airway pressure (Ppeak), mean airway pressure (Pmean), and dynamic compliance (Cdyn) in the OXY group were significantly lower than those in the ULS group (P < .05) at the pulmonary recruitment endpoint; however, there was no statistical significance in the mean arterial blood pressure (MAP) or heart rate (HR) (P > .05). The best PEEPs in the OXY and ULS groups were 13.1 ± 3.1 and 15.7 ± 4.2 cmH2O, respectively, with a significant difference between the 2 groups (t = 2.227, P = .016). Compared with the basal state, the Cdyn, oxygenation index, Pmean, and Ppeak in both groups significantly increased after pulmonary recruitment (P < .05). Furthermore, the Cdyn and oxygenation index in the ULS group were significantly higher than those in the OXY group after pulmonary recruitment (P < .05). The HR in both groups significantly increased, and the MAP significantly decreased. Two hours after recruitment, the HR and MAP returned to near basal levels without a significant difference between the 2 groups (P > .05).Lung ultrasound can be used to detect the endpoint of lung recruitment and the best PEEP, with good effects on lung compliance and oxygenation improvement.