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Technegas, 99mTc-labeled aerosolized carbon nanoparticles, has been used internationally since 1986 for pulmonary ventilation imaging. Unlike traditional gases, Technegas exhibits only a gaslike behavior, allowing deep and uniform deposition in the lungs' subsegmental regions. This hydrophobic property minimizes central airway clumping, as is particularly advantageous for patients with chronic obstructive pulmonary disease. Approved by the U.S. Food and Drug Administration in September 2023, Technegas is now available in the United States for diagnosing pulmonary embolism and broader ventilation and airway evaluations. The Technegas Plus system, which produces the radioaerosol onsite by heating [99mTc]sodium pertechnetate in a carbon crucible at ultrahigh temperatures, requires a specific infrastructure, including a 220-volt power supply and an argon gas source. Its rapid administration—often requiring only 1–3 breaths—streamlines workflows while ensuring patient comfort, especially for those with respiratory limitations. Additionally, Technegas supports SPECT and SPECT/CT imaging, enabling sensitivity and specificity superior to those of traditional planar methods. Despite the global adoption of ventilation–perfusion SPECT as the standard for pulmonary embolism diagnosis, its use in the United States remains limited. Now that Technegas is available in the United States, U.S. nuclear medicine departments can transition to advanced ventilation imaging, aligning with international best practices. This paper outlines essential considerations for Technegas implementation: infrastructure requirements, staff training, protocol development, and imaging optimization, including clinical experiences and perspectives from the staff at Barnes Jewish Hospital in St. Louis, Missouri. By integrating Technegas, departments can enhance diagnostic accuracy, improve workflow efficiency, and expand clinical applications, particularly for patients with complex pulmonary conditions. [ABSTRACT FROM AUTHOR]

Objective. Lung nodule appearance may provide prognostic information, as the presence of spiculation increases the suspicion of a nodule being cancerous. Spiculations can be quantified using morphological radiomics features extracted from CT images. Radiomics features can be affected by the acquisition parameters and scanner technologies; thus, it is essential to identify imaging conditions that provide reliable measurements, particularly for emerging technologies like photon-counting CT (PCCT). This study aimed to systematically quantify the effect of imaging parameters on the radiomics measurements using a virtual imaging trial (VIT) platform, and further verify the findings with human clinical data. Approach. The VIT utilized nine virtual patients, each with three 6 mm nodules of varying spiculations. The virtual patients were run through a validated CT simulator (DukeSim) to acquire images at three dose levels (CTDIvol = 2.85, 5.69, and 11.38 mGy) with a clinical energy-integrating CT and a PCCT. The acquired projection images were reconstructed using multiple slice thicknesses, kernels, and matrix sizes. The reconstructed images were processed to extract morphological features using three segmentation methods. The features were clustered into three broad type categories. Features extracted from the acquired CT images were compared to their corresponding ground truth values, across all imaging conditions. Main results. Among all imaging conditions, slice thickness had the greatest effect on the radiomics measurements. When the thickest slices were used, the coefficient of variation increased by [1.19%–9.66%] in the energy integrating CT images, and [3.94%–24.43%] in the PCCT images. For both scanners, varying the kernel sharpness and dose affected the radiomics measurements insignificantly, while pixel size and segmentation method were observed to have stronger effects. Under varying imaging conditions, the trends and magnitude of radiomics features measurements were coherent with virtual trial results. Significance. The findings stress the importance of choosing optimal reconstruction settings for radiomics extraction to achieve precise feature quantifications. [ABSTRACT FROM AUTHOR]

BackgroundDark-field chest radiography allows the assessment of the structural integrity of the alveoli by exploiting the wave properties of x-rays.PurposeTo compare the qualitative and quantitative features of dark-field chest radiography in patients with COVID-19 pneumonia with conventional CT imaging.Materials and methodsIn this prospective study conducted from May 2020 to December 2020, patients aged at least 18 years who underwent chest CT for clinically suspected COVID-19 infection were screened for participation. Inclusion criteria were a CO-RADS score ≥4, the ability to consent to the procedure and to stand upright without help. Participants were examined with a clinical dark-field chest radiography prototype. For comparison, a healthy control cohort of 40 subjects was evaluated. Using Spearman's correlation coefficient, correlation was tested between dark-field coefficient and CT-based COVID-19 index and visual total CT score as well as between the visual total dark-field score and the visual total CT score.ResultsA total of 98 participants [mean age 58 ± 14 (standard deviation) years; 59 men] were studied. The areas of signal intensity reduction observed in dark-field images showed a strong correlation with infiltrates identified on CT scans. The dark-field coefficient had a negative correlation with both the quantitative CT-based COVID-19 index (r = −.34, p = .001) and the overall CT score used for visual grading of COVID-19 severity (r = −.44, p

This article discusses innovations in protective mechanical ventilation for the management of acute respiratory distress syndrome (ARDS). ARDS is a challenging condition to manage, and effective therapies are limited. Lung-protective mechanical ventilation plays a crucial role in improving survival rates for ARDS patients. The article explores advancements in personalized ventilation strategies, sub-phenotyping ARDS based on lung morphology, heart-lung interactions, and the use of closed-loop control systems. These innovations have the potential to optimize ventilation strategies and improve patient outcomes. [Extracted from the article]

Purpose: Hyperpolarized xenon MRI suffers from heterogeneous coil bias and magnetization decay that obscure pulmonary abnormalities. Non‐physiological signal variability can be mitigated by measuring and mapping the nominal flip angle, and by rescaling the images to correct for signal bias and decay. While flip angle maps can be calculated from sequentially acquired images, scan time and breath‐hold duration are doubled. Here, we exploit the low‐frequency oversampling of 2D‐spiral and keyhole reconstruction to measure flip angle maps from a single acquisition. Methods: Flip angle maps were calculated from two images generated from a single dataset using keyhole reconstructions and a Bloch‐equation–based model suitable for hyperpolarized substances. Artifacts resulting from acquisition and reconstruction schemes (e.g., keyhole reconstruction radius, slice‐selection profile, spiral‐ordering, and oversampling) were assessed using point‐spread functions. Simulated flip angle maps generated using keyhole reconstruction were compared against the paired‐image approach using RMS error (RMSE). Finally, feasibility was demonstrated for in vivo xenon ventilation imaging. Results: Simulations demonstrated accurate flip angle maps and B1‐inhomogeneity correction can be generated with only 1.25‐fold central‐oversampling and keyhole reconstruction radius = 5% (RMSE = 0.460°). These settings also generated accurate flip angle maps in a healthy control (RSME = 0.337°) and a person with cystic fibrosis (RMSE = 0.404°) in as little as 3.3 s. Conclusion: Regional lung ventilation images with reduced impact of B1‐inhomogeneity can be acquired rapidly by combining 2D‐spiral acquisition, Bloch‐equation–based modeling, and keyhole reconstruction. This approach will be especially useful for breath‐hold studies where short scan durations are necessary, such as dynamic imaging and applications in children or people with severely compromised respiratory function. [ABSTRACT FROM AUTHOR]

Rationale: Rates of emphysema progression vary in chronic obstructive pulmonary disease (COPD), and the relationships with vascular and airway pathophysiology remain unclear. Objectives: We sought to determine if indices of peripheral (segmental and beyond) pulmonary arterial dilation measured on computed tomography (CT) are associated with a 1-year index of emphysema (EI; percentage of voxels <−950 Hounsfield units) progression. Methods: Five hundred ninety-nine former and never-smokers (Global Initiative for Chronic Obstructive Lung Disease stages 0–3) were evaluated from the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort: rapid emphysema progressors (RPs; n = 188, 1-year ΔEI > 1%), nonprogressors (n = 301, 1-year ΔEI ± 0.5%), and never-smokers (n = 110). Segmental pulmonary arterial cross-sectional areas were standardized to associated airway luminal areas (segmental pulmonary artery–to–airway ratio [PAARseg]). Full-inspiratory CT scan–derived total (arteries and veins) pulmonary vascular volume (TPVV) was compared with small vessel volume (radius smaller than 0.75 mm). Ratios of airway to lung volume (an index of dysanapsis and COPD risk) were compared with ratios of TPVV to lung volume. Results: Compared with nonprogressors, RPs exhibited significantly larger PAARseg (0.73 ± 0.29 vs. 0.67 ± 0.23; P = 0.001), lower ratios of TPVV to lung volume (3.21 ± 0.42% vs. 3.48 ± 0.38%; P = 5.0 × 10−12), lower ratios of airway to lung volume (0.031 ± 0.003 vs. 0.034 ± 0.004; P = 6.1 × 10−13), and larger ratios of small vessel volume to TPVV (37.91 ± 4.26% vs. 35.53 ± 4.89%; P = 1.9 × 10−7). In adjusted analyses, an increment of 1 standard deviation in PAARseg was associated with a 98.4% higher rate of severe exacerbations (95% confidence interval, 29–206%; P = 0.002) and 79.3% higher odds of being in the RP group (95% confidence interval, 24–157%; P = 0.001). At 2-year follow-up, the CT-defined RP group demonstrated a significant decline in postbronchodilator percentage predicted forced expiratory volume in 1 second. Conclusions: Rapid one-year progression of emphysema was associated with indices indicative of higher peripheral pulmonary vascular resistance and a possible role played by pulmonary vascular–airway dysanapsis. [ABSTRACT FROM AUTHOR]

Copyright of Medizinische Klinik: Intensivmedizin & Notfallmedizin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: Measure the changes in relative lung water density (rLWD), lung volume, and total lung water content as a function of time after supine body positioning. Methods: An efficient ultrashort-TE pulse sequence with a yarnball k-space trajectory was used to measure water density-weighted lung images for 25 min following supine body positioning (free breathing, 74-s acquisitions, 3D images at functional residual capacity, 18 time points) in 9 healthy volunteers. Global and regional (10 chest-to-back positions) rLWD, lung volume, and total lung water volume were measured in all subjects at all time points. Volume changes were validated with a nitrogen washout study in 3 participants. Results: Global rLWD increased significantly (p=0.001) from 31.8±5.5% to 34.8±6.8%, while lung volumes decreased significantly (p<0.001) from 2390±620mL to 2130±630mL over the same 25-min interval. Total lung water volume decreased slightly from730±125mL to 706±126mL (p=0.028). There was a significant chest-to-back gradient in rLWD (20.7±4.6% to 39.9±6.1%) at all time points with absolute increases of 1.8±1.2% at the chest and 5.4±1.9% at the back. Nitrogen washout studies yielded a similar reduction in lung volume (12.5±0.9%) and time course following supine positioning. Conclusion: Lung volumes during tidal breathing decrease significantly over tens of minutes following supine body positioning, with corresponding increases in lung water density (9.2±4.4% relative increase). The total volume of lung water is slightly reduced over this interval (3.3±4.0% relative change). Evaluation of rLWD should take time after supine positioning, and more generally, all sources of lung volume changes should be taken into consideration to avoid significant bias. [ABSTRACT FROM AUTHOR]

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death. Recent studies have underlined the importance of non-contrast-enhanced chest CT scans not only for emphysema progression quantification, but for correlation with clinical outcomes as well. As about 40 percent of the 300 million CT scans per year are contrast-enhanced, no proper emphysema quantification is available in a one-stop-shop approach for patients with known or newly diagnosed COPD. Since the introduction of spectral imaging (e.g., dual-energy CT scanners), it has been possible to create virtual non-contrast-enhanced images (VNC) from contrast-enhanced images, making it theoretically possible to offer proper COPD imaging despite contrast enhancing. This study is aimed towards investigating whether these VNC images are comparable to true non-contrast-enhanced images (TNC), thereby reducing the radiation exposure of patients and usage of resources in hospitals. In total, 100 COPD patients with two scans, one with (VNC) and one without contrast media (TNC), within 8 weeks or less obtained by a spectral CT using dual-layer technology, were included in this retrospective study. TNC and VNC were compared according to their voxel-density histograms. While the comparison showed significant differences in the low attenuated volumes (LAVs) of TNC and VNC regarding the emphysema threshold of −950 Houndsfield Units (HU), the 15th and 10th percentiles of the LAVs used as a proxy for pre-emphysema were comparable. Upon further investigation, the threshold-based LAVs (−950 HU) of TNC and VNC were comparable in patients with a water equivalent diameter (DW) below 270 mm. The study concludes that VNC imaging may be a viable option for assessing emphysema progression in COPD patients, particularly those with a normal body mass index (BMI). Further, pre-emphysema was generally comparable between TNC and VNC. This approach could potentially reduce radiation exposure and hospital resources by making additional TNC scans obsolete. [ABSTRACT FROM AUTHOR]

Pandemics like COVID-19 have highlighted the potential of Photoacoustic imaging (PAI) for Brain-Computer Interface (BCI) communication and lung diagnostics. However, PAI struggles with the clear imaging of blood vessels in areas like the lungs and brain due to their cavity structures. This paper presents a simulation model to analyze the generation and propagation mechanism within phantom tissues of PAI artifacts, focusing on the evaluation of both Anisotropic diffusion filtering (ADF) and Non-local mean (NLM) filtering, which significantly reduce noise and eliminate artifacts and signify a pivotal point for selecting artifact-removal algorithms under varying conditions of light distribution. Experimental validation demonstrated the efficacy of our technique, elucidating the effect of light source uniformity on artifact-removal performance. The NLM filtering simulation and ADF experimental validation increased the peak signal-to-noise ratio by 11.33% and 18.1%, respectively. The proposed technique adds a promising dimension for BCI and is an accurate imaging solution for diagnosing lung diseases.

Abstract Background Increased pulmonary $$^{18}{}$$ 18 F-FDG metabolism in patients with idiopathic pulmonary fibrosis, and other forms of diffuse parenchymal lung disease, can predict measurements of health and lung physiology. To improve PET quantification, voxel-wise air fractions (AF) determined from CT can be used to correct for variable air content in lung PET/CT. However, resolution mismatches between PET and CT can cause artefacts in the AF-corrected image. Methods Three methodologies for determining the optimal kernel to smooth the CT are compared with noiseless simulations and non-TOF MLEM reconstructions of a patient-realistic digital phantom: (i) the point source insertion-and-subtraction method, $$h_{pts}$$ h pts ; (ii) AF-correcting with varyingly smoothed CT to achieve the lowest RMSE with respect to the ground truth (GT) AF-corrected volume of interest (VOI), $$h_{AFC}$$ h AFC ; iii) smoothing the GT image to match the reconstruction within the VOI, $$h_{PVC}$$ h PVC . The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, $$h_{PVC}$$ h PVC was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. Results The simulations demonstrated that at $$

It is evident that radiolabeled drug delivery systems hold great promise in the field of lung cancer management. The combination of therapeutic agents with radiotracers not only allows for precise localization within lung tumors but also enables real-time monitoring of drug distribution. This approach has the potential to enhance targeted therapy and improve patient outcomes. The integration of advanced imaging modalities, such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), has played a crucial role in the non-invasive tracking of radiolabeled drugs. These techniques provide valuable insights into drug pharmacokinetics, biodistribution, and tumor-targeting efficiency, offering clinicians the ability to personalize treatment regimens. The comprehensive analysis of preclinical and clinical studies presented in this review underscores the progress made in the field. The evidence suggests that radiolabeled drug delivery systems have the potential to revolutionize oncology by offering precise, targeted, and image-guided therapeutic interventions for lung cancer. This innovative approach not only enhances the effectiveness of treatment but also contributes to the development of personalized medicine strategies, tailoring interventions to the specific characteristics of each patient's cancer. The ongoing research in this area holds promise for further advancements in lung cancer management, potentially leading to improved outcomes and quality of life for patients. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) can make intelligent decisions in a manner akin to that of the human mind. AI has the potential to improve clinical workflow, diagnosis, and prognosis, especially in radiology. Acute respiratory distress syndrome (ARDS) is a very diverse illness that is characterized by interstitial opacities, mostly in the dependent areas, decreased lung aeration with alveolar collapse, and inflammatory lung edema resulting in elevated lung weight. As a result, lung imaging is a crucial tool for evaluating the mechanical and morphological traits of ARDS patients. Compared to traditional chest radiography, sensitivity and specificity of lung computed tomography (CT) and ultrasound are higher. The state of the art in the application of AI is summarized in this narrative review which focuses on CT and ultrasound techniques in patients with ARDS. A total of eighteen items were retrieved. The primary goals of using AI for lung imaging were to evaluate the risk of developing ARDS, the measurement of alveolar recruitment, potential alternative diagnoses, and outcome. While the physician must still be present to guarantee a high standard of examination, AI could help the clinical team provide the best care possible. [ABSTRACT FROM AUTHOR]

Background: Increased pulmonary 18 F-FDG metabolism in patients with idiopathic pulmonary fibrosis, and other forms of diffuse parenchymal lung disease, can predict measurements of health and lung physiology. To improve PET quantification, voxel-wise air fractions (AF) determined from CT can be used to correct for variable air content in lung PET/CT. However, resolution mismatches between PET and CT can cause artefacts in the AF-corrected image. Methods: Three methodologies for determining the optimal kernel to smooth the CT are compared with noiseless simulations and non-TOF MLEM reconstructions of a patient-realistic digital phantom: (i) the point source insertion-and-subtraction method, h pts ; (ii) AF-correcting with varyingly smoothed CT to achieve the lowest RMSE with respect to the ground truth (GT) AF-corrected volume of interest (VOI), h AFC ; iii) smoothing the GT image to match the reconstruction within the VOI, h PVC . The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, h PVC was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. Results: The simulations demonstrated that at < 200 iterations (200 i), the kernel width was dependent on iteration number and VOI position in the lung. The h pts method estimated a lower, more uniform, kernel width in all parts of the lung investigated. However, all three methods resulted in approximately equivalent AF-corrected VOI RMSEs (<10%) at ≥ 200i. The insensitivity of AF-corrected quantification to kernel width suggests that a single global kernel could be used. For all three methodologies, the computed global kernel resulted in an AF-corrected lung RMSE <10% at ≥ 200i, while larger lung RMSEs were observed for the VOI–specific kernels. The global kernel approach was then employed with the h PVC method on measured data. The optimally smoothed GT emission matched the reconstructed image well, both within the VOI and the lung background. VOI RMSE was <10%, pre-AFC, for all reconstructions investigated. Conclusions: Simulations for non-TOF PET indicated that around 200i were needed to approach image resolution stability in the lung. In addition, at this iteration number, a single global kernel, determined from several VOIs, for AFC, performed well over the whole lung. The h PVC method has the potential to be used to determine the kernel for AFC from scans of phantoms on clinical scanners. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth overview of Deep Neural Networks and their application in the segmentation and analysis of lung Magnetic Resonance Imaging (MRI) scans, specifically focusing on hyperpolarized gas MRI and the quantification of lung ventilation defects. An in-depth understanding of Deep Neural Networks is presented, laying the groundwork for the exploration of their use in hyperpolarized gas MRI and the quantification of lung ventilation defects. Five distinct studies are examined, each leveraging unique deep learning architectures and data augmentation techniques to optimize model performance. These studies encompass a range of approaches, including the use of 3D Convolutional Neural Networks, cascaded U-Net models, Generative Adversarial Networks, and nnU-net for hyperpolarized gas MRI segmentation. The findings highlight the potential of deep learning methods in the segmentation and analysis of lung MRI scans, emphasizing the need for consensus on lung ventilation segmentation methods. [ABSTRACT FROM AUTHOR]

Oxygen‐enhanced MR imaging (OE‐MRI) is a special proton imaging technique that can be performed without modifying the scanner hardware. Many fundamental studies have been conducted following the initial reporting of this technique in 1996, illustrating the high potential for its clinical application. This review aims to summarise and analyse current pulse sequences and T1 measurement methods for OE‐MRI, including fundamental theories, existing pulse sequences applied to OE‐MRI acquisition and T1 mapping. Wash‐in and wash‐out time identify lung function and are sensitive to ventilation; thus, dynamic OE‐MRI is also discussed in this review. We compare OE‐MRI with the primary competitive technique, hyperpolarised gas MRI. Finally, an overview of lower‐field applications of OE‐MRI is highlighted, as relatively recent publications demonstrated positive results. Lower‐field OE‐MRI, which is lower than 1.5 T, could be an alternative modality for detecting lung diseases. This educational review is aimed at researchers who want a quick summary of the steps needed to perform pulmonary OE‐MRI with a particular focus on sequence design, settings, and quantification methods. [ABSTRACT FROM AUTHOR]

The lung represents a unique immune environment. The primary function of the lung is to enable gas exchange by facilitating the transfer of oxygen into and carbon dioxide out of the blood. However, as a direct byproduct of this process the lung is also constantly exposed to particles, allergens, and pathogens alongside air itself. Due to this, the pulmonary immune system exists in a fine balance between quiescence and inflammation, deviations from which can lead to a failure in respiratory function. A rich history exists attempting to define the critical features of lung immunity, and most recently advances in intravital microscopy have enabled the visualization of intercellular immune dynamics in both steady-state and a variety of disease conditions. In this review, we will summarize a variety of approaches to intravital lung imaging as well as how its application has advanced our understanding of normal lung function as well as disease states such as pulmonary metastasis, asthma, and lung injury.

Purpose: To demonstrate free-breathing thoracic MRI with a minimal-TR balanced steady-state free precession (bSSFP) technique using wobbling Archimedean spiral pole (WASP) trajectories. Methods: Phantom and free-breathing in vivo chest imaging in healthy volunteers was performed at 1.5T with a half-radial, dual-echo, bSSFP sequence, termed bSTAR. For maximum sampling efficiency, a single analog-to-digital converter window along the full bipolar readout was used. To ensure a homogeneous coverage of the k-space over multiple breathing cycles, radial k-space sampling followed short-duration Archimedean spiral interleaves that were randomly titled by a small polar angle and rotated by a golden angle about the polar axis; depticting a wobbling Archimedean spiral pole (WASP) trajectory. In phantom and in vivo experiments, WASP trajectories were compared to spiral phyllotaxis sampling in terms of eddy currents and were used to generate in vivo thorax images at different respiratory phases. Results: WASP trajectories provided artifact-free bSTAR imaging in both phantom and in vivo and respiratory self-gated reconstruction was successfully performed in all subjects. The amount of the acquired data allowed the reconstruction of 10 volumes at different respiratory levels with isotropic resolution of 1.77 mm from a scan of 5.5 minutes (using a TR of 1.32ms), and one high-resolution 1.16 mm end-expiratory volume from a scan of 4.7 minutes (using a TR of 1.42ms). The very short TR of bSTAR mitigated off-resonance artifacts despite the large field-of-view. Conclusion: We have demonstrated the feasibility of high-resolution free-breathing thoracic imaging with bSTAR using the wobbling Archimedean spiral pole in healthy subjects at 1.5T.

Electrical impedance tomography (EIT) is a non-invasive technique for visualizing the internal structure of a human body. Capacitively coupled electrical impedance tomography (CCEIT) is a new contactless EIT technique that can potentially be used as a wearable device. Recent studies have shown that a machine learning-based approach is very promising for EIT image reconstruction. Most of the studies concern models containing up to 22 electrodes and focus on using different artificial neural network models, from simple shallow networks to complex convolutional networks. However, the use of convolutional networks in image reconstruction with a higher number of electrodes requires further investigation. In this work, two different architectures of artificial networks were used for CCEIT image reconstruction: a fully connected deep neural network and a conditional generative adversarial network (cGAN). The training dataset was generated by the numerical simulation of a thorax phantom with healthy and illness-affected lungs. Three kinds of illnesses, pneumothorax, pleural effusion, and hydropneumothorax, were modeled using the electrical properties of the tissues. The thorax phantom included the heart, aorta, spine, and lungs. The sensor with 32 area electrodes was used in the numerical model. The ECTsim custom-designed toolbox for Matlab was used to solve the forward problem and measurement simulation. Two artificial neural networks were trained with supervision for image reconstruction. Reconstruction quality was compared between those networks and one-step algebraic reconstruction methods such as linear back projection and pseudoinverse with Tikhonov regularization. This evaluation was based on pixel-to-pixel metrics such as root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. Additionally, the diagnostic value measured by the ROC AUC metric was used to assess the image quality. The results showed that obtaining information about regional lung function (regions affected by pneumothorax or pleural effusion) is possible using image reconstruction based on supervised learning and deep neural networks in EIT. The results obtained using cGAN are strongly better than those obtained using a fully connected network, especially in the case of noisy measurement data. However, diagnostic value estimation showed that even algebraic methods allow us to obtain satisfactory results. [ABSTRACT FROM AUTHOR]

Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Deep silicon-based photon-counting CT (Si-PCCT) is an emerging detector technology that provides improved spatial resolution by virtue of its reduced pixel sizes. This article reports the outcomes of the first simulation study evaluating the impact of this advantage over energy-integrating CT (ECT) for estimation of morphological radiomics features in lung lesions. A dynamic nutrient-access-based stochastic model was utilized to generate three distinct morphologies for lung lesions. The lesions were inserted into the lung parenchyma of an anthropomorphic phantom (XCAT - 50th percentile BMI) at 50, 70, and 90 mm from isocenter. The phantom was virtually imaged with an imaging simulator (DukeSim) modeling a Si-PCCT and a conventional ECT system using varying imaging conditions (dose, reconstruction kernel, and pixel size). The imaged lesions were segmented using a commercial segmentation tool (AutoContour, Advantage Workstation Server 3.2, GE Healthcare) followed by extraction of morphological radiomics features using an open-source radiomics package (pyradiomics). The estimation errors for both systems were computed as percent differences from corresponding feature values estimated for the ground-truth lesions. Compared to ECT, the mean estimation error was lower for Si-PCCT (independent features: 35.9% vs. 54.0%, all features: 54.5% vs. 68.1%) with statistically significant reductions in errors for 8/14 features. For both systems, the estimation accuracy was minimally affected by dose and distance from the isocenter while reconstruction kernel and pixel size were observed to have a relatively stronger effect. For all lesions and imaging conditions considered, Si-PCCT exhibited improved estimation accuracy for morphological radiomics features over a conventional ECT system, demonstrating the potential of this technology for improved quantitative imaging. [ABSTRACT FROM AUTHOR]

Purpose: To demonstrate the feasibility of using octafluorocyclobutane (OFCB, c‐C4F8) for T1 mapping of lungs in 19F MRI. Methods: The study was performed at 7 T in three healthy rats and three rats with pulmonary hypertension. To increase the sensitivity of 19F MRI, a bent‐shaped RF coil with periodic metal strips structure was used. The double flip angle method was used to calculate normalized transmitting RF field (B1n+) maps and for correcting T1 maps built with the variable flip angle (VFA) method. The ultrashort TE pulse sequence was applied for acquiring MR images throughout the study. Results: The dependencies of OFCB relaxation times on its partial pressure in mixtures with oxygen, air, helium, and argon were obtained. T1 of OFCB linearly depended on its partial pressure with the slope of about 0.35 ms/kPa in the case of free diffusion. RF field inhomogeneity leads to distortion of T1 maps built with the VFA method, and therefore to high standard deviation of T1 in these maps. To improve the accuracy of the T1 maps, the B1n+ maps were applied for VFA correction. This contributed to a 2–3‐fold decrease in the SD of T1 values in the corresponding maps compared with T1 maps calculated without the correction. Three‐dimensional T1 maps were obtained, and the mean T1 in healthy rat lungs was 35 ± 10 ms, and in rat lungs with pulmonary hypertension – 41 ± 9 ms. Conclusion: OFCB has a spin‐rotational relaxation mechanism and can be used for 19F T1 mapping of lungs. The calculated OFCB maps captured ventilation defects induced by edema. [ABSTRACT FROM AUTHOR]

To demonstrate free-breathing thoracic MRI with a minimal-TR balanced steady-state free precession (bSSFP) technique using wobbling Archimedean spiral pole (WASP) trajectories. Phantom and free-breathing in vivo chest imaging in healthy volunteers was performed at 1.5T with a half-radial, dual-echo, bSSFP sequence, termed bSTAR. For maximum sampling efficiency, a single analog-to-digital converter window along the full bipolar readout was used. To ensure a homogeneous coverage of the k-space over multiple breathing cycles, radial k-space sampling followed short-duration Archimedean spiral interleaves that were randomly titled by a small polar angle and rotated by a golden angle about the polar axis; depticting a wobbling Archimedean spiral pole (WASP) trajectory. In phantom and in vivo experiments, WASP trajectories were compared to spiral phyllotaxis sampling in terms of eddy currents and were used to generate in vivo thorax images at different respiratory phases. WASP trajectories provided artifact-free bSTAR imaging in both phantom and in vivo and respiratory self-gated reconstruction was successfully performed in all subjects. The amount of the acquired data allowed the reconstruction of 10 volumes at different respiratory levels with isotropic resolution of 1.77 mm from a scan of 5.5 minutes (using a TR of 1.32ms), and one high-resolution 1.16 mm end-expiratory volume from a scan of 4.7 minutes (using a TR of 1.42ms). The very short TR of bSTAR mitigated off-resonance artifacts despite the large field-of-view. We have demonstrated the feasibility of high-resolution free-breathing thoracic imaging with bSTAR using the wobbling Archimedean spiral pole in healthy subjects at 1.5T. [ABSTRACT FROM AUTHOR]

Purpose: To determine R2 and R2′$$ {R}_2^{\prime } $$ transverse relaxation rates in healthy lung parenchyma at 0.55 T. This is important in that it informs the design and optimization of new imaging methods for 0.55T lung MRI. Methods: Experiments were performed in 3 healthy adult volunteers on a prototype whole‐body 0.55T MRI, using a custom free‐breathing electrocardiogram‐triggered, single‐slice echo‐shifted multi‐echo spin echo (ES‐MCSE) pulse sequence with respiratory navigation. Transverse relaxation rates R2 and R2′$$ {R}_2^{\prime } $$ and off‐resonance ∆f were jointly estimated using nonlinear least‐squares estimation. These measurements were compared against R2 estimates from T2‐prepared balanced SSFP (T2‐Prep bSSFP) and R2*$$ {R}_2^{\ast } $$ estimates from multi‐echo gradient echo, which are used widely but prone to error due to different subvoxel weighting. Results: The mean R2 and R2′$$ {R}_2^{\prime } $$ values of lung parenchyma obtained from ES‐MCSE were 17.3 ± 0.7 Hz and 127.5 ± 16.4 Hz (T2 = 61.6 ± 1.7 ms; T2′$$ {\mathrm{T}}_2^{\prime } $$ = 9.5 ms ± 1.6 ms), respectively. The off‐resonance estimates ranged from −60 to 30 Hz. The R2 from T2‐Prep bSSFP was 15.7 ± 1.7 Hz (T2 = 68.6 ± 8.6 ms) and R2*$$ {R}_2^{\ast } $$ from multi‐echo gradient echo was 131.2 ± 30.4 Hz (T2*$$ {\mathrm{T}}_2^{\ast } $$ = 8.0 ± 2.5 ms). Paired t‐test indicated that there is a significant difference between the proposed and reference methods (p < 0.05). The mean R2 estimate from T2‐Prep bSSFP was slightly smaller than that from ES‐MCSE, whereas the mean R2′$$ {R}_2^{\prime } $$ and R2*$$ {R}_2^{\ast } $$ estimates from ES‐MCSE and multi‐echo gradient echo were similar to each other across all subjects. Conclusions: Joint estimation of transverse relaxation rates and off‐resonance is feasible at 0.55 T with a free‐breathing electrocardiogram‐gated and navigator‐gated ES‐MCSE sequence. At 0.55 T, the mean R2 of 17.3 Hz is similar to the reported mean R2 of 16.7 Hz at 1.5 T, but the mean R2′$$ {R}_2^{\prime } $$ of 127.5 Hz is about 5–10 times smaller than that reported at 1.5 T. [ABSTRACT FROM AUTHOR]

Abstract Background Quantitative assessment of dynamic lung water accumulation is of interest to unmask latent heart failure. We develop and validate a free-breathing 3D ultrashort echo time (UTE) sequence with automated inline image processing to image changes in lung water density (LWD) using high-performance 0.55 T cardiovascular magnetic resonance (CMR). Methods Quantitative lung water CMR was performed on 15 healthy subjects using free-breathing 3D stack-of-spirals proton density weighted UTE at 0.55 T. Inline image reconstruction and automated image processing was performed using the Gadgetron framework. A gravity-induced redistribution of LWD was provoked by sequentially acquiring images in the supine, prone, and again supine position. Quantitative validation was performed in a phantom array of vials containing mixtures of water and deuterium oxide. Results The phantom experiment validated the capability of the sequence in quantifying water density (bias ± SD 4.3 ± 4.8%, intraclass correlation coefficient, ICC = 0.97). The average global LWD was comparable between imaging positions (supine 24.7 ± 3.4%, prone 22.7 ± 3.1%, second supine 25.3 ± 3.6%), with small differences between imaging phases (first supine vs prone 2.0%, p

Mechanical forces affect the alveolar shape, depending on location and tissue composition, and vary during the respiratory cycle. This study performs alveolar morphomics in different lobes of human lungs using models generated from three-dimensional (3-D) micro-computed tomography (microCT) images. Cylindrical tissue samples (1.6 cm x 2 cm) were extracted from two nontransplantable donor lungs (one ex-smoker and one smoker, 3 samples per subject) that were air-inflated and frozen solid in liquid nitrogen vapor. Samples were scanned with microCT (11 µm/voxel). Within representative cubic regions of interest (5.5 mm edge length), alveoli were segmented to produce corresponding 3-D models from which quantitative data were obtained. The surface of segmented alveoli (n_alv_total = 23,587) was divided into individual planar surfaces (facets) and angles between facet normals were calculated. Moreover, the number of neighboring alveoli was estimated for every alveolus. In this study, we examined intraindividual differences in alveolar morphology, which were reproducible in the lungs of two subjects. The main aspects are higher mean alveolar volumes (v_alv: 6.64 x 106 and 6.63 x 106 µm³ vs. 5.78 x 106 and 6.29 x 106 µm³) and surface sizes (s_alv: 0.19 and 0.18 mm2 vs. 0.17 mm² in both lower lobes) in both upper lung lobes compared with the lower lobes. An increasing number of facets (f_alv) from top to bottom (12 and 14 in the upper lobes; 14 and 15 in the lower lobes), as well as a decreasing number of alveolar neighbors (nei_alv: 9 and 8 in the upper lobes; 8 and 7 in the lower lobes) from the upper lobes to the lower lobes were observed. We could observe an increasing ratio of alveolar entrance size to the surface size of the alveoli from top to bottom (S_ratio_alv: 0.71 and 0.64 in the upper lobes, 0.73 and 0.70 in the lower lobes). The angles between facet normals (ang_alv) were larger in the upper lobes (67.72± and 62.44±) of both lungs than in the lower lobes (66.19± and 61.30±). By using this new approach of analyzing alveolar 3-D data, which enables the estimation of facet, neighbor, and shape characteristics, we aimed to establish the baseline measures for in-depth studies of mechanical conditions and morphology. [ABSTRACT FROM AUTHOR]

Patients recovered from COVID-19 may develop long-COVID symptoms in the lung. For this patient population (post-COVID patients), they may benefit from longitudinal, radiation-free lung MRI exams for monitoring lung lesion development and progression. The purpose of this study was to investigate the performance of a spiral ultrashort echo time MRI sequence (Spiral-VIBE-UTE) in a cohort of post-COVID patients in comparison with CT and to compare image quality obtained using different spiral MRI acquisition protocols. Lung MRI was performed in 36 post-COVID patients with different acquisition protocols, including different spiral sampling reordering schemes (line in partition or partition in line) and different breath-hold positions (inspiration or expiration). Three experienced chest radiologists independently scored all the MR images for different pulmonary structures. Lung MR images from spiral acquisition protocol that received the highest image quality scores were also compared against corresponding CT images in 27 patients for evaluating diagnostic image quality and lesion identification. Spiral-VIBE-UTE MRI acquired with the line in partition reordering scheme in an inspiratory breath-holding position achieved the highest image quality scores (score range = 2.17–3.69) compared to others (score range = 1.7–3.29). Compared to corresponding chest CT images, three readers found that 81.5% (22 out of 27), 81.5% (22 out of 27) and 37% (10 out of 27) of the MR images were useful, respectively. Meanwhile, they all agreed that MRI could identify significant lesions in the lungs. The Spiral-VIBE-UTE sequence allows for fast imaging of the lung in a single breath hold. It could be a valuable tool for lung imaging without radiation and could provide great value for managing different lung diseases including assessment of post-COVID lesions. [ABSTRACT FROM AUTHOR]

Objective To increase the understanding of the clinical and imaging changes of family clustered novel coronavirus pneumonia, and to explore the targeted epidemic prevention and control strategies. Methods The clinical symptoms and imaging examination data of 27 cases in 10 groups of new coronavirus pneumonia families were retrospectively analyzed. Results Among the 27 cases, 12 males and 15 females, aged 6 months old -57 years old, 12 cases were the mild type and 15 cases were the common type. Among all cases, 5 cases were manifested by fever, 20 cases by cough and expectoration, 13 cases by nasal congestion and runny nose, 5 cases by dry throat and sore throat, 5 cases by loss of smell and taste and 2 cases by dizziness and headache. Among the 15 cases of common type, 10 cases involved both lungs; 5 cases involved unilateral lung; 8 cases involved the lower lobe, and 9 cases involved subpleural; 9 cases showed the ground-glass opacity; 7 cases showed the high density film; there were 3 cases of pleural thickening; no pleural effusion, mediastinal and axillary lymph node enlargement signs were observed. Conclusion The family clusters of novel coronavirus pneumonia have certain clinical and imaging characteristics. Attaching the importance to sentinel surveillance in fever clinics, actively controlling the source of infection, and adopting the non-drug intervention combined with intensive vaccination are the important strategies to win the battle against the epidemic. [ABSTRACT FROM AUTHOR]

Lung ultrasound is a growing modality in clinics for diagnosing and monitoring acute and chronic lung diseases due to its low cost and accessibility. Lung ultrasound works by emitting diagnostic pulses, receiving pressure waves and converting them into radio frequency (RF) data, which are then processed into B-mode images with beamformers for radiologists to interpret. However, unlike conventional ultrasound for soft tissue anatomical imaging, lung ultrasound interpretation is complicated by complex reverberations from the pleural interface caused by the inability of ultrasound to penetrate air. The indirect B-mode images make interpretation highly dependent on reader expertise, requiring years of training, which limits its widespread use despite its potential for high accuracy in skilled hands. To address these challenges and democratize ultrasound lung imaging as a reliable diagnostic tool, we propose Luna (the Lung Ultrasound Neural operator for Aeration), an AI model that directly reconstructs lung aeration maps from RF data, bypassing the need for traditional beamformers and indirect interpretation of B-mode images. Luna uses a Fourier neural operator, which processes RF data efficiently in Fourier space, enabling accurate reconstruction of lung aeration maps. From reconstructed aeration maps, we calculate lung percent aeration, a key clinical metric, offering a quantitative, reader-independent alternative to traditional semi-quantitative lung ultrasound scoring methods. The development of Luna involves synthetic and real data: We simulate synthetic data with an experimentally validated approach and scan ex vivo swine lungs as real data. Trained on abundant simulated data and fine-tuned with a small amount of real-world data, Luna achieves robust performance, demonstrated by an aeration estimation error of 9% in ex-vivo swine lung scans. We demonstrate the potential of directly reconstructing lung aeration maps from RF data, providing a foundation for improving lung ultrasound interpretability, reproducibility and diagnostic utility., Competing Interests: Competing Interests The authors declare no competing interests.

Abstract Background Prone positioning enables the redistribution of lung weight, leading to the improvement of gas exchange and respiratory mechanics. We aimed to evaluate whether the initial findings of acute respiratory distress syndrome (ARDS) on computed tomography (CT) are associated with the subsequent response to prone positioning in terms of oxygenation and 60-day mortality. Methods We retrospectively included patients who underwent prone positioning for moderate to severe ARDS from October 2014 to November 2020 at a medical center in Taiwan. A semiquantitative CT rating scale was used to quantify the extent of consolidation and ground-glass opacification (GGO) in the sternal, central and vertebral regions at three levels (apex, hilum and base) of the lungs. A prone responder was identified by a 20% increase in the ratio of arterial oxygen pressure (PaO2) to the fraction of oxygen (FiO2) or a 20 mmHg increase in PaO2. Results Ninety-six patients were included, of whom 68 (70.8%) were responders. Compared with nonresponders, responders had a significantly greater median dorsal–ventral difference in CT-consolidation scores (10 vs. 7, p = 0.046) but not in CT-GGO scores (− 1 vs. − 1, p = 0.974). Although dorsal–ventral differences in neither CT-consolidation scores nor CT-GGO scores were associated with 60-day mortality, high total CT-GGO scores (≥ 15) were an independent factor associated with 60-day mortality (odds ratio = 4.07, 95% confidence interval, 1.39–11.89, p = 0.010). Conclusions In patients with moderate to severe ARDS, a greater difference in the extent of consolidation along the dependent-independent axis on CT scan is associated with subsequent prone positioning oxygenation response, but not clinical outcome regarding survival. High total CT-GGO scores were independently associated with 60-day mortality.

Several imaging modalities have been employed to quantify lung dose and the distribution of the dose of orally inhaled aerosols in vivo. Two-dimensional (2D, or planar) imaging using gamma scintigraphy is the most widely used of these modalities. Two-dimensional gamma scintigraphy studies are accomplished using a single- or dual-headed gamma camera. The formulation to be tested is admixed with the gamma emitting radioisotope 99mtechnetium, which serves as a surrogate for the drug. This article provides details as to how 2D gamma scintigraphy images should be acquired and analyzed using recently standardized methods. Based on the new guidelines, the investigator should confirm that the drug formulation is unchanged with the addition of the radioisotope, determine the amount of radioactivity needed for inhalation to obtain appropriate radioactivity counts in the lungs, perform quality control procedures for the gamma camera, identify the lung borders of the study subject using a reference image such as an X-ray computed tomography scan, a ventilation scan, or a transmission scan, acquire a lung transmission image to correct for attenuation of radioactivity by lung tissue, instruct the subject how to inhale the radiolabel-drug mixture and record associated breathing parameters, acquire anterior and/or posterior views of the lungs and any other regions of interest (i.e., oropharynx, stomach) and assess the acquired images for total and regional dose to the lungs. Total dose should be assessed after identification of the right lung border and appropriate correction for tissue attenuation. Regional dose should be quantified as a normalized outer/inner deposition ratio (O/I) and expressed as the penetration index (PI). Mass balance should be performed as needed. By following the standardized methods, 2D gamma scintigraphy data from studies in different laboratories may be compared and combined, leading to multi-center studies and more rapid development of new medications and devices for inhaled therapies. [ABSTRACT FROM AUTHOR]

A new shape reconstruction framework that incorporates sparse Bayesian learning (SBL) and the B-spline level set (BLS) is proposed for difference electrical impedance tomography (EIT). To begin with, the conductivity distribution to be reconstructed is assumed to be piecewise constant under the BLS framework. Second, the boundaries of the inclusions are represented by the B-spline curves in a parametric and explicit way. Next, the optimal shapes are found by solving the unknown variables using the SBL method. Then, the proposed method takes full advantage of B-spline flexible representation and Bayesian learning capability, resulting in improvements in reconstruction performance, noise robustness, and computational efficiency. Finally, a series of numerical and experimental tests are performed to verify the feasibility of the proposed method. Furthermore, the robustness studies are also investigated considering the inhomogeneous background, different numbers of control points, and different initial guesses. The results show that the proposed method leads to reliable and robust shape reconstructions, as evaluated by several quantitative metrics. Specifically, the forced vital capacity (FVC) tests of ten healthy volunteers show that the mean error in the 1-s rate of the shape reconstructions is less than 5% compared with simultaneous spirometry measurements. Hence, the proposed method is promising in providing an effective assessment for lung imaging. [ABSTRACT FROM AUTHOR]

Pulmonary embolism (PE), along with the closely related condition of deep vein thrombosis, affect an estimated 600 000 patients in the US per year. Untreated, PE carries a mortality rate of 30%. Because many patients experience mild or non-specific symptoms, imaging studies are necessary for definitive diagnosis of PE. Iodinated CT pulmonary angiography is recommended for most patients, while nuclear medicine-based ventilation/perfusion (V/Q) scans are reserved for patients in whom the use of iodine is contraindicated. Magnetic particle imaging (MPI) is an emerging tracer imaging modality with high image contrast (no tissue background signal) and sensitivity to superparamagnetic iron oxide (SPIO) tracer. Importantly, unlike CT or nuclear medicine, MPI uses no ionizing radiation. Further, MPI is not derived from magnetic resonance imaging (MRI); MPI directly images SPIO tracers via their strong electronic magnetization, enabling deep imaging of anatomy including within the lungs, which is very challenging with MRI. Here, the first high-contrast in vivo MPI lung perfusion images of rats are shown using a novel lung perfusion agent, MAA-SPIOs.

ObjectiveTo explore the clinical efficacy and adverse reactions of Jiawei Maxing Shigan Tang (JMST; a modified decoction of ephedra, apricot kernel, gypsum, and licorice) combined with western medicine in the symptomatic treatment of COVID-19.MethodsIn this study, we retrospectively collected the basic data of 48 patients with COVID-19 who were discharged from our hospital between January 20 and February 28, 2020. Besides, the blood routines, biochemical indexes, nucleic acid detection results, clinical symptoms, lung imaging improvements, adverse reactions, and other clinical data of these patients before and after treatment were recorded. Finally, we drew comparisons between the outcomes and adverse reactions of patients in the combined treatment group (therapeutic regimen recommended by authoritative guidelines and supplemented by JMST) and the conventional treatment group (therapeutic regimen recommended by authoritative guidelines).ResultsThere were no significant differences in age, gender, clinical classification, and underlying medical conditions between the combined treatment group (28 cases) and the conventional treatment group (20 cases). However, the combined treatment group presented superior results to the conventional treatment group in several key areas. For instance, patients produced negative nasal/throat swab-based nucleic acid detection results in a shorter time, clinical symptoms were more effectively alleviated, and the absorption time of lung exudation was shorter (P < 0.05). Furthermore, the combined treatment group had a shorter length of stay (LOS) and faster lymphocyte recovery duration than the conventional treatment group, although the differences were not statistically significant. Moreover, there were no significant differences concerning gastrointestinal reaction, hepatic injury, renal impairment, myocardial injury, and other adverse reactions between the two groups.ConclusionThe results of this study indicate that JMST combined with the recommended therapeutic regimen enhances the recovery of COVID-19 patients without increasing the risk of adverse reactions. Therefore, this therapy promotes positive outcomes for COVID-19 patients.

• HU for PCD-CT show variations below 6 HU in the COPDGene lung phantom. • HU accuracy was independent of CTDI vol , QIR and keV parameters. • Broad range of operating conditions in which HU values have low variability. • Quantitative lung density imaging biomarkers may be robust using PCD-CT. Accurate quantification of lung density, in Hounsfield Units (HU), is of high importance to monitor progression of diseases such as emphysema using chest CT imaging. Reproducibility of HU quantification on independent photon counting detector CT (PCD-CT) systems with a focus on lung imaging have not yet been evaluated. We thus aimed to evaluate HU reproducibility on 2 independent PCD-CT systems using a repeatable phantom setup with identical acquisition and image reconstruction settings. A COPDGene phantom comprising densities of air, water and lung was scanned on 2 independent PCCT systems using 3 different radiation exposures, 2 medium-sharpness reconstruction kernels (Br40 and Qr36), with and without iterative reconstruction (levels 0 vs 3). Our results demonstrate that acquisitions performed with full dose (3.2 mGy), half dose (1.6 mGy), and one-eighth dose (0.4 mGy) had minimal influence on HU accuracy (<6 HU) when using Br40 and Qr36 kernels. The level of iterative reconstruction also has a minimal impact (<6 HU) with the same kernels. Between the 2 PCD-CT systems evaluated, reproducible HU quantification was achieved for changes to CTDI vol , iterative reconstruction level and reconstruction kernel. [ABSTRACT FROM AUTHOR]

In this work, a feasibility study for lung lesion detection through microwave imaging based on Huygens' principle (HP) has been performed using multilayer oval shaped phantoms mimicking human torso having a cylindrically shaped inclusion simulating lung lesion. First, validation of the proposed imaging method has been performed through phantom experiments using a dedicated realistic human torso model inside an anechoic chamber, employing a frequency range of 1–5 GHz. Subsequently, the miniaturized torso phantom validation (using both single and double inclusion scenarios) has been accomplished using a microwave imaging (MWI) device, which operates in free space using two antennas in multi-bistatic configuration. The identification of the target's presence in the lung layer has been achieved on the obtained images after applying both of the following artifact removal procedures: (i) the "rotation subtraction" method using two adjacent transmitting antenna positions, and (ii) the "ideal" artifact removal procedure utilizing the difference between received signals from unhealthy and healthy scenarios. In addition, a quantitative analysis of the obtained images was executed based on the definition of signal to clutter ratio (SCR). The obtained results verify that HP can be utilized successfully to discover the presence and location of the inclusion in the lung-mimicking phantom, achieving an SCR of 9.88 dB. [ABSTRACT FROM AUTHOR]

Abstract Background Critically ill COVID-19 patients have pathophysiological lung features characterized by perfusion abnormalities. However, to date no study has evaluated whether the changes in the distribution of pulmonary gas and blood volume are associated with the severity of gas-exchange impairment and the type of respiratory support (non-invasive versus invasive) in patients with severe COVID-19 pneumonia. Methods This was a single-center, retrospective cohort study conducted in a tertiary care hospital in Northern Italy during the first pandemic wave. Pulmonary gas and blood distribution was assessed using a technique for quantitative analysis of dual-energy computed tomography. Lung aeration loss (reflected by percentage of normally aerated lung tissue) and the extent of gas:blood volume mismatch (percentage of non-aerated, perfused lung tissue—shunt; aerated, non-perfused dead space; and non-aerated/non-perfused regions) were evaluated in critically ill COVID-19 patients with different clinical severity as reflected by the need for non-invasive or invasive respiratory support. Results Thirty-five patients admitted to the intensive care unit between February 29th and May 30th, 2020 were included. Patients requiring invasive versus non-invasive mechanical ventilation had both a lower percentage of normally aerated lung tissue (median [interquartile range] 33% [24–49%] vs. 63% [44–68%], p

BackgroundMechanical ventilation is a common therapy in operating rooms and intensive care units. When ill-adapted, it can lead to ventilator-induced lung injury (VILI), which is associated with poor outcomes. Excessive regional pulmonary strain is thought to be a major mechanism responsible for VILI. Scarce bedside methods exist to measure regional pulmonary strain. We propose a novel way to measure regional pleural strain using ultrasound elastography. The objective of this study was to assess the feasibility and reliability of pleural strain measurement by ultrasound elastography and to determine if elastography parameters would correlate with varying tidal volumes.MethodsA single-blind randomized crossover proof of concept study was conducted July to October 2017 at a tertiary care referral center. Ten patients requiring general anesthesia for elective surgery were recruited. After induction, patients received tidal volumes of 6, 8, 10, and 12 mL.kg–1 in random order, while pleural ultrasound cineloops were acquired at 4 standardized locations. Ultrasound radiofrequency speckle tracking allowed computing various pleural translation, strain and shear components. We screened 6 elastography parameters (lateral translation, lateral absolute translation, lateral strain, lateral absolute strain, lateral absolute shear and Von Mises Strain) to identify those with the best dose-response with tidal volumes using linear mixed effect models. Goodness-of-fit was assessed by the coefficient of determination. Intraobserver, interobserver and test-retest reliability were calculated using intraclass correlation coefficients.ResultsAnalysis was possible in 90.7% of ultrasound cineloops. Lateral absolute shear, lateral absolute strain and Von Mises strain varied significantly with tidal volume and offered the best dose-responses and data modeling fits. Point estimates for intraobserver reliability measures were excellent for all 3 parameters (0.94, 0.94, and 0.93, respectively). Point estimates for interobserver (0.84, 0.83, and 0.77, respectively) and test-retest (0.85, 0.82, and 0.76, respectively) reliability measures were good.ConclusionStrain imaging is feasible and reproducible. Future studies will have to investigate the clinical relevance of this novel imaging modality.Clinical trial registrationwww.Clinicaltrials.gov, identifier NCT03092557.

Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

Background: Quantitative assessment of dynamic lung water accumulation is of interest to unmask latent heart failure. We develop and validate a free-breathing 3D ultrashort echo time (UTE) sequence with automated inline image processing to image changes in lung water density (LWD) using high-performance 0.55 T cardiovascular magnetic resonance (CMR). Methods: Quantitative lung water CMR was performed on 15 healthy subjects using free-breathing 3D stack-of-spirals proton density weighted UTE at 0.55 T. Inline image reconstruction and automated image processing was performed using the Gadgetron framework. A gravity-induced redistribution of LWD was provoked by sequentially acquiring images in the supine, prone, and again supine position. Quantitative validation was performed in a phantom array of vials containing mixtures of water and deuterium oxide. Results: The phantom experiment validated the capability of the sequence in quantifying water density (bias ± SD 4.3 ± 4.8%, intraclass correlation coefficient, ICC = 0.97). The average global LWD was comparable between imaging positions (supine 24.7 ± 3.4%, prone 22.7 ± 3.1%, second supine 25.3 ± 3.6%), with small differences between imaging phases (first supine vs prone 2.0%, p < 0.001; first supine vs second supine − 0.6%, p = 0.001; prone vs second supine − 2.7%, p < 0.001). In vivo test–retest repeatability in LWD was excellent (− 0.17 ± 0.91%, ICC = 0.97). A regional LWD redistribution was observed in all subjects when repositioning, with a predominant posterior LWD accumulation when supine, and anterior accumulation when prone (difference in anterior–posterior LWD: supine − 11.6 ± 2.7%, prone 5.5 ± 2.7%, second supine − 11.4 ± 2.9%). Global LWD maps were calculated inline within 23.2 ± 0.3 s following the image reconstruction using the automated pipeline. Conclusions: Redistribution of LWD due to gravitational forces can be depicted and quantified using a validated free-breathing 3D proton density weighted UTE sequence and inline automated image processing pipeline on a high-performance 0.55 T CMR system. [ABSTRACT FROM AUTHOR]

This paper provides a review of a selection of papers published in the Journal of Clinical Monitoring and Computing in 2020 and 2021 highlighting what is new within the field of respiratory monitoring. Selected papers cover work in pulse oximetry monitoring, acoustic monitoring, respiratory system mechanics, monitoring during surgery, electrical impedance tomography, respiratory rate monitoring, lung ultrasound and detection of patient-ventilator asynchrony. [ABSTRACT FROM AUTHOR]