Advanced lung imaging with photon-counting detectors: Insights from thermoluminescence dosimetry.

RATIONALE AND OBJECTIVES: This study investigates the dose burden of photon-counting detector (PCD) lung CT with ultra-high-resolution (UHR) and standard mode using organ-based tube current modulation (OBTCM). MATERIALS AND METHODS: An anthropomorphic Alderson-Rando phantom was scanned in UHR and standard mode with and without OBTCM on three dose levels (IQ 5, 20, 50). Effective radiation dose was determined by thermoluminescent dosimetry in 13 measurement sites and compared with the calculated effective dose derived from the dose-length product. Image quality was evaluated subjectively by six radiologists using an equidistant 7-point scale and objectively by means of modulation transfer function analysis. RESULTS: Measured effective radiation exposure was lower in UHR and OBTCM studies than in standard mode (IQ 5: 0.34-0.36, IQ 20: 1.57-1.70, IQ 50: 3.76-3.99 mSv). Compared with the calculated effective dose, the radiation exposure measured with thermoluminescence dosimetry was 131-170% higher. Noise in UHR mode was rated lower than in standard (all p ≤ 0.042) and OBTCM images (all p ≤ 0.028) for all dose levels, while image sharpness was deemed highest for UHR protocols (all p ≤ 0.042). The use of OBTCM had no significant effect on either dimension of subjective image quality (all p ≥ 0.999). Modulation transfer function analysis confirmed the highest spatial frequency in UHR datasets (all p ≤ 0.016). CONCLUSION: In PCD-CT of the lung, full field-of-view UHR imaging entails no dose disadvantage over standard mode despite superior image quality. OBTCM possesses moderate dose saving potential. Thermoluminescence dosimetry yielded considerably higher effective doses than those calculated from dose-length products.

Diagnostic Potentials of Lung Ultrasound In Neonatal Care: An Updated Overview.

Recent technological strides, including high-frequency probes and lung ultrasound, have become a crucial non-invasive diagnostic tool in neonatal care, revolutionizing how respiratory conditions are assessed in the neonatal intensive care unit (NICU). High-frequency probes and portable devices significantly enhance the effectiveness of lung ultrasound in identifying respiratory distress syndrome (RDS), pneumonia, and pneumothorax, and underscore its growing significance. This comprehensive review explores the historical journey of lung ultrasonography, technological advancements, contemporary applications in neonatal care, emerging trends, and collaborative initiatives, and foresees a future where personalized healthcare optimizes outcomes for neonates.

Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.

Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in pulmonary diagnostics. This comprehensive review explores the impact of AI on revolutionizing lung imaging, focusing on its applications in detecting abnormalities, diagnosing pulmonary conditions, and predicting disease prognosis. We provide an overview of traditional pulmonary diagnostic methods and highlight the importance of accurate and efficient lung imaging for early intervention and improved patient outcomes. Through the lens of AI, we examine machine learning algorithms, deep learning techniques, and natural language processing for analyzing radiology reports. Case studies and examples showcase the successful implementation of AI in pulmonary diagnostics, alongside challenges faced and lessons learned. Finally, we discuss future directions, including integrating AI into clinical workflows, ethical considerations, and the need for further research and collaboration in this rapidly evolving field. This review underscores the transformative potential of AI in enhancing the accuracy, efficiency, and accessibility of pulmonary healthcare.

The Comparison of Ultrasound and Tomographic Images of Lung Involvement in Critically Ill Patients With COVID-19 Pneumonia: A Prospective Observational Study.

Introduction Computed tomography (CT) has a high sensitivity for diagnosing COVID-19 pneumonia in critically ill patients, but it has significant limitations. Lung ultrasonography (LUS) is an imaging method increasingly used in intensive care units. Our primary aim is to evaluate the relationship between LUS and CT images by scoring a critically ill patient who was previously diagnosed with COVID-19 pneumonia and underwent CT, as well as to determine their relationship with the patient's oxygenation. Methods This was a single-center, prospective observational study. The study included COVID-19 patients (positive reverse transcription polymerase chain reaction, RT-PCR) who were admitted to the intensive care unit between June 2020 and December 2020, whose oxygen saturation (SpO2) was below 92%, and who underwent a chest tomography scan within the last 12 hours. CT findings were scored by the radiologist using the COVID-19 Reporting and Data System (CO-RADS). The intensivist evaluated 12 regions to determine the LUS score. The ratio of the partial pressure of oxygen in the arterial blood to the inspiratory oxygen concentration (PaO2/FiO2) was used to assess the patient's oxygenation. Results The study included 30 patients and found a weak correlation (ICC = 0.45, 95% CI = 0.25-0.65, p < 0.05) between total scores obtained from LUS and CT scans. The correlation between the total LUS score and oxygenation (r = -0.514, p = 0.004) was stronger than that between the CT score and oxygenation (r = -0.400, p = 0.028). The most common sonographic findings were abnormalities in the pleural line, white lung, and subpleural consolidation. On the other hand, the CT images revealed dense ground-glass opacities and consolidation patterns classified as CO-RADS 5. Conclusion A weak correlation was found between LUS and CT scores in critically ill COVID-19 pneumonia patients. Also, as both scores increased, oxygenation was detected to be impaired, and such a correlation is more evident with the LUS score.

Comparison of Virtual Non-Contrast and True Non-Contrast CT Images Obtained by Dual-Layer Spectral CT in COPD Patients.

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.

Functional gas exchange measures on 129Xe MRI and spectroscopy are associated with age, sex, and BMI in healthy subjects.

Introduction: Hyperpolarized 129Xe MRI and spectroscopy is a rapidly growing technique for assessing lung function, with applications in a wide range of obstructive, restrictive, and pulmonary vascular disease. However, normal variations in 129Xe measures of gas exchange across healthy subjects are not well characterized, presenting an obstacle to differentiating disease processes from the consequences of expected physiological heterogeneity. Here, we use multivariate models to evaluate the role of age, sex, and BMI in a range of commonly used 129Xe measures of gas exchange. Materials and methods: Healthy subjects (N = 40, 16F, age 44.3 ± 17.8 yrs., min-max 22-87 years) with no history of cardiopulmonary disease underwent 129Xe gas exchange MRI and spectroscopy. We used multivariate linear models to assess the associations of age, sex, and body mass index (BMI) with the RBC:Membrane (RBC:M), membrane to gas (Mem:Gas), and red blood cell to gas (RBC:Gas) ratios, as well as measurements of RBC oscillation amplitude and RBC chemical shift. Results: Age, sex, and BMI were all significant covariates in the RBC:M model. Each additional 10 years of age was associated with a 0.05 decrease in RBC:M (p < 0.001), each additional 10 points of BMI was associated with a decrease of 0.07 (p = 0.02), and males were associated with a 0.17 higher RBC:M than females (p < 0.001). For Mem:Gas, male sex was associated with a decrease and BMI was associated with an increase. For RBC:Gas, age was associated with a decrease and male sex was associated with an increase. RBC oscillation amplitude increased with age and RBC chemical shift was not associated with any of the three covariates. Discussion: 129Xe MRI and spectroscopy measurements in healthy subjects, particularly the widely used RBC:M measurement, exhibit heterogeneity associated in part with variations in subject age, sex, and BMI. Elucidating the contributions of these and other factors to 129Xe gas exchange measurements is a critical component for differentiating disease processes from expected variation in healthy subjects. Notably, the Mem:Gas and RBC chemical shift appear to be stable with aging, suggesting that unexplained deviations in these metrics may be signs of underlying abnormalities.

Vessel and Airway Characteristics in One-Year Computed Tomography-defined Rapid Emphysema Progression: SPIROMICS.

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.

Molecular Imaging for Lung Cancer: Exploring Small Molecules, Peptides, and Beyond in Radiolabeled Diagnostics.

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.

Analytical corrections for B1-inhomogeneity and signal decay in multi-slice 2D spiral hyperpolarized 129Xe MRI using keyhole reconstruction.

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.

Dynamic changes in lung water density and volume following supine body positioning.

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 ± 620 mL to 2130 ± 630 mL over the same 25-min interval. Total lung water volume decreased slightly from 730 ± 125 mL to 706 ± 126 mL (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.

Lung Imaging and Artificial Intelligence in ARDS.

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.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs.

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.

Automated MRI Lung Segmentation and 3D Morphologic Features for Quantification of Neonatal Lung Disease.

Purpose: To analyze the performance of deep learning (DL) models for segmentation of the neonatal lung in MRI and investigate the use of automated MRI-based features for assessment of neonatal lung disease. Materials and Methods: Quiet-breathing MRI was prospectively performed in two independent cohorts of preterm infants (median gestational age, 26.57 weeks; IQR, 25.3-28.6 weeks; 55 female and 48 male infants) with (n = 86) and without (n = 21) chronic lung disease (bronchopulmonary dysplasia [BPD]). Convolutional neural networks were developed for lung segmentation, and a three-dimensional reconstruction was used to calculate MRI features for lung volume, shape, pixel intensity, and surface. These features were explored as indicators of BPD and disease-associated lung structural remodeling through correlation with lung injury scores and multinomial models for BPD severity stratification. Results: The lung segmentation model reached a volumetric Dice coefficient of 0.908 in cross-validation and 0.880 on the independent test dataset, matching expert-level performance across disease grades. MRI lung features demonstrated significant correlations with lung injury scores and added structural information for the separation of neonates with BPD (BPD vs no BPD: average area under the receiver operating characteristic curve [AUC], 0.92 ± 0.02 [SD]; no or mild BPD vs moderate or severe BPD: average AUC, 0.84 ± 0.03). Conclusion: This study demonstrated high performance of DL models for MRI neonatal lung segmentation and showed the potential of automated MRI features for diagnostic assessment of neonatal lung disease while avoiding radiation exposure.Keywords: Bronchopulmonary Dysplasia, Chronic Lung Disease, Preterm Infant, Lung Segmentation, Lung MRI, BPD Severity Assessment, Deep Learning, Lung Imaging Biomarkers, Lung Topology Supplemental material is available for this article. Published under a CC BY 4.0 license.See also the commentary by Parraga and Sharma in this issue.

Optimisation of the air fraction correction for lung PET/CT: addressing resolution mismatch.

BACKGROUND: Increased pulmonary [Formula: see text]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, [Formula: see text]; (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), [Formula: see text]; iii) smoothing the GT image to match the reconstruction within the VOI, [Formula: see text]. The methods were evaluated both using VOI-specific kernels, and a single global kernel optimised for the six VOIs combined. Furthermore, [Formula: see text] was implemented on thorax phantom data measured on two clinical PET/CT scanners with various reconstruction protocols. RESULTS: The simulations demonstrated that at [Formula: see text] iterations (200 i), the kernel width was dependent on iteration number and VOI position in the lung. The [Formula: see text] 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 [Formula: see text]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 [Formula: see text]200i, while larger lung RMSEs were observed for the VOI-specific kernels. The global kernel approach was then employed with the [Formula: see text] 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 [Formula: see text] method has the potential to be used to determine the kernel for AFC from scans of phantoms on clinical scanners.

Deep Learning Approaches for Quantifying Ventilation Defects in Hyperpolarized Gas Magnetic Resonance Imaging of the Lung: A Review.

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.

[Is JAK inhibition an option in the treatment of interstitial lung disease in rheumatoid arthritis?] / Ist JAK-Hemmung eine Option in der Behandlung der interstitiellen Lungenerkrankung bei einer rheumatoiden Arthritis?

A 69-year-old male patient with seropositive erosive rheumatoid arthritis (RA) presented to our clinic due to progressive dyspnea. High-resolution computed tomography (HRCT) and immunological bronchioalveolar lavage revealed ground-glass opacities and a lymphocytic alveolitis caused by interstitial lung disease (ILD) in RA. Considering previous forms of treatment, disease-modifying antirheumatic drug (DMARD) treatment was switched to tofacitinib. Tofacitinib treatment demonstrated a 33% reduction in ground-glass opacities by artificial intelligence-based quantification of pulmonary HRCT over the course of 6 months, which was associated with an improvement in dyspnea symptoms. In conclusion, tofacitinib represents an effective anti-inflammatory therapeutic option in the treatment of RA-ILD.

Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax.

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.

[Correlation between comorbidities and thoracic CT manifestations of COVID-19 pneumonia]. / Korrelation zwischen Komorbiditäten und Veränderungen des Lungenparenchyms im CT des Thorax bei Patienten mit COVID-19-Pneumonie.

BACKGROUND AND OBJECTIVES: Pulmonary manifestation of coronavirus disease 2019 (COVID-19) is described using standardized computed tomography (CT) morphologic criteria. In this study, we investigated possible associations between thoracic CT manifestations in COVID-19 pneumonia and typical comorbidities, as well as clinical course. METHODS: We analyzed clinical data and pulmonary imaging of 61 patients with positive PCR test. Pulmonary changes were categorized and reviewed for associations with pre-existing comorbidities and clinical course. RESULTS: Compared to patients with atypical infiltrate patterns (2/19, 10.5%), 25 patients with typical infiltrate patterns (25/42, 59.5%) were significantly more likely to receive intensive care (p<0.001). In addition, patients with typical infiltrate patterns were more likely to receive non-invasive ventilation (12/42, 28.6%, p=0.040) and high-flow therapy (8/42, 19%, p=0.041) compared to patients with atypical infiltrate patterns. Mortality was also higher in patients with typical infiltrate patterns, with 15 patients (15/42, 35.7%) dying during follow-up compared to only 1 patient with atypical infiltrate pattern (1/19, 10.5%, p=0.012). No significant association between specific comorbidities and the resulting infiltrate pattern could be demonstrated. CONCLUSIONS: Patients with a typical COVID-19 infiltrate pattern are more likely to receive intensive care and show higher mortality rates. Further analysis with larger patient collectives is needed to identify specific risk factors for typical COVID-19 pneumonia.