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Objective: To determine reference values for diagnosing sarcopenia through chest CT scans and evaluate their suitability for use among the Chinese elderly population., Methods: Chest CT scans were obtained from 500 healthy individuals aged 19-39. Skeletal muscle mass was assessed on chest CT at the level of T4 by the skeletal muscle area (T4SMA), skeletal muscle index (T4SMI), T12 erector spinae muscle area (T12ESMA), and T12 skeletal muscle index (T12SMI), as well as skeletal muscle density (SMD) at T4 and T12 levels. The diagnostic threshold for sarcopenia was defined as a gender-specific value below 2 SD of the mean value in the young group. These cutoff values were then applied to a group of older adults aged 65 and over., Results: Diagnostic thresholds for low skeletal muscle in men were 110.05 cm², 36.01 cm²/m², 29.56 cm², and 9.65 cm²/m² for T4SMA, T4SMI, T12ESMA, and T12SMI, respectively. For women, the thresholds were: 69.93 cm², 26.51 cm²/m², 17.84 cm²/m², and 6.87 cm²/m², respectively. Diagnostic thresholds for low SMD were 38.63HU in men, 34.74 HU for women at T4 level. At T12 level, the cutoff values were 40.94 HU for men and 36.63 HU for women. Sarcopenia prevalence in men, defined by T4SMA, T4SMI, T12ESMA, and T12SMI cutoffs, was 35.6%, 18.9%, 36.7%, and 23.7%, respectively. In women, sarcopenia prevalence was 5.1%, 3.2%, 3.2%, and 1.9%, respectively., Conclusion: This study established reference values for sarcopenia diagnosis through chest CT scans among the Chinese population, highlighting the importance of utilizing chest CT scans for sarcopenia detection and muscle health monitoring in older adults., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Purpose: We evaluated the measurement accuracy and time efficiency of the tumor respiratory motion evaluation methods using a dynamic thorax motion phantom., Methods: A total of 12 patterns of 4DCT images with different tumor displacements and artifacts were used for the measurement. Three methods were employed to measure tumor motion. The first method was the manual delineation of the tumor on each phase CT image with a treatment planning system (RTPS [Manual]). The second method was the automatic delineation of the tumor structure by deformation and copying (RTPS [Auto]). The third method was tumor motion analysis software (Simple 4D Analysis Ver.1.3.1 [Simple 4D]; Triangle Products, Chiba, Japan). For each method, the difference between the phantom motion and the measured value was determined., Results: The differences (mean±standard deviation: SD) in the superior-inferior direction for RTPS (Manual), RTPS (Auto), and Simple 4D in the without-artifact images were -0.6 mm±0.6 mm, -5.0 mm±2.2 mm, and -1.0 mm±0.0 mm, respectively. The difference in the left-right and anterior-posterior directions was within 1 mm for all methods. Furthermore, the time required for Simple 4D was shorter than for the other methods., Conclusion: Simple 4D showed the comparable measurement accuracy and improvement time efficiency to RTPS (Manual) and RTPS (Auto), and was useful for tumor respiratory motion analysis.

Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation of R squared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities., (Creative Commons Attribution license.)

Background: Recent advancements in anomaly detection have paved the way for novel radiological reading assistance tools that support the identification of findings, aimed at saving time. The clinical adoption of such applications requires a low rate of false positives while maintaining high sensitivity., Purpose: In light of recent interest and development in multi pathology identification, we present a novel method, based on a recent contrastive self-supervised approach, for multiple chest-related abnormality identification including low lung density area ("LLDA"), consolidation ("CONS"), nodules ("NOD") and interstitial pattern ("IP"). Our approach alerts radiologists about abnormal regions within a computed tomography (CT) scan by providing 3D localization., Methods: We introduce a new method for the classification and localization of multiple chest pathologies in 3D Chest CT scans. Our goal is to distinguish four common chest-related abnormalities: "LLDA", "CONS", "NOD", "IP" and "NORMAL". This method is based on a 3D patch-based classifier with a Resnet backbone encoder pretrained leveraging recent contrastive self supervised approach and a fine-tuned classification head. We leverage the SimCLR contrastive framework for pretraining on an unannotated dataset of randomly selected patches and we then fine-tune it on a labeled dataset. During inference, this classifier generates probability maps for each abnormality across the CT volume, which are aggregated to produce a multi-label patient-level prediction. We compare different training strategies, including random initialization, ImageNet weight initialization, frozen SimCLR pretrained weights and fine-tuned SimCLR pretrained weights. Each training strategy is evaluated on a validation set for hyperparameter selection and tested on a test set. Additionally, we explore the fine-tuned SimCLR pretrained classifier for 3D pathology localization and conduct qualitative evaluation., Results: Validated on 111 chest scans for hyperparameter selection and subsequently tested on 251 chest scans with multi-abnormalities, our method achieves an AUROC of 0.931 (95% confidence interval [CI]: [0.9034, 0.9557], p $ p$ -value < 0.001) and 0.963 (95% CI: [0.952, 0.976], p $ p$ -value < 0.001) in the multi-label and binary (i.e., normal versus abnormal) settings, respectively. Notably, our method surpasses the area under the receiver operating characteristic (AUROC) threshold of 0.9 for two abnormalities: IP (0.974) and LLDA (0.952), while achieving values of 0.853 and 0.791 for NOD and CONS, respectively. Furthermore, our results highlight the superiority of incorporating contrastive pretraining within the patch classifier, outperforming Imagenet pretraining weights and non-pretrained counterparts with uninitialized weights (F1 score = 0.943, 0.792, and 0.677 respectively). Qualitatively, the method achieved a satisfactory 88.8% completeness rate in localization and maintained an 88.3% accuracy rate against false positives., Conclusions: The proposed method integrates self-supervised learning algorithms for pretraining, utilizes a patch-based approach for 3D pathology localization and develops an aggregation method for multi-label prediction at patient-level. It shows promise in efficiently detecting and localizing multiple anomalies within a single scan., (© 2024 American Association of Physicists in Medicine.)

Objectives: This study aims to develop an automated approach for estimating the vertical rotation of the thorax, which can be used to assess the technical adequacy of chest X-ray radiographs (CXRs)., Methods: Total 800 chest radiographs were used to train and establish segmentation networks for outlining the lungs and spine regions in chest X-ray images. By measuring the widths of the left and right lungs between the central line of segmented spine and the lateral sides of the segmented lungs, the quantification of thoracic vertical rotation was achieved. Additionally, a life-size, full body anthropomorphic phantom was employed to collect chest radiographic images under various specified rotation angles for assessing the accuracy of the proposed approach., Results: The deep learning networks effectively segmented the anatomical structures of the lungs and spine. The proposed approach demonstrated a mean estimation error of less than 2° for thoracic rotation, surpassing existing techniques and indicating its superiority., Conclusions: The proposed approach offers a robust assessment of thoracic rotation and presents new possibilities for automated image quality control in chest X-ray examinations., Advances in Knowledge: This study presents a novel deep-learning-based approach for the automated estimation of vertical thoracic rotation in chest X-ray radiographs. The proposed method enables a quantitative assessment of the technical adequacy of CXR examinations and opens up new possibilities for automated screening and quality control of radiographs., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

To optimise soldier protection within body armour systems, knowledge of the boundaries of essential thoraco-abdominal organs is necessary to inform coverage requirements. However, existing methods of organ boundary identification are costly and time consuming, limiting widespread adoption for use on soldier populations. The aim of this study was to evaluate a novel method of using 3D organ models to identify essential organ boundaries from low dose planar X-rays and 3D external surface scans of the human torso. The results revealed that, while possible to reconstruct 3D organs using template 3D organ models placed over X-ray images, the boundary data (relating to the size and position of each organ) obtained from the reconstructed organs differed significantly from MRI organ data. The magnitude of difference varied between organs. The most accurate anatomical boundaries were the left, right, and inferior boundaries of the heart, and lateral boundaries for the liver and spleen. Visual inspection of the data demonstrated that 11 of 18 organ models were successfully integrated within the 3D space of the participant's surface scan. These results suggest that, if this method is further refined and evaluated, it has potential to be used as a tool for estimating body armour coverage requirements., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: This study was funded by the Defence Science and Technology Group, Department of Defence, Australia under a Defence Science Partnering Deed (reference number 10829). Sheridan Laing and Mark Jaffrey were employees of Defence Science and Technology Group, Department of Defence, Australia at the time the project was undertaken., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective . This study aims to design and fabricate a 3D printed heterogeneous paediatric head phantom and to customize a thorax phantom for radiotherapy dosimetry. Approach . This study designed, fabricated, and tested 3D printed radiotherapy phantoms that can simulate soft tissue, lung, brain, and bone. Various polymers were considered in designing the phantoms. Polylactic acid+, nylon, and plaster were used in simulating different tissue equivalence. Dimensional accuracy, and CT number were investigated. The phantoms were subjected to a complete radiotherapy clinical workflow. Several treatment plans were delivered in both the head and the thorax phantom from a simple single 6 MV beam, parallel opposed beams, and five-field intensity modulated radiotherapy (IMRT) beams. Dose measurements using an ionization chamber and radiochromic films were compared with the calculated doses of the Varian Eclipse treatment planning system (TPS). Main results . The fabricated heterogeneous phantoms represent paediatric human head and adult thorax based on its radiation attenuation and anatomy. The measured CT number ranges are within -786.23 ± 10.55, 0.98 ± 3.86, 129.51 ± 12.83, and 651.14 ± 47.76 HU for lung, water/brain, soft tissue, and bone, respectively. It has a good radiological imaging visual similarity relative to a real human head and thorax depicting soft tissue, lung, bone, and brain. The accumulated dose readings for both conformal radiotherapy and IMRT match with the TPS calculated dose within ±2% and ±4% for head and thorax phantom, respectively. The mean pass rate for all the plans delivered are above 90% for gamma analysis criterion of 3%/3 mm. Significance and conclusion . The fabricated heterogeneous paediatric head and thorax phantoms are useful in Linac end-to-end radiotherapy quality assurance based on its CT image and measured radiation dose. The manufacturing and dosimetry workflow of this study can be utilized by other institutions for dosimetry and trainings., (Creative Commons Attribution license.)

This study introduced a depth-sensing-based approach with robust algorithms for tracking relative morphological changes in the chests of patients undergoing physical therapy. The problem that was addressed was the periodic change in morphological parameters induced by breathing, and since the recording was continuous, the parameters were extracted for the moments of maximum and minimum volumes of the chest (inspiration and expiration moments), and analyzed. The parameters were derived from morphological transverse cross-sections (CSs), which were extracted for the moments of maximal and minimal depth variations, and the reliability of the results was expressed through the coefficient of variation (CV) of the resulting curves. Across all subjects and levels of observed anatomy, the mean CV for CS depth values was smaller than 2%, and the mean CV of the CS area was smaller than 1%. To prove the reproducibility of measurements (extraction of morphological parameters), 10 subjects were recorded in two consecutive sessions with a short interval (2 weeks) where no changes in the monitored parameters were expected and statistical methods show that there was no statistically significant difference between the sessions, which confirms the reproducibility hypothesis. Additionally, based on the representative CSs for inspiration and expirations moments, chest mobility in quiet breathing was examined, and the statistical test showed no difference between the two sessions. The findings justify the proposed algorithm as a valuable tool for evaluating the impact of rehabilitation exercises on chest morphology.

Introduction: The study aims to compare the accuracy and safety of robotic-assisted navigation puncture to freehand puncture during computed tomography (CT)-guided percutaneous needle insertion in the chest and abdomen., Methods: A total of 60 patients required percutaneous puncture procedures, with 40 involving the chest and 20 involving the abdomen. Eligible patients were randomly assigned to two groups. The test group punctured using a robotic-assisted navigation system, whereas the control group punctured manually. The primary outcome assessment standards are single puncture success rates, with the number of needle modifications and CT scan timings during the procedure serving as supplementary outcome evaluation standards. The Wilcoxon rank sum test is used for the comparison., Results: The puncture procedure's success rates after just one puncture: The test group punctures accurately without adjusting the puncture needle, while the control group uses an average number of 1.73 ± 1.20 pins. The once-puncture success rate of robot navigation puncture is considerably higher than that of bare-handed puncture (P < 0.001). The times of CT scan are necessitated when the puncture is in place: the average times in the test group is 3.03 ± 0.18 times, while the control group is 4.70 ± 1.24 times., Conclusion: In conclusion, the robotic-assisted navigation system improves puncture accuracy while reducing the need for needle corrections during percutaneous puncture procedures. It also shortens CT scans and reduces radiation exposure from X-rays., (Copyright © 2024 Copyright: © 2024 Journal of Cancer Research and Therapeutics.)

Objectives: Current resuscitation guidelines recommend target chest compression depth (CCd) of approximately 4cm for infants and 5cm for children. Previous reports based on chest CT suggest these recommended CCd targets might be too deep for younger children. Our aim was to examine measurements of anterior-posterior chest diameter (APd) with a laser distance meter and calculate CCd targets in critically ill infants and children., Design: A retrospective descriptive study., Setting: Single-center PICU, using data from May 2019 to May 2022., Patients: All critically ill children admitted to PICU and under 8 years old were eligible to be included in the retrospective cohort., Interventions: None., Measurements and Main Results: The chest APd measurements using a laser distance meter are part of our usual practice on the PICU. Target CCd and the over-compression threshold CCd for each age group was calculated as 1/3 and 1/2 of APd, respectively. In 555 patients, the median (interquartile range) of the calculated target CCd for each age group was: 2.7 cm (2.5-2.9 cm), 2.9 cm (2.7-3.2 cm), 3.2 cm (3-3.5 cm), 3.4 cm (3.2-3.6 cm), 3.4 cm (3.2-3.6 cm), 3.6 cm (3.4-3.8 cm), 3.6 cm (3.4-4 cm), and 4 cm (3.5-4.2 cm), for 0, 2, 3-5, 6-8, 9-11, 12-17, 18-23, 24 to less than 60, and 60 to less than 96 months, respectively. Using guideline-recommended absolute CCd targets, 4 cm for infants and 5 cm for children, 49% of infants between 0 and 2 months, and 45.5% of children between 12 and 17 months would be over-compressed during cardiopulmonary resuscitation., Conclusions: In our cohort, the 1/3 CCd targets calculated from APd measured by laser meter were shallower than the guideline-recommended CCd. Further studies including evaluating hemodynamics during cardiopulmonary resuscitation with these shallower CCd targets are needed., Competing Interests: Dr. Nadkarni received unrestricted research funding to his institution from the National Institutes of Health, Agency for Healthcare Research and Quality, ZOLL Medical Corporation, Nihon Kohden, and Volunteers on Scientific Advisory Committees for the American Heart Association, Citizen Cardiopulmonary Resuscitation (CPR) Foundation, the International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE), and Citizen CPR; he received funding from the Society of Critical Care Medicine and the Laerdal Foundation; and he is the President of the Society of Critical Care Medicine to 2023. Ms. Niles disclosed that the Children’s Hospital of Philadelphia receives support from an unrestricted research grant from ZOLL Medical Corporation. The remaining authors have disclosed that they do not have any potential conflicts of interest., (Copyright © 2024 by the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies.)

Background: The worldwide COVID-19 pandemic has initiated a change in medical education and the development of new teaching concepts has become inevitable to maintain adequate training., Objective: This pilot study aims to compare teledidactic teaching with traditional face-to-face teaching for abdominal, thoracic, and thyroid ultrasound., Design: Concurrently, a teledidactic and a face-to-face ultrasound course were held. The students completed seven 90-min modules using mobile ultrasound probes (Butterfly IQ). Each module consisted of a lecture, a demonstration of probe guidance, and independent training., Participants: A total of thirty medical students took part in the study and were randomly assigned to a teledidactic and a face-to-face group., Main Measures: An objective structured assessment of ultrasound skills (OSAUS) was performed as a pre-test and as the final exam and ultrasound images obtained during the exam were evaluated using the brightness mode quality ultrasound imaging examination (B-QUIET) scale., Key Results: No significant difference between the two cohorts on the OSAUS final exam was shown (p > 0.05 in all modules). There was a significant difference in the assessment of the images in the focused assessment with sonography for trauma (FAST) (p 0.015) and aorta (p 0.017) modules. Students in the teledidactic group performed better in both modules, scoring 33.59 (± 2.61) out of 44 in the module FAST (face-to-face group 30.95 (± 1.76)) and aortic images averaged 35.41 (± 2.61) points (face-to-face group 32.35 (± 3.08))., Conclusions: A teledidactic course for abdominal and thoracic ultrasound examinations is equally effective to traditional face-to-face teaching in this pilot study. Digital implementation with a portable ultrasound machine could be a great opportunity to promote ultrasound education worldwide and over great distances., (© 2024. The Author(s).)

Introduction: Ankylosing spondylitis is chronic progressive disease, which decrease functions of musculoskeletal system including chest area. Those changes influences respiratory mechanics, worsen conditions of proper ventilation of lungs., Objectives: Rating of functional and respiratory parameters and dependence between them at patients with ankylosing spondylitis., Materials & Methods: The study included 45 patients with diagnosed ankylosing spondylitis. Chest and upper limbs mobility, resting spinal curvature alignment were assessed, and respiratory parameters were measured in a plethysmographic chamber JAGGER MasterScreen Body., Results: Ankylosing spondylitis patients had lower respiratory parameters especially sReff, and FRC. Restriction of chest and upper limbs mobility was also demonstrated. Forward head extension was observed based on the occipital wall test. Correlations between functional parameters and correlations between functional and respiratory parameters were shown, in particular MIP, MEP, sReff, Rtot, TLC, ERV., Conclusions: The study confirmed a decrease in functional and respiratory parameters in the examined patients with ankylosing spondylitis compared to the applicable standards. A significant relationship was found between functional parameters in the upper body and respiratory parameters, which worsen with increasing thoracic dysfunction. The obtained results indicate the directions of therapy that should be taken into account to improve respiratory parameters and reduce respiratory dysfunction in these patients. Chest-focused physiotherapy appears to be an important element in improving function in patients with ankylosing spondylitis., (© 2024. The Author(s).)

Low-frequency ultrasound can permeate human thorax and can be applied in functional imaging of the respiratory system. In this study, we investigate the transmission of low-frequency ultrasound through the human thorax and propose a waveform matching method to track the changes in the transmission signal during subject's respiration. The method's effectiveness is validated through experiments involving ten human subjects. Furthermore, the experimental findings indicate that the traveltime of the first-arrival signal remains consistent throughout the respiratory cycle. Leveraging this observation, we introduce an algorithm for ultrasound thorax attenuation factor differential imaging. By computing the paths and energy variation of the first-arrival signal from the received waveform, the algorithm reconstructs the distribution of attenuation factor differences between two different thorax states, providing insights into the functional status of the respiratory system. Numerical experiments, using both normal thorax and defective thorax models, confirm the algorithm's feasibility and its robustness against noise, variations in transducer position and orientation. These results highlight the potential of low-frequency ultrasound for bedside, continuous monitoring of human respiratory system through functional imaging.

Anomalies of the fetal chest require advanced imaging with ultrasound and MR imaging as well as expertise on the part of the interpreting pediatric radiologist. Congenital diaphragmatic hernia and congenital lung malformation are the most frequently seen, and in both conditions, the radiologist should provide both detailed anatomic description and measurement data for prognostication. This article provides a detailed approach to imaging the anatomy, in-depth explanation of available measurements and prognostic value, and keys to identifying candidates for fetal intervention. Less common congenital lung tumors and mediastinal and chest wall masses are also reviewed., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Accurate motion estimation at high acceleration factors enables rapid motion-compensated reconstruction in Magnetic Resonance Imaging (MRI) without compromising the diagnostic image quality. In this work, we introduce an attention-aware deep learning-based framework that can perform non-rigid pairwise registration for fully sampled and accelerated MRI. We extract local visual representations to build similarity maps between the registered image pairs at multiple resolution levels and additionally leverage long-range contextual information using a transformer-based module to alleviate ambiguities in the presence of artifacts caused by undersampling. We combine local and global dependencies to perform simultaneous coarse and fine motion estimation. The proposed method was evaluated on in-house acquired fully sampled and accelerated data of 101 patients and 62 healthy subjects undergoing cardiac and thoracic MRI. The impact of motion estimation accuracy on the downstream task of motion-compensated reconstruction was analyzed. We demonstrate that our model derives reliable and consistent motion fields across different sampling trajectories (Cartesian and radial) and acceleration factors of up to 16x for cardiac motion and 30x for respiratory motion and achieves superior image quality in motion-compensated reconstruction qualitatively and quantitatively compared to conventional and recent deep learning-based approaches. The code is publicly available at https://github.com/lab-midas/GMARAFT.

This study aimed to investigate body physical parameters as substitutes for water equivalent diameter (Dw) while calculating size-specific dose estimates (SSDEs) during adult chest computed tomography (CT). A retrospective analysis was conducted on 776 patients. Patients were divided into training set (542 patients) and validation set (234 patients) according to a ratio of 7:3. The correlations between physical parameters and Dw were analyzed. The differences between SSDE substitutes and the reference SSDE (SSDE reference ) were compared. Strong positive correlations were observed between body mass index (BMI) and Dw as well as between weight and Dw in overall, male, and female patients (all p < 0.001). The correlations between BMI and Dw were stronger than those between weight and Dw in overall, male, and female subjects (all p < 0.001). SSDE weight and SSDE BMI were not significantly different from SSDE reference (p > 0.05). The RMSEs of overall patients between SSDE weight and SSDE reference as well as between SSDE BMI and SSDE reference were 0.237 and 0.2, respectively. The use of sex-specific regression equations for BMI caused a slightly reduction in RMSE. Weight and BMI can be used as surrogate parameters for Dw when calculating SSDE in adult chest CT exams, with BMI being the preferred substitute parameter., (© 2024. The Author(s).)

Objectives: To study chest computed tomography (CT) manifestations in neonates with chronic granulomatous disease (CGD) to provide clues for early diagnosis of this disease., Methods: A retrospective analysis was conducted on the clinical data and chest CT scan results of neonates diagnosed with CGD from January 2015 to December 2022 at Anhui Provincial Children's Hospital., Results: Nine neonates with CGD were included, with eight presenting respiratory symptoms as the initial sign. Chest CT findings included: consolidation in all 9 cases; nodules in all 9 cases, characterized by multiple, variably sized scattered nodules in both lungs; masses in 4 cases; cavities in 3 cases; abscesses in 6 cases; bronchial stenosis in 2 cases; pleural effusion, interstitial changes, and mediastinal lymphadenopathy each in 1 case. CT enhancement scans showed nodules and masses with uneven or ring-shaped enhancement; no signs of pulmonary emphysema, lung calcification, halo signs, crescent signs, bronchiectasis, or scar lesions were observed. There was no evidence of rib or vertebral bone destruction. Fungal infections were present in 8 of the 9 cases, including 6 with Aspergillus infections; three of these involved mixed infections with Aspergillus , with masses most commonly associated with mixed Aspergillus infections (3/4)., Conclusions: The primary manifestations of neonatal CGD on chest CT are consolidation, nodules, and/or masses, with Aspergillus as a common pathogen. These features can serve as early diagnostic clues for neonatal CGD.

Objective. Electrical impedance tomography (EIT) has been used to determine regional lung ventilation distribution in humans for decades, however, the effect of biological sex on the findings has hardly ever been examined. The aim of our study was to determine if the spatial distribution of ventilation assessed by EIT during quiet breathing was influenced by biological sex. Approach. 219 adults with no known acute or chronic lung disease were examined in sitting position with the EIT electrodes placed around the lower chest (6th intercostal space). EIT data were recorded at 33 images/s during quiet breathing for 60 s. Regional tidal impedance variation was calculated in all EIT image pixels and the spatial distribution of the values was determined using the established EIT measures of centre of ventilation in ventrodorsal (CoV vd ) and right-to-left direction (CoV rl ), the dorsal and right fraction of ventilation, and ventilation defect score. Main results. After exclusion of one subject due to insufficient electrode contact, 218 data sets were analysed (120 men, 98 women) (age: 53 ± 18 vs 50 ± 16 yr ( p = 0.2607), body mass index: 26.4 ± 4.0 vs 26.4 ± 6.6 kg m -2 ( p = 0.9158), mean ± SD). Highly significant differences in ventilation distribution were identified between men and women between the right and left chest sides (CoV rl : 47.0 ± 2.9 vs 48.8 ± 3.3% of chest diameter ( p < 0.0001), right fraction of ventilation: 0.573 ± 0.067 vs 0.539 ± 0.071 ( p = 0.0004)) and less significant in the ventrodorsal direction (CoV vd : 55.6 ± 4.2 vs 54.5 ± 3.6% of chest diameter ( p = 0.0364), dorsal fraction of ventilation: 0.650 ± 0.121 vs 0.625 ± 0.104 ( p = 0.1155)). Ventilation defect score higher than one was found in 42.5% of men but only in 16.6% of women. Significance. Biological sex needs to be considered when EIT findings acquired in upright subjects in a rather caudal examination plane are interpreted. Sex differences in chest anatomy and thoracoabdominal mechanics may explain the results., (Creative Commons Attribution license.)

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field., (© 2024. The Author(s).)

Background: Current guidelines recommend the reporting of incidental coronary artery calcification (CAC) on non-electrocardigram-gated computed tomography (CT) scans of the chest. The finding of incidental moderate or severe CAC on non-cardiac non-contrast chest CT correlates with a CAC score ≥ 100 Agatston units, a guideline-based indication for a clinician-patient discussion regarding the initiation of statin therapy. In contemporary practice, whether the presence and severity of incidental CAC are routinely reported on such CT scans of the chest is unknown., Methods: At a major university hospital, we collected a one-month convenience sample of 297 patients who had chest CT imaging for indications other than lung cancer screening (OICT) and 42 patients who underwent lung cancer chest CT screening (LSCT). We evaluated reporting patterns of incidental CAC in the body and impression of the reports as compared to the overreading of such studies by a board-certified CT chest radiologist. We hypothesized and demonstrated that there was underreporting of incidental CAC on these scans. We then undertook an initiative to educate reporting radiologists on the importance of reporting CAC and implemented a reporting template change to encourage routine reporting. Then we repeated another one-month sample (n= 363 for the OICT and n= 63 for the LSCT groups) to evaluate reporting patterns following our intervention., Results: The presence of incidental moderate and severe CAC was systematically underreported in the OICT group (0 and 4.8 %) and the severity was never mentioned in the impression of reports. In the LSCT group, the presence of incidental moderate and severe CAC was also underreported (66.7 % and 75 %) and the severity of CAC was mentioned 50 % of the time in the impression of the reports. Following the initiation of an educational program and radiology reporting template change, there was a significant increase in reporting of moderate or severe CAC in the OICT group (0 vs. 80.0 %, p < 0.001) and (4.8 vs. 93.5 %, p < 0.001) respectively and a significant increase in the reporting of the severity of incidental CAC for those with severe CAC in the LSCT group (50 vs. 94.1 %, p=0.006)., Conclusion: Despite guideline recommendations, incidental CAC was underreported at a large academic center. We implemented a system that significantly improved reporting patterns of incidental CAC. Failure to report incidental CAC represents a missed opportunity to initiate preventive therapies. Hospital systems interested in improving the quality of their radiology reporting procedures should examine their practices to assure that CAC quantification is routinely performed., Competing Interests: Declaration of competing interest All authors confirm no relationship with industry, disclosures, or source of funding for the work presented in this manuscript., (Copyright © 2024. Published by Elsevier Inc.)

Purpose: The aim of this study was to compare the organ doses assessed through a digital phantom-based and a patient specific-based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms., Methods: Two Monte Carlo based dose calculation tools were used to assess organ doses in routine adult thorax CT examinations. These were a digital phantom-based dosimetry tool (NCICT, National Cancer Institute, USA) and a patient-specific individualized dosimetry tool (ImpactMC, CT Imaging GmbH, Germany). Digital phantoms and patients were classified in four groups according to their water equivalent diameter (D w ). Normalized to volume computed tomography dose index (CTDI vol ), organ dose was assessed for lungs, esophagus, heart, breast, active bone marrow, and skin. Organ doses were compared to measurements performed using thermoluminescent detectors (TLDs) in two physical anthropomorphic phantoms that simulate the average adult individual as a male (Alderson Research Labs, USA) and as a female (ATOM Phantoms, USA)., Results: The average percent difference of NCICT to TLD and ImpactMC to TLD dose measurements across all organs in both sexes was 13% and 6%, respectively. The average ± 1 standard deviation in dose values across all organs with NCICT, ImpactMC, and TLDs was ± 0.06 (mGy/mGy), ± 0.19 (mGy/mGy), and ± 0.13 (mGy/mGy), respectively. Organ doses decreased with increasing D w in both NCICT and ImpactMC., Conclusion: Organ doses estimated with ImpactMC were in closer agreement to TLDs compared to NCICT. This may be attributed to the inherent property of ImpactMC methodology to generate phantoms that resemble the realistic anatomy of the examined patient as opposed to NCICT methodology that incorporates an anatomical discrepancy between phantoms and patients., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Purpose: To develop and validate risk models incorporating clinical and/or imaging parameters based on three-dimensional treatment-planning systems (3D-TPS) to predict the occurrence of 125 I seed migration and the number of migrated seeds <2/≥2 to the chest after brachytherapy for patients with malignant hepatic tumors., Methods and Materials: A total of 480 patients diagnosed with malignant liver tumors receiving 125 I seed brachytherapy from July 2010 to May 2020 were retrospectively enrolled. Variables included 3D-TPS-based CT parameters, that is, the distance from the seed to the inferior vena cava (DSI), the distance from the seed to the second hepatic portal (DSP) and the angle from the seed to the second hepatic portal (ASP), and patients' clinical characteristics, that is, the number of seed implantation procedures (NSP), the maximum number of implanted seeds one time (MAX) and laboratory parameters within 1 week before treatment. Two sets of logistic regression models incorporating clinical and/or imaging variables were developed to predict the occurrence of seed migration and the number of migrated seeds <2/≥2. Model performance was assessed by ROC analysis and decision curve analysis., Results: Compared with the clinical models, the combined model showed a higher discriminative ability for both the prediction of migration occurrence and number of migrated seeds ≥ 2/<2 to the chest (AUC, 0.879 vs. 0.668, p < 0.05; 0.895 vs. 0.701, p < 0.05). The decision curve analysis results indicated higher net benefits of combined models than clinical models. Variables, including DSI, NSP and pretreatment lymphocyte-to-neutrophil ratio, acted as the most important predictors in combined models., Conclusions: The proposed combined models based on 3D-TPS improved discriminative abilities for predicting 125 I seed migration and number of migrated seeds <2/≥2 to the chest after hepatic brachytherapy, being promising to aid clinical decision-making., (Copyright © 2024. Published by Elsevier Inc.)

Background: Management of twin pregnancy after conceptus vesicle fixation in the horse is challenging because the reduction techniques described are either invasive, difficult to perform or associated with disappointing success rates., Objectives: To evaluate the success of transrectal ultrasound-guided fetal thorax compression for reducing post-fixation twin pregnancy in mares., Study Design: Retrospective clinical study., Methods: Sixteen mares were presented for twin reduction between 51 and 79 days of gestation. History obtained from the owner and/or referring veterinarian detailed information regarding the mare (age, breed), pregnancy (day of gestation, dizygotic versus monozygotic twins, unilateral versus bilateral fixation), treatment and outcome (one live fetus at discharge; live singleton at foaling) after twin reduction. Transrectal fetal thorax compression was performed under ultrasound guidance by two experienced operators., Results: Overall 9 of 16 twin pregnancies were successfully reduced and the likelihood of success was significantly higher in dizygotic than monozygotic twins. The procedure was successful in 9 of 10 dizygotic twins but unsuccessful in all six cases of monozygotic twins. Among the dizygotic twins, two mares lost the pregnancy after discharge from the clinic, seven mares delivered a healthy foal of normal size., Main Limitations: Small case number., Conclusions: Transrectal ultrasound-guided fetal thorax compression is a minimally-invasive and successful technique for reducing dizygotic twin pregnancies at approximately 2 months of gestation, but does not lead to any live births in cases of monozygotic twins., (© 2024 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.)

Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been proved that exploring and embedding context knowledge in deep network can significantly improve accuracy. To emphasize these tips, we present CDT-CAD, i.e., context-aware deformable transformers for end-to-end chest abnormality detection on X-Ray images. CDT-CAD first constructs an iterative context-aware feature extractor, which not only enlarges receptive fields to encode multi-scale context information via dilated context encoding blocks, but also captures unique and scalable feature variation patterns in wavelet frequency domain via frequency pooling blocks. Afterwards, a deformable transformer detector on the extracted context features is built to accurately classify disease categories and locate regions, where a small set of key points are sampled, thus leading the detector to focus on informative feature subspace and accelerate convergence speed. Through comparative experiments on Vinbig Chest and Chest Det 10 Datasets, CDT-CAD demonstrates its effectiveness in recognizing chest abnormities and outperforms 1.4% and 6.0% than the existing methods in AP 5 0 and AR on VinBig dateset, and 0.9% and 2.1% on Chest Det-10 dataset, respectively.

Introduction: Mycetoma is a chronic granulomatous inflammatory disease of the subcutaneous tissue, which affects deep structures and bone. Most cases of actinomycetoma are caused by members of the genus Nocardia., Case Presentation: Here we report the case of a 43-year-old male who presented a disseminated mycetoma on the forearm, chest and neck, characterized by enlarged and erythematous lesions through which seropurulent material drains, and numerous atrophic scars. Molecular identification was performed by 16S gene amplification and sequencing. Nocardia mexicana was identified with 100% identity. Trimethoprim-sulfamethoxazole, diaminodiphenyl sulfone and amikacin was a successful treatment after 6 months., Conclusions: Nocardia mexicana is a rare organism that causes mycetoma. We report a case of extensive mycetoma on the forearm with spread to the neck and thorax associated with manipulation of the mouth of a calf., Competing Interests: No Conflict of Interest is declared, (Copyright (c) 2024 Roberto Arenas, Erika J Damián-Magaña, Karla A Sandoval-Navarro, Balfré Torres-Bibiano, Marina Romero-Navarrete, Luary C Martínez-Chavarría, Juan Xicohtencatl-Cortes, Rigoberto Hernández-Castro.)

Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in human-chest models, the HMANN algorithm was proposed using the Hadamard product to optimize multilayer artificial neural networks (MANN). The reconstructed images of the HMANN algorithm were compared with those of the generalized vector sampled pattern matching (GVSPM) algorithm, truncated singular value decomposition (TSVD) algorithm, backpropagation (BP) neural network algorithm, and traditional MANN algorithm. The simulation results showed that the correlation coefficient of the reconstructed images obtained by the HMANN algorithm was increased by 17.30% in the circular cross-section models compared with the MANN algorithm. It was increased by 13.98% in the lung cross-section models. In the lung cross-section models, some of the correlation coefficients obtained by the HMANN algorithm would decrease. Nevertheless, the HMANN algorithm retained the image information of the MANN algorithm in all models, and the HMANN algorithm had fewer artifacts in the reconstructed images. The distinguishability between the objects and the background was better compared with the traditional MANN algorithm. The algorithm could improve the correlation coefficient of the reconstructed images, and effectively remove the artifacts, which provides a new direction to effectively improve the quality of the reconstructed images for EIT.

Objectives: To examine the fetal thymic-thoracic ratio (TTR) in intrahepatic cholestasis of pregnancy (ICP)., Methods: This prospective case-control study was conducted in a single tertiary center. The sample consisted of 86 pregnant women at 28-37 weeks of gestation, including 43 women with ICP and 43 healthy controls. TTR was calculated for each patient using the anterior-posterior measurements of the thymus and intrathoracic mediastinal measurements., Results: The median TTR value was found to be smaller in the ICP group compared to the control group (0.32 vs. 0.36, p<0.001). The ICP group had a greater rate of admission to the neonatal intensive care unit (NICU) (p<0.001). Univariate regression analysis revealed that lower TTR values increased the possibility of NICU admission six times (95 % confidence interval: 0.26-0.39, p=0.01). A statistically significant negative correlation was detected between TTR and the NICU requirement (r: -0.435, p=0.004). As a result of the receiver operating characteristic analysis, in predicting NICU admission, the optimal cut-off value of TTR was determined to be 0.31 with 78 % sensitivity and 67 % specificity (area under the curve=0.819; p<0.001)., Conclusions: We determined that the fetal TTR may be affected by the inflammatory process caused by the maternal-fetal immune system and increased serum bile acid levels in fetal organs in the presence of ICP. We consider that TTR can be used to predict adverse pregnancy outcomes in patients with ICP., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Several data sets have been collected and various artificial intelligence models have been developed for COVID-19 classification and detection from both chest radiography (CXR) and thorax computed tomography (CTX) images. However, the pitfalls and shortcomings of these systems significantly limit their clinical use. In this respect, improving the weaknesses of advanced models can be very effective besides developing new ones. The inability to diagnose ground-glass opacities by conventional CXR has limited the use of this modality in the diagnostic work-up of COVID-19. In our study, we investigated whether we could increase the diagnostic efficiency by collecting a novel CXR data set, which contains pneumonic regions that are not visible to the experts and can only be annotated under CTX guidance. We develop an ensemble methodology of well-established deep CXR models for this new data set and develop a machine learning-based non-maximum suppression strategy to boost the performance for challenging CXR images. CTX and CXR images of 379 patients who applied to our hospital with suspected COVID-19 were evaluated with consensus by seven radiologists. Among these, CXR images of 161 patients who also have had a CTX examination on the same day or until the day before or after and whose CTX findings are compatible with COVID-19 pneumonia, are selected for annotating. CTX images are arranged in the main section passing through the anterior, middle, and posterior according to the sagittal plane with the reformed maximum intensity projection (MIP) method in the coronal plane. Based on the analysis of coronal MIP reconstructed CTX images, the regions corresponding to the pneumonia foci are annotated manually in CXR images. Radiologically classified posterior to anterior (PA) CXR of 218 patients with negative thorax CTX imaging were classified as COVID-19 pneumonia negative group. Accordingly, we have collected a new data set using anonymized CXR (JPEG) and CT (DICOM) images, where the PA CXRs contain pneumonic regions that are hidden or not easily recognized and annotated under CTX guidance. The reference finding was the presence of pneumonic infiltration consistent with COVID-19 on chest CTX examination. COVID-Net, a specially designed convolutional neural network, was used to detect cases of COVID-19 among CXRs. Diagnostic performances were evaluated by ROC analysis by applying six COVID-Net variants (COVIDNet-CXR3-A, -B, -C/COVIDNet-CXR4-A, -B, -C) to the defined data set and combining these models in various ways via ensemble strategies. Finally, a convex optimization strategy is carried out to find the outperforming weighted ensemble of individual models. The mean age of 161 patients with pneumonia was 49.31 ± 15.12, and the median age was 48 years. The mean age of 218 patients without signs of pneumonia in thorax CTX examination was 40.04 ± 14.46, and the median was 38. When working with different combinations of COVID-Net's six variants, the area under the curve (AUC) using the ensemble COVID-Net CXR 4A-4B-3C was .78, sensitivity 67%, specificity 95%; COVID-Net CXR 4a-3b-3c was .79, sensitivity 69% and specificity 94%. When diverse and complementary COVID-Net models are used together through an ensemble, it has been determined that the AUC values are close to other studies, and the specificity is significantly higher than other studies in the literature., (© 2024 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.)

Background and Aim: This study aimed to elucidate the effectiveness of bedside thoracic ultrasound according to BLUE protocol and to investigate its superiority over other imaging methods in the emergency service., Methods: A total of 120 patients admitted to our institution's emergency care department due to respiratory distress have been enrolled in this prospective research. Thorax USG has been performed in the right and left hemithorax at the points specified in the BLUE protocol for each patient. Pleural sliding motion, A-lines, B-lines, consolidation, effusion, and the presence of barcode signs were evaluated individually. Age, sex, comorbid diseases, other radiological examination findings, laboratory findings, final clinical diagnosis, and hospitalization-discharge status of the patients were recorded., Results: When a correct diagnosis of pneumonia has been analyzed for imaging techniques, the diagnostic rate of chest radiography was 83.3%, CT was 100.0%, and USG was 66.6%. The correct diagnostic rate of chest radiography was 94.5%; CT and USG were 100.0%. The correct diagnosis of pulmonary edema on chest radiography was 94.5%; CT and USG were 100.0%. While the correct diagnosis of pleural effusion on chest radiography and CT was 100.0%, it was 92.3% in USG imaging. Finally, CT and USG imaging performed better than chest radiography in patients with pneumothorax (chest radiography 80.0%, CT and USG 100%)., Conclusion: USG imaging could be preferred in the diagnosis of pneumonia, pulmonary edema, pleural effusion, pneumothorax, pulmonary embolism, and differential diagnosis at the emergency service., (© 2024. The Author(s), under exclusive licence to Royal Academy of Medicine in Ireland.)

Introduction: Concerns regarding the adverse consequences of radiation have increased due to the expanded application of computed tomography (CT) in medical practice. Certain studies have indicated that the radiation dosage depends on the anatomical region, the imaging technique employed and patient-specific variables. The aim of this study is to present fitting models for the estimation of age-specific dose estimates (ASDE), in the same direction of size-specific dose estimates, and effective doses based on patient age, gender and the type of CT examination used in paediatric head, chest and abdomen-pelvis imaging., Methods: A total of 583 paediatric patients were included in the study. Radiometric data were gathered from DICOM files. The patients were categorised into five distinct groups (under 15 years of age), and the effective dose, organ dose and ASDE were computed for the CT examinations involving the head, chest and abdomen-pelvis. Finally, the best fitting models were presented for estimation of ASDE and effective doses based on patient age, gender and the type of examination., Results: The ASDE in head, chest, and abdomen-pelvis CT examinations increases with increasing age. As age increases, the effective dose in head and abdomen-pelvis CT scans decreased. However, for chest scans, the effective dose initially showed a decreasing trend until the first year of life; after that, it increases in correlation with age., Conclusions: Based on the presented fitting model for the ASDE, these CT scan quantities depend on factors such as patient age and the type of CT examination. For the effective dose, the gender was also included in the fitting model. By utilising the information about the scan type, region and age, it becomes feasible to estimate the ASDE and effective dose using the models provided in this study., (© 2024 The Authors. Journal of Medical Radiation Sciences published by John Wiley & Sons Australia, Ltd on behalf of Australian Society of Medical Imaging and Radiation Therapy and New Zealand Institute of Medical Radiation Technology.)

Paediatric imaging protocols should be carefully optimised to maintain the desired image quality while minimising the delivered patient dose. A paediatric chest phantom was designed, constructed and evaluated to optimise chest CT examinations for infants. The phantom was designed to enable dosimetry and image quality measurements within the anthropomorphic structure. It was constructed using tissue equivalent materials to mimic thoracic structures of infants, aged 0-6 months. The phantom materials were validated across a range of diagnostic tube voltages with resulting CT numbers found equivalent to paediatric tissues observed via a survey of clinical paediatric chest studies. The phantom has been successfully used to measure radiation dose and evaluate various image quality parameters for paediatric specific protocols., (© 2024. Crown.)

Background: Accurate and automated segmentation of thoracic organs-at-risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs., Purpose: To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT., Methods: A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded-TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result., Results: We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto-Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset., Conclusions: This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning., (© 2023 American Association of Physicists in Medicine.)

Dosimetry audits for passive motion management require dynamically-acquired measurements in a moving phantom to be compared to statically calculated planned doses. This study aimed to characterise the relationship between planning and delivery errors, and the measured dose in the Imaging and Radiation Oncology Core (IROC) thorax phantom, to assess different audit scoring approaches. Treatment plans were created using a 4DCT scan of the IROC phantom, equipped with film and thermoluminescent dosimeters (TLDs). Plans were created on the average intensity projection from all bins. Three levels of aperture complexity were explored: dynamic conformal arcs (DCAT), low-, and high-complexity volumetric modulated arcs (VMATLo, VMATHi). Simulated-measured doses were generated by modelling motion using isocenter shifts. Various errors were introduced including incorrect setup position and target delineation. Simulated-measured film doses were scored using gamma analysis and compared within specific regions of interest (ROIs) as well as the entire film plane. Positional offsets were estimated based on isodoses on the film planes, and point doses within TLD contours were compared. Motion-induced differences between planned and simulated-measured doses were evident even without introduced errors Gamma passing rates within target-centred ROIs correlated well with error-induced dose differences, while whole film passing rates did not. Isodose-based setup position measurements demonstrated high sensitivity to errors. Simulated point doses at TLD locations yielded erratic responses to introduced errors. ROI gamma analysis demonstrated enhanced sensitivity to simulated errors compared to whole film analysis. Gamma results may be further contextualized by other metrics such as setup position or maximum gamma., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Associazione Italiana di Fisica Medica e Sanitaria. All rights reserved.)

Background and Objectives: Generating coherent reports from medical images is an important task for reducing doctors' workload. Unlike traditional image captioning tasks, the task of medical image report generation faces more challenges. Current models for generating reports from medical images often fail to characterize some abnormal findings, and some models generate reports with low quality. In this study, we propose a model to generate high-quality reports from medical images., Methods: In this paper, we propose a model called Hybrid Discriminator Generative Adversarial Network (HDGAN), which combines Generative Adversarial Network (GAN) with Reinforcement Learning (RL). The HDGAN model consists of a generator, a one-sentence discriminator, and a one-word discriminator. Specifically, the RL reward signals are judged on the one-sentence discriminator and one-word discriminator separately. The one-sentence discriminator can better learn sentence-level structural information, while the one-word discriminator can learn word diversity information effectively., Results: Our approach performs better on the IU-X-ray and COV-CTR datasets than the baseline models. For the ROUGE metric, our method outperforms the state-of-the-art model by 0.36 on the IU-X-ray, 0.06 on the MIMIC-CXR and 0.156 on the COV-CTR., Conclusions: The compositional framework we proposed can generate more accurate medical image reports at different levels., Competing Interests: Declaration of competing interest We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction . The currently available dosimetry techniques in computed tomography can be inaccurate which overestimate the absorbed dose. Therefore, we aimed to provide an automated and fast methodology to more accurately calculate the SSDE usingDwobtained by using CNN from thorax and abdominal CT study images. Methods . The SSDE was determined from the 200 records files. For that purpose, patients' size was measured in two ways: (a) by developing an algorithm following the AAPM Report No. 204 methodology; and (b) using a CNN according to AAPM Report No. 220. Results . The patient's size measured by the in-house software in the region of thorax and abdomen was 27.63 ± 3.23 cm and 28.66 ± 3.37 cm, while CNN was 18.90 ± 2.6 cm and 21.77 ± 2.45 cm. The SSDE in thorax according to 204 and 220 reports were 17.26 ± 2.81 mGy and 23.70 ± 2.96 mGy for women and 17.08 ± 2.09 mGy and 23.47 ± 2.34 mGy for men. In abdomen was 18.54 ± 2.25 mGy and 23.40 ± 1.88 mGy in women and 18.37 ± 2.31 mGy and 23.84 ± 2.36 mGy in men. Conclusions . Implementing CNN-based automated methodologies can contribute to fast and accurate dose calculations, thereby improving patient-specific radiation safety in clinical practice., (© 2024 IOP Publishing Ltd.)

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations., Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines., Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations., Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy., Competing Interests: No competing interests were disclosed., (Copyright: © 2024 Chandran M O et al.)

Non-linear properties of iterative reconstruction (IR) algorithms can alter image texture. We evaluated the effect of a model-based IR algorithm (advanced modelled iterative reconstruction; ADMIRE) and dose on computed tomography thorax image quality. Dual-source scanner data were acquired at 20, 45 and 65 reference mAs in 20 patients. Images reconstructed with filtered back projection (FBP) and ADMIRE Strengths 3-5 were assessed independently by six radiologists and analysed using an ordinal logistic regression model. For all image criteria studied, the effects of tube load 20 mAs and all ADMIRE strengths were significant (p < 0.001) when compared to reference categories 65 mAs and FBP. Increase in tube load from 45 to 65 mAs showed image quality improvement in three of six criteria. Replacing FBP with ADMIRE significantly improves perceived image quality for all criteria studied, potentially permitting a dose reduction of almost 70% without loss in image quality., (© The Author(s) 2024. Published by Oxford University Press.)

Background and Objective: High-frequency chest wall compression (HFCC) therapy by airway clearance devices (ACDs) acts on the rheological properties of bronchial mucus to assist in clearing pulmonary secretions. Investigating low-frequency vibrations on the human thorax through numerical simulations is critical to ensure consistency and repeatability of studies by reducing extreme variability in body measurements across individuals. This study aims to present the numerical investigation of the harmonic acoustic excitation of ACDs on the human chest as a gentle and effective HFCC therapy., Methods: Four software programs were sequentially used to visualize medical images, decrease the number of surfaces, generate and repair meshes, and conduct numerical analysis, respectively. The developed methodology supplied the validation of the effect of HFCC through computed tomography-based finite element analysis (CT-FEM) of a human thorax. To illustrate the vibroacoustic characteristics of the HFCC therapy device, a 146-decibel sound pressure level (dB SPL ) was applied on the back-chest surface of the model. Frequency response function (FRF) across 5-100 Hz was analyzed to characterize the behaviour of the human thorax with the state-space model., Results: We discovered that FRF pertaining to accelerance equals 0.138 m/s 2 N at the peak frequency of 28 Hz, which is consistent with two independent experimental airway clearance studies reported in the literature. The state-space model assessed two apparent resonance frequencies at 28 Hz and 41 Hz for the human thorax. The total displacement, kinetic energy density, and elastic strain energy density were furthermore quantified at 1 µm, 5.2 µJ/m 3 , and 140.7 µJ/m 3 , respectively, at the resonance frequency. In order to deepen our understanding of the impact on internal organs, the model underwent simulations in both the time domain and frequency domain for a comprehensive analysis., Conclusion: Overall, the present study enabled determining and validating FRF of the human thorax to roll out the inconsistencies, contributing to the health of individuals with investigating gentle but effective HFCC therapy conditions with ACDs. This innovative finding furthermore provides greater clarity and a tangible understanding of the subject by simulating the responses of CT-FEM of the human thorax and internal organs at resonance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have influenced the present work., (Crown Copyright © 2024. Published by Elsevier B.V. All rights reserved.)

In the realm of healthcare, the demand for swift and precise diagnostic tools has been steadily increasing. This study delves into a comprehensive performance analysis of three pre-trained convolutional neural network (CNN) architectures: ResNet50, DenseNet121, and Inception-ResNet-v2. To ensure the broad applicability of our approach, we curated a large-scale dataset comprising a diverse collection of chest X-ray images, that included both positive and negative cases of COVID-19. The models' performance was evaluated using separate datasets for internal validation (from the same source as the training images) and external validation (from different sources). Our examination uncovered a significant drop in network efficacy, registering a 10.66% reduction for ResNet50, a 36.33% decline for DenseNet121, and a 19.55% decrease for Inception-ResNet-v2 in terms of accuracy. Best results were obtained with DenseNet121 achieving the highest accuracy at 96.71% in internal validation and Inception-ResNet-v2 attaining 76.70% accuracy in external validation. Furthermore, we introduced a model ensemble approach aimed at improving network performance when making inferences on images from diverse sources beyond their training data. The proposed method uses uncertainty-based weighting by calculating the entropy in order to assign appropriate weights to the outputs of each network. Our results showcase the effectiveness of the ensemble method in enhancing accuracy up to 97.38% for internal validation and 81.18% for external validation, while maintaining a balanced ability to detect both positive and negative cases., (© 2024. The Author(s).)

In this article, we introduce a novel use of depth camera to extract cardiac pulse signal from human chest area, in which the depth information is obtained from a near infrared sensor using time-of-flight technology. We successfully isolate weak chest motion due to heartbeat by processing a sequence of depth images without raising privacy concern. We discuss motion sensitivity in depth video with examples from actuator simulation and human chest motion. Compared to other imaging modalities, the depth image intensity can be directly used for micromotion reconstruction. To deal with the challenges of recovering heartbeat from the chest area, we develop a set of coherent processing techniques to suppress the unwanted motion interference from breathing motion and involuntary body motion and eventually obtain clean cardiac pulse signal. We, thus, derive inter-beat-interval, showing high consistency to the contact photoplethysmography. Additionally, we develop a graphical interpretation of the most and the less pulsatile principal components in eigen space. For validation, we test our method on ten healthy human subjects with different resting heart rates. More importantly, we conduct a set of experiments to study the robustness and weakness of our methods, including extended range, multi-subject, thickness of clothes and generation to other measurement site.

Background and Objective: Multi-label Chest X-ray (CXR) images often contain rich label relationship information, which is beneficial to improve classification performance. However, because of the intricate relationships among labels, most existing works fail to effectively learn and make full use of the label correlations, resulting in limited classification performance. In this study, we propose a multi-label learning framework that learns and leverages the label correlations to improve multi-label CXR image classification., Methods: In this paper, we capture the global label correlations through the self-attention mechanism. Meanwhile, to better utilize label correlations for guiding feature learning, we decompose the image-level features into label-level features. Furthermore, we enhance label-level feature learning in an end-to-end manner by a consistency constraint between global and local label correlations, and a label correlation guided multi-label supervised contrastive loss., Results: To demonstrate the superior performance of our proposed approach, we conduct three times 5-fold cross-validation experiments on the CheXpert dataset. Our approach obtains an average F1 score of 44.6% and an AUC of 76.5%, achieving a 7.7% and 1.3% improvement compared to the state-of-the-art results., Conclusion: More accurate label correlations and full utilization of the learned label correlations help learn more discriminative label-level features. Experimental results demonstrate that our approach achieves exceptionally competitive performance compared to the state-of-the-art algorithms., Competing Interests: Declaration of Competing Interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Label Correlation guided Discriminative Label Feature Learning for Multi-label Chest Image Classification”., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Dynamic digital radiography (DDR) is a high-resolution radiographic imaging technique using pulsed X-ray emission to acquire a multiframe cine-loop of the target anatomical area. The first DDR technology was orthostatic chest acquisitions, but new portable equipment that can be positioned at the patient's bedside was recently released, significantly expanding its potential applications, particularly in chest examination. It provides anatomical and functional information on the motion of different anatomical structures, such as the lungs, pleura, rib cage, and trachea. Native images can be further analyzed with dedicated post-processing software to extract quantitative parameters, including diaphragm motility, automatically projected lung area and area changing rate, a colorimetric map of the signal value change related to respiration and motility, and lung perfusion. The dynamic diagnostic information along with the significant advantages of this technique in terms of portability, versatility, and cost-effectiveness represents a potential game changer for radiological diagnosis and monitoring at the patient's bedside. DDR has several applications in daily clinical practice, and in this narrative review, we will focus on chest imaging, which is the main application explored to date in the literature. However, studies are still needed to understand deeply the clinical impact of this method.

Deep learning technology can effectively assist physicians in diagnosing chest radiographs. Conventional domain adaptation methods suffer from inaccurate lesion region localization, large errors in feature extraction, and a large number of model parameters. To address these problems, we propose a novel domain-adaptive method WDDM to achieve abnormal identification of chest radiographic images by combining Wasserstein distance and difference measures. Specifically, our method uses BiFormer as a multi-scale feature extractor to extract deep feature representations of data samples, which focuses more on discriminant features than convolutional neural networks and Swin Transformer. In addition, based on the loss minimization of Wasserstein distance and contrast domain differences, the source domain samples closest to the target domain are selected to achieve similarity and dissimilarity across domains. Experimental results show that compared with the non-transfer method that directly uses the network trained in the source domain to classify the target domain, our method has an average AUC increase of 14.8% and above. In short, our method achieves higher classification accuracy and better generalization performance., (© 2024. The Author(s).)

The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly in terms of diagnosis and management of thoracic disorders. This study proposes a comprehensive framework, named Lung-DT, which leverages IoT sensors and AI algorithms to establish the digital representation of a patient's respiratory health. Using the YOLOv8 neural network, the Lung-DT system accurately classifies chest X-rays into five distinct categories of lung diseases, including "normal", "covid", "lung&#95;opacity", "pneumonia", and "tuberculosis". The performance of the system was evaluated employing a chest X-ray dataset available in the literature, demonstrating average accuracy of 96.8%, precision of 92%, recall of 97%, and F1-score of 94%. The proposed Lung-DT framework offers several advantages over conventional diagnostic methods. Firstly, it enables real-time monitoring of lung health through continuous data acquisition from IoT sensors, facilitating early diagnosis and intervention. Secondly, the AI-powered classification module provides automated and objective assessments of chest X-rays, reducing dependence on subjective human interpretation. Thirdly, the twin digital representation of the patient's respiratory health allows for comprehensive analysis and correlation of multiple data streams, providing valuable insights as to personalized treatment plans. The integration of IoT sensors, AI algorithms, and DT technology within the Lung-DT system demonstrates a significant step towards improving thoracic healthcare. By enabling continuous monitoring, automated diagnosis, and comprehensive data analysis, the Lung-DT framework has enormous potential to enhance patient outcomes, reduce healthcare costs, and optimize resource allocation.

Background: Dual-energy (DE) chest radiography (CXR) enables the selective imaging of two relevant materials, namely, soft tissue and bone structures, to better characterize various chest pathologies (i.e., lung nodule, bony lesions, etc.) and potentially improve CXR-based diagnosis. Recently, deep-learning-based image synthesis techniques have attracted considerable attention as alternatives to existing DE methods (i.e., dual-exposure-based and sandwich-detector-based methods) because software-based bone-only and bone-suppression images in CXR could be useful., Purpose: The objective of this study was to develop a new framework for DE-like CXR image synthesis from single-energy computed tomography (CT) based on a cycle-consistent generative adversarial network., Methods: The core techniques of the proposed framework are divided into three categories: (1) data configuration from the generation of pseudo CXR from single energy CT, (2) learning of the developed network architecture using pseudo CXR and pseudo-DE imaging using a single-energy CT, and (3) inference of the trained network on real single-energy CXR. We performed a visual inspection and comparative evaluation using various metrics and introduced a figure of image quality (FIQ) to consider the effects of our framework on the spatial resolution and noise in terms of a single index through various test cases., Results: Our results indicate that the proposed framework is effective and exhibits potential synthetic imaging ability for two relevant materials: soft tissue and bone structures. Its effectiveness was validated, and its ability to overcome the limitations associated with DE imaging techniques (e.g., increase in exposure dose owing to the requirement of two acquisitions, and emphasis on noise characteristics) via an artificial intelligence technique was presented., Conclusions: The developed framework addresses X-ray dose issues in the field of radiation imaging and enables pseudo-DE imaging with single exposure., (© 2023 American Association of Physicists in Medicine.)

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

Introduction: Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images., Data Description: To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection that includes not only COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19 dataset, which includes slice-level and patient-level labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative., (© 2024. The Author(s).)