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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).)

Background: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease., Objective: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations., Methods: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features are extracted for further processing., Results: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches., Conclusion: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units.

Our study provided a comprehensive characterization of the thorax of Shirazi cats by comparing the relevant soft and bone windows of computed tomography (CT) and magnetic resonance imaging (MRI) with cross, sagittal and coronal sectional anatomy. We outlined the mediastinum and its anatomic relationships with the trachea, oesophagus, lungs, heart, cranial and caudal vena cavae, and other thoracic structures using the data series gathered from adult normal Shirazi cats. The cranial mediastinum extended from the thoracic inlet to the 4th intercostal space, the middle mediastinum extended from the 5th and 7th intercostal spaces and was occupied by the heart and large blood vessels and the caudal mediastinum extended as a short and narrow portion from the 8th intercostal space to the diaphragm. The contents of the mediastinum and its relationship with the lungs and diaphragm were clearly presented in coronal-sectional anatomy and CT slices. The diaphragm was clearly observed in the lung windows of the ventral thorax. Sagittal-sectional anatomy and CT clarified the thorax's architecture and its contents, with higher density in the soft windows. The distribution of thoracic vessels on cross- and coronal-contrast CT scans was clearly visible. In addition, MRI scans provided an excellent anatomic reference of the thorax with the help of cross, coronal and sagittal scans, especially in the heart and blood vessels. Our study provides a valuable atlas for the diagnosis of malformations of the thoracic structures and offers better assessments for helping veterinary radiologists and clinicians in diagnostic processes., (© 2023 Wiley-VCH GmbH. Published by John Wiley & Sons Ltd.)

Background: Segmentation of medical images plays a key role in the correct identification and management of different diseases. In this study, we present a new segmentation method that meets the difficulties posed by sophisticated organ shapes in computed tomography (CT) images, particularly targeting lung, breast, and gastric cancers., Methods: Our suggested methods, Resio-Inception U-Net and Deep Cluster Recognition (RIUDCR), use a Residual Inception Architecture, which combines the power of residual connections and inception blocks to achieve cutting-edge segmentation performance while reducing the risk of overfitting., Results: We present mathematical equations and functions that describe the design, including the encoding and decoding steps within the UC-Net system. Furthermore, we provide strong testing results that show the effectiveness of our method. Through thorough testing on varied datasets, our method regularly beats current techniques, achieving amazing precision and stability in organ task segmentation. These results show the promise of our residual inception architecture in better medical picture analysis., Conclusion: In summary, our research not only shows a state-of-the-art segment methodology but also reinforces its usefulness through thorough testing. The inclusion of residual inception architecture in medical picture segmentation offers good possibilities for improving the identification and management of disease planning., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Purpose: We present a patient-specific model to estimate tumor location in the thorax during radiation therapy using chest surface displacement as the surrogate signal., Methods: Two types of data are used for model construction: Four-dimensional computed tomography (4D-CT) images of the patient and the displacement of two points on the patient's skin on the thoracic area. Principal component analysis is used to fit the correspondence model. This model incorporates the recorded surrogate signals during radiation delivery as input and delivers the 3D trajectory of the tumor as output. We evaluated the accuracy of the proposed model on a respiratory phantom and five lung cancer patients., Results: For the respiratory phantom, the location of the center of the sphere during treatment was calculated in three directions: Left-Right (LR), Anterior-Posterior (AP) and, Superior-Inferior (SI). The error of localization was less than 1 mm in the LR and AP directions and less than 2 mm in the SI direction. The location of the tumor center for two of the patients, and the location of the apex of the diaphragm for the other three, was calculated in three directions. For all patients, the localization error in the LR and AP directions was less than 1.1 mm for two fractions and the maximum localization error in the SI direction was 6.4 mm., Conclusions: This work presents a feasibility study of utilizing surface displacement data to locate the tumor in the thorax during radiation treatment. Future work will validate the model on a larger patient population., 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 © 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.)

Our goal was to investigate the effects of head-thorax elevation (HUP) during chest compressions (CC) on lung ventilation. A prospective study was performed on seven human cadavers. Chest was automatically compressed-decompressed in flat position and during progressive HUP from 18 to 35°. Lung ventilation was measured with electrical impedance tomography. In each cadaver, 5 sequences were randomly performed: one without CC at positive end-expiratory pressure (PEEP) 0cmH 2 O, 3 s with CC at PEEP0, 5 or 10cmH 2 O and 1 with CC and an impedance threshold device at PEEP0cmH 2 O. The minimal-to-maximal change in impedance (VT EIT in arbitrary unit a.u.) and the minimal impedance in every breathing cycle (EELI) the) were compared between flat, 18°, and 35° in each sequence by a mixed-effects model. Values are expressed as median (1st-3rd quartiles). With CC, between flat, 18° and 35° VT EIT decreased at each level of PEEP. It was 12416a.u. (10,689; 14,442), 11,239 (7667; 13,292), and 6457 (4631; 9516), respectively, at PEEP0. The same was true with the impedance threshold device. EELI/VT EIT significantly decreased from - 0.30 (- 0.40; - 0.15) before to - 1.13 (- 1.70; - 0.61) after the CC (P = 0.009). With HUP lung ventilation decreased with CC as compared to flat position. CC are associated with decreased in EELI., (© 2023. The Author(s).)

Aging is an important risk factor for disease, leading to morphological change that can be assessed on Computed Tomography (CT) scans. We propose a deep learning model for automated age estimation based on CT- scans of the thorax and abdomen generated in a clinical routine setting. These predictions could serve as imaging biomarkers to estimate a "biological" age, that better reflects a patient's true physical condition. A pre-trained ResNet-18 model was modified to predict chronological age as well as to quantify its aleatoric uncertainty. The model was trained using 1653 non-pathological CT-scans of the thorax and abdomen of subjects aged between 20 and 85 years in a 5-fold cross-validation scheme. Generalization performance as well as robustness and reliability was assessed on a publicly available test dataset consisting of thorax-abdomen CT-scans of 421 subjects. Score-CAM saliency maps were generated for interpretation of model outputs. We achieved a mean absolute error of 5.76 ± 5.17 years with a mean uncertainty of 5.01 ± 1.44 years after 5-fold cross-validation. A mean absolute error of 6.50 ± 5.17 years with a mean uncertainty of 6.39 ± 1.46 years was obtained on the test dataset. CT-based age estimation accuracy was largely uniform across all age groups and between male and female subjects. The generated saliency maps highlighted especially the lumbar spine and abdominal aorta. This study demonstrates, that accurate and generalizable deep learning-based automated age estimation is feasible using clinical CT image data. The trained model proved to be robust and reliable. Methods of uncertainty estimation and saliency analysis improved the interpretability., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Kerber et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: X-ray Computed Tomography dose levels have been varying among modalities and scanning body regions due to the absence of incessant routine follow-up. Thus, the study aimed to compute the dose index discrepancies in Ethiopia for the most recurring scan protocols (head, chest, abdomen, and pelvis)., Methods: A purposive sampling method was employed to select the hospitals due to the rare existence of functional CT scanners in Ethiopia. From the selected hospitals, a total of 1,385 (249 heads, 804 chests, 132 abdomens, and 200 pelvis) were collected in terms of standard dose metric values in the period of December 2019-March 2020. Patients' DLP was computed into mean value using IBM SPSS Statistics 20 software. From the mean DLP, we can compute the effective dose., Results: Patients' dose level disparity was observed in this study though it is below the ICRP standard level for all body regions except for pelvis DLP (593.37 mGy-cm) at Black Lion. The dose level for the head and chest are computed within the recommended level at all hospitals. Effective doses for the pelvis at four hospitals (Teklehaimanot, Black Lion, ALERT, Paul's, and Ayder hospitals) were computed as 6.45, 8.90, 5.08, 6.54, and 6.84 mSv respectively, and the effective doses for abdomen at Ayder Hospital was obtained to be 8.90 mSv, which is above the recommended value., Conclusion: X-ray CT scanners are somewhat properly functioning although some sort of justification and optimization for pelvis and abdomen examinations are strongly recommended to implement as low as reasonably achievable principle., (© 2023 Gebremedhin, et al.)

Objective: This study aimed to demonstrate the relationship between thorax computed tomography (CT) findings at the time of admission and prognosis using a semiquantitative CT severity scoring system in patients diagnosed with coronavirus disease 2019 (COVID-19) who tested positive for reverse transcriptase polymerase chain reaction (RT-PCR)., Patients and Methods: A total of 305 patients aged 18 years and older who were diagnosed with COVID-19 confirmed by RT-PCR and underwent thorax CT at the time of admission, were included in the study between March and July 2020. The demographic data of the patients, their presenting complaints at the time of admission, RT-PCR results, and thorax CT images were scanned retrospectively from electronic medical records. Lesions on thorax CT were evaluated for the presence of ground glass opacity, consolidation, and septal thickening and scoring., Results: No significant relationship was found between mortality and CT score or other parameters. A significant relationship was found between admission to the intensive care unit and CT scoring (p=0.014), aortic diameter (p=0.032), chronic pulmonary disease (p=0.004), halo sign (p=0.031), mortality (p<0.001), fever (p=0.038), and dyspnea (p=0.031). A statistically significant difference was detected in the score parameter between discharged patients and intensive care unit patients who survived and those who died (p<0.001). In the parameter of the number of lobes, a statistically significant difference was found only between discharged patients and intensive care unit patients who survived (p=0.016)., Conclusions: Thorax CT is an advisor for early diagnosis, treatment, and prognosis assessment of the disease. Semiquantitative CT severity scoring can provide valuable information about the prognosis of the patient.

Background: The effect of clinical history on the interpretation of radiographs has been widely researched in human medicine. There is, however, no data on this topic in veterinary medicine., Hypothesis/objectives: Diagnostic accuracy would improve when history was supplied., Animals: Thirty client-owned dogs with abnormal findings on thoracic radiographs and confirmation of the disease, and 30 healthy client-owned controls were drawn retrospectively., Methods: Retrospective case-control study. Sixty radiographic studies of the thorax were randomized and interpreted by 6 radiologists; first, with no access to the clinical information; and a second time with access to all pertinent clinical information and signalment., Results: A significant increase in diagnostic accuracy was noted when clinical information was provided (64.4% without and 75.2% with clinical information; P = .002). There was no significant difference in agreement between radiologists when comparing no clinical information and with clinical information (Kappa 0.313 and 0.300, respectively)., Conclusions and Clinical Importance: The addition of pertinent clinical information to the radiographic request significantly improves the diagnostic accuracy of thorax radiographs of dogs and is recommended as standard practice., (© 2023 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.)

Precise segmentation of lung field is a crucial step in chest radiographic computer-aided diagnosis system. With the development of deep learning, fully convolutional network based models for lung field segmentation have achieved great effect but are poor at accurate identification of the boundary and preserving lung field consistency. To solve this problem, this paper proposed a lung segmentation algorithm based on non-local attention and multi-task learning. Firstly, an encoder-decoder convolutional network based on residual connection was used to extract multi-scale context and predict the boundary of lung. Secondly, a non-local attention mechanism to capture the long-range dependencies between pixels in the boundary regions and global context was proposed to enrich feature of inconsistent region. Thirdly, a multi-task learning to predict lung field based on the enriched feature was conducted. Finally, experiments to evaluate this algorithm were performed on JSRT and Montgomery dataset. The maximum improvement of Dice coefficient and accuracy were 1.99% and 2.27%, respectively, comparing with other representative algorithms. Results show that by enhancing the attention of boundary, this algorithm can improve the accuracy and reduce false segmentation.

The goal of the study was to evaluate the inter- and intrafractional patient setup accuracy of target volumes located in the head, thoracic, abdominal, and pelvic regions when using SGRT, by comparing it with that of laser alignment using patient skin marks, and to calculate the corresponding setup margins. A total of 2303 radiotherapy fractions of 183 patients were analyzed. All patients received daily kilovoltage cone-beam computed tomography scans (kV-CBCT) for online verification. From November 2019 until September 2020, patient setup was performed using laser alignment with patient skin marks, and since October 2020, using SGRT. The setup accuracy was measured by the six degrees of freedom (6DOF) corrections based on the kV-CBCT. The corresponding setup margins were calculated using the van Herk formula. Analysis of variance (ANOVA) was used to evaluate the impact of multiple factors on the setup accuracy. The inter-fractional patient setup accuracy was significantly better using SGRT compared to laser alignment with skin marks. The mean three-dimensional vector of the translational setup deviation of tumors located in the thorax, abdomen, and pelvis using SGRT was 3.6 mm (95% confidence interval (CI) 3.3 mm to 3.9 mm) and 4.5 mm using laser alignment with skin marks (95% CI 3.9 mm to 5.2 mm; p = 0.001). Calculation of setup margins for the combined inter- and intra-fractional setup error revealed similar setup margins using SGRT and kV-CBCT once a week compared to laser alignment with skin marks and kV-CBCT every other day. Furthermore, comparable setup margins were found for open-face thermoplastic masks with AlignRT compared to closed-face thermoplastic masks with laser alignment and mask marks. SGRT opens the possibility to reduce the number of CBCTs while maintaining sufficient setup accuracy. The advantage is a reduction of imaging dose and overall treatment time. Open-face thermoplastic masks may be used instead of closed-face thermoplastic masks to increase the patient's comfort., (© 2023. Springer Nature Limited.)

Background: Rib fracture(s) occurs in 85% of blunt chest trauma cases. Increasing evidence supports that surgical intervention, particularly for multiple fractures, may improve outcomes. Thoracic morphology diversity across ages and sexes is important to consider in the design and use of surgical intervention devices in chest trauma. However, research on non-average thoracic morphology is lacking., Methods: The rib cage was segmented from patient computed tomography (CT) scans to create 3D point clouds. These point clouds were uniformly oriented and chest height, width, and depth were measured. Size categorization was determined by grouping each dimension into small, medium, and large tertiles. From small and large size combinations, subgroups were extracted to develop thoracic 3D models of the rib cage and surrounding soft tissue., Results: The study population included 141 subjects (48% male) ranging from age 10-80 with ∼20 subjects/age decade. Mean chest volume increased with age by 26% from the age groups 10-20 to 60-70, with 11% of this increase occurring between the youngest groups of 10-20 and 20-30. Across all ages, chest dimensions were ∼10% smaller in females and chest volume was highly variable (SD: ±3936.5 cm 3 ). Representative thoracic models of four males (ages 16, 24, 44, 48) and three females (ages 19, 50, 53) were developed to characterize morphology associated with combinations of small and large chest dimensions., Conclusions: The seven models developed cover a broad range of non-average thoracic morphologies and can serve as a basis for informing device design, surgical planning, and injury risk assessments., Competing Interests: Declaration of competing interest None declared., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

The current study aimed to investigate the relationship between body parameters and the current-time product (mAs) in chest digital radiography using a non-contact infrared thickness-measurement sensor. An anthropomorphic chest phantom was first used to understand variations in mAs over multiple positionings during chest radiography when using the automatic exposure control (AEC) technique. In a human study, 929 consecutive male subjects who underwent regular chest examinations were enrolled, and their height (H), weight (W), and body mass index (BMI) were recorded. In addition, their chest thickness (T) was measured at exhalation using a non-contact infrared sensor, and chest radiography was then performed using the AEC technique. Finally, the relationship between four body parameters (T, BMI, T*BMI, and W/H) and mAs was investigated by fitting the body parameters to mAs using three curve models. The phantom study showed that the maximum mAs was 1.76 times higher than the lowest mAs during multiple positionings in chest radiography. In the human study, all chest radiographs passed the routine quality control procedure and had an exposure index between 100 and 212. In curve fitting, the comparisons showed that W/H had a closer relationship with mAs than the other body parameters, while the first-order power model with W/H fitted to mAs performed the best and had an R-square of 0.9971. We concluded that the relationship between W/H and mAs in the first-order power model may be helpful in predicting the optimal mAs and reducing the radiation dose for chest radiography when using the AEC technique.

Background and Aim: To evaluate and compare magnetic resonance imaging (MRI) sequences that could potentially be used in the diagnosis of coronavirus disease 2019 (COVID-19)., Materials and Methods: Included in the study were 42 patient who underwent thorax computed tomography (CT) for COVID-19 pneumonia and thorax MRI for any reason within 24 h after CT. The T2-weighted fast spin echo periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) (T2W-FSE-P), fast imaging employing steady-state acquisition, T2 fat-saturated FSE, axial T1 liver acquisition with volume acceleration (LAVA) and single-shot FSE images were compared in terms of their ability to show COVID-19 findings., Results: The mean age of the patients was 47.2 ± 24 years. Of the patients, 22 were male (52.4%) and 20 (47.6%) were female. The interobserver intraclass coefficient (ICC) for the image quality score was the highest in the T2W-FSE-P sequence and lowest in the T1 LAVA sequence. All of the lesion-based evaluations of the interobserver agreement were statistically significant, with the kappa value varying between 0.798 and 0.998., Conclusion: All 5 sequences evaluated in the study were successful in showing the parenchymal findings of COVID-19. Since the T2W-FSE-P sequence had the best scores in both interobserver agreement and ICC for the image quality score, it was considered that it can be included in thorax MRI examinations to assist the diagnosis of COVID-19., Competing Interests: Conflict of 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., (© TÜBİTAK.)

A chest radiograph is the most common and the initial radiological investigation for evaluating a child presenting with respiratory complaints. However, performing and interpreting chest radiography optimally requires training and skill. With the relatively easy availability of computed tomography (CT) scanning and more recently multidetector computed tomography (MDCT), these investigations are often performed. Although these can be the cross-sectional imaging modalities of choice in certain situations where detailed and exact anatomical and etiological information is required, both these investigations are associated with increased radiation exposure which has more detrimental effects on children, especially when repeated follow-up imaging is necessary to assess the disease status. Ultrasonography (USG) and magnetic resonance imaging (MRI) have evolved as radiation-free radiological investigations for evaluating the pediatric chest pathologies over the last few years. In the present review article, the utility and the current status, as well as the limitations of USG and MRI for evaluation of pediatric chest pathologies, are discussed. Radiology has grown beyond having just the diagnostic capabilities in managing children with chest disorders in the last two decades. Image-guided therapeutic procedures (percutaneous and endovascular) are routinely performed in children with pathologies in the mediastinum and lungs. The commonly performed image-guided pediatric chest interventions, including biopsies, fine needle aspiration, drainage procedures and therapeutic endovascular procedures, are also discussed in the current review., (© 2023. The Author(s), under exclusive licence to Dr. K C Chaudhuri Foundation.)

The National COVID-19 Chest Imaging Database (NCCID) is a centralized UK database of thoracic imaging and corresponding clinical data. It is made available by the National Health Service Artificial Intelligence (NHS AI) Lab to support the development of machine learning tools focused on Coronavirus Disease 2019 (COVID-19). A bespoke cleaning pipeline for NCCID, developed by the NHSx, was introduced in 2021. We present an extension to the original cleaning pipeline for the clinical data of the database. It has been adjusted to correct additional systematic inconsistencies in the raw data such as patient sex, oxygen levels and date values. The most important changes will be discussed in this paper, whilst the code and further explanations are made publicly available on GitLab. The suggested cleaning will allow global users to work with more consistent data for the development of machine learning tools without being an expert. In addition, it highlights some of the challenges when working with clinical multi-center data and includes recommendations for similar future initiatives., (© 2023. The Author(s).)

The current tool for assessing thoracic asymmetry of thoracic surgery patients is inappropriate for timely or frequent clinical routines due to its dependency on empirical physical examinations or specialized machines. This study investigates the camera-based respiratory imaging for screening thoracic asymmetry, in an intelligent and convenient way. The respiratory heatmaps are generated based on the respiratory magnitudes, phases and angles extracted from the chest video, and bilateral chest region of interest are compared statistically. Due to the variability of chest respiratory direction, spatial enhancement (SDR and SPCA) algorithms are proposed to magnify the respiratory energy. The proposed framework was validated in a clinical trial involving 31 patients, recorded by a smartphone camera. A high correlation was found between the camera measurements and patients' thoracic status in both the visual imaging and quantified indices. The respiratory imaging of camera shows a clear potential for assessing chest abnormalities of thoracic surgery patients.

Background and Objective: Cone-beam computed tomography (CBCT) has become a more and more active cutting-edge topic in the international computed tomography (CT) research due to its advantages of fast scanning speed, high ray utilization rate and high precision. However, scatter artifacts affect the imaging performance of CBCT, which hinders its application seriously. Therefore, our study aimed to propose a novel algorithm for scatter artifacts suppression in thorax CBCT based on a feature fusion residual network (FFRN), where the contextual loss was introduced to adapt the unpaired datasets better., Methods: In the method we proposed, a FFRN with contextual loss was used to reduce CBCT artifacts in the region of chest. Unlike L1 or L2 loss, the contextual loss function makes input images which are not aligned strictly in space available, so we performed it on our unpaired datasets. The algorithm aims to reduce artifacts via studying the mapping between CBCT and CT images, where the CBCT images were set as the beginning while planning CT images as the end., Results: The proposed method effectively removes artifacts in thorax CBCT, including shadow artifacts and cup artifacts which can be collectively referred to as uneven grayscale artifacts, in the CBCT image, and perform well in preserving details and maintaining the original shape. In addition, the average PSNR number of our proposed method achieved 27.7, which was higher than the methods this paper referred which indicated the significance of our method., Conclusions: What is revealed by the results is that our method provides a highly effective, rapid, and robust solution for the removal of scatter artifacts in thorax CBCT images. Moreover, as is shown in Table 1, our method has demonstrated better artifact reduction capability than other methods., (© 2023 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

Injuries to the chest and thorax are rare, but when they occur, they can be life-threatening. It is important to have a high index of suspicion to be able to make these diagnoses when evaluating a patient with a chest injury. Often, sideline management is limited and immediate transport to a hospital is indicated., (Published by Elsevier Inc.)

Anthropomorphic phantoms with tissue equivalency are required in radiotherapy for quality assurance of imaging and dosimetric processes used in radiotherapy treatments. Commercial phantoms are expensive and provide limited approximation to patient geometry and tissue equivalency. In this study, a 5 cm thick anthropomorphic thoracic slab phantom was designed and 3D printed using models exported from a CT dataset to demonstrate the feasibility of manufacturing anthropomorphic 3D printed phantoms onsite in a clinical radiotherapy department. The 3D printed phantom was manufactured with polylactic acid with an in-fill density of 80% to simulate tissue density and 26% to simulate lung density. A common radio-opacifier, barium sulfate (BaSO 4 ), was added 6% w/w to an epoxy resin mixture to simulate similar HU numbers for bone equivalency. A half-cylindrical shape was cropped away from the spine region to allow insertion of the bone equivalent mixture. Two Gafchromic™ EBT3 film strips were inserted into the 3D printed phantom to measure the delivery of two stereotactic radiotherapy plans targeting lung and bone lesions respectively. Results were analysed within SNC Patient with a low dose threshold of 10% and a gamma criterion of 3%/2 mm and 5%/1 mm. The resulting gamma pass rate across both criterions for lung and bone were ≥ 95% and approximately 85% respectively. Results shows that a cost-effective anthropomorphic 3D printed phantom with realistic heterogeneity simulation can be fabricated in departments with access a suitable 3D printer, which can be used for performing commissioning and quality assurance for stereotactic type radiotherapy to lesions in the presence of heterogeneity., (© 2023. Australasian College of Physical Scientists and Engineers in Medicine.)

Background: Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x-ray images. Although extensive efforts have been put into this field, there are still many challenges., Purpose: Most of the existing fine-grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high-level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine-grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine-grained medical images still has the problem of insufficient sample data with accurate annotation information., Methods: To address above challenges, we introduced a novel fine-grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x-ray datasets., Results: Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26 % ∼ 4.6 % $\%{\sim }4.6\%$ and achieves a very efficient training speed which is 100 times faster than the pair-wise loss-based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrieval performance., Conclusions: The proposed model is capable of learning discriminative representations from chest x-ray datasets, and it achieves better performance compared with other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chest disease., (© 2022 American Association of Physicists in Medicine.)

Background: This study was conducted to alleviate a common difficulty in chest X-ray image diagnosis: The attention region in a convolutional neural network (CNN) does not often match the doctor's point of focus. The method presented herein, which guides the area of attention in CNN to a medically plausible region, can thereby improve diagnostic capabilities., Methods: The model is based on an attention branch network, which has excellent interpretability of the classification model. This model has an additional new operation branch that guides the attention region to the lung field and heart in chest X-ray images. We also used three chest X-ray image datasets (Teikyo, Tokushima, and ChestX-ray14) to evaluate the CNN attention area of interest in these fields. Additionally, after devising a quantitative method of evaluating improvement of a CNN's region of interest, we applied it to evaluation of the proposed model., Results: Operation branch networks maintain or improve the area under the curve to a greater degree than conventional CNNs do. Furthermore, the network better emphasizes reasonable anatomical parts in chest X-ray images., Conclusions: The proposed network better emphasizes the reasonable anatomical parts in chest X-ray images. This method can enhance capabilities for image interpretation based on judgment., (© 2023. The Author(s).)

Background: Currently, 4.8% of bariatric operations worldwide are one-anastomosis gastric bypass (OAGB) procedures. If a hiatal hernia is detected in a preoperative gastroscopy, OAGB can be combined with hiatoplasty. Intrathoracic pouch migration (ITM) is common after bypass procedures because the fundus, a natural abutment, is separated from the pouch., Objective: The aim of this study was to find out whether OAGB or OAGB combined with hiatoplasty carries a higher risk of ITM and, therefore, also gastroesophageal reflux disease (GERD)., Setting: University hospital., Methods: Fifty patients (group 1: 25× primary OAGB; group 2: 25× primary OAGB with hiatoplasty) were included in this study. History of weight, GERD, and quality of life were recorded in patient interviews and pouch volume and ITM were evaluated using 3-dimensional-computed tomography volumetry., Results: There were no differences in terms of patient characteristics, history of weight, pouch volume, or quality of life between both groups. ITM was found in group 1 in 60% (n = 15) and group 2 in 76% (n = 19) of all patients (P = .152). The ITM mean length was significantly lower in group 1 with .9 ± 1.1 cm than in group 2 with 1.8 ± 1.2 cm (P = .007). Regarding GERD, there was no difference between both groups; nevertheless, significantly more patients with ITM (38.2%; n = 13) had GERD compared with patients without ITM (6.3%; n = 1)., Conclusion: In primary OAGB, an additional hiatoplasty was not associated with higher rates of ITM or GERD; nevertheless, the length of ITM was higher after hiatoplasty. If ITM occurs, patients have a risk of developing GERD., (Copyright © 2023. Published by Elsevier Inc.)

Introduction: Quantitative thorax Computed Tomography (CT) is used to determine the severity of COVID-19 pneumonia. With a new approach, quantitative thoracic CT is to contribute to the triage of patients with severe COVID-19 pneumonia in the ICU and to evaluate its relation with mortality by taking into account the vaccination status., Methods: Fifty-six patients who had a diagnosis of COVID-19 pneumonia confirmed in the adult ICU were evaluated retrospectively. To evaluate the degree of parenchymal involvement, the quantitative CT "craniocaudal diameter of the thorax/craniocaudal largest lesion diameter (CCDT/CCDL)" ratio and semi-quantitative total CT severity scores (TCTSS) (0:0%; 1:1-25%; 2:26-50%; 3:51-75% and 4:76-100%) were calculated. Both methods were analyzed with comparative ROC curves for predicting mortality. The effects of vaccines on thorax CT findings and laboratory parameters were also investigated., Results: The sensitivities and specificities were found to be 72.5%, 75.61%, and 80%, 73.33% when CCDT/CCDL and TCTSS cutoff value was taken <1.4, and >9, respectively, to predict mortality in COVID-19 pneumonia (Area Under the Curve = AUC = 0.797 and 0.752). Both methods predicted mortality well and no statistical differences were detected between them (p = 0.3618). In vaccinated patients, CRP was higher (p = 0.045), and LDH and ferritin were lower (p = 0.049, p = 0.004). The number of lobes involved was lower in the vaccinated group (p = 0.001)., Conclusions: The quantitative CT score (CCDT/CCDL) may play as important a role as TCTSS in diagnosing COVID-19 pneumonia, determining the severity of the disease, and predicting the related mortality. COVID-19 vaccines may affect laboratory parameters and cause less pneumonia on thoracic CT than in unvaccinated individuals., Competing Interests: Declaration of competing interest The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (Copyright © 2023 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd. All rights reserved.)

Purpose: To demonstrate free-breathing thoracic MRI with a minimal-TR balanced steady-state free precession (bSSFP) technique using wobbling Archimedean spiral pole (WASP) trajectories., Methods: Phantom and free-breathing in vivo chest imaging in healthy volunteers was performed at 1.5T with a half-radial, dual-echo, bSSFP sequence, termed bSTAR. For maximum sampling efficiency, a single analog-to-digital converter window along the full bipolar readout was used. To ensure a homogeneous coverage of the k-space over multiple breathing cycles, radial k-space sampling followed short-duration Archimedean spiral interleaves that were randomly titled by a small polar angle and rotated by a golden angle about the polar axis; depticting a wobbling Archimedean spiral pole (WASP) trajectory. In phantom and in vivo experiments, WASP trajectories were compared to spiral phyllotaxis sampling in terms of eddy currents and were used to generate in vivo thorax images at different respiratory phases., Results: WASP trajectories provided artifact-free bSTAR imaging in both phantom and in vivo and respiratory self-gated reconstruction was successfully performed in all subjects. The amount of the acquired data allowed the reconstruction of 10 volumes at different respiratory levels with isotropic resolution of 1.77mm from a scan of 5.5minutes (using a TR of 1.32ms), and one high-resolution 1.16mm end-expiratory volume from a scan of 4.7minutes (using a TR of 1.42ms). The very short TR of bSTAR mitigated off-resonance artifacts despite the large field-of-view., Conclusion: We have demonstrated the feasibility of high-resolution free-breathing thoracic imaging with bSTAR using the wobbling Archimedean spiral pole in healthy subjects at 1.5T., (Copyright © 2022. Published by Elsevier GmbH.)

Purpose: We evaluate an SGRT device (Voxelan HEV-600 M/RMS) installed with Radixact, with the view angle of the Voxelan's camera at 74 degrees. The accuracy of Voxelan with this steep angle was evaluated with phantom experiments and inter-fractional setup errors of patients., Methods: In the phantom experiments, the difference between the measured values of Voxelan from the truth was evaluated for translations and rotations. The inter-fractional setup error between the setup using skin markers with laser localizer (laser setup: LS) and the setup using Voxelan (surface setup: SS) was compared for head and neck (N = 19), chest (N = 7) and pelvis (N = 9) cases. The inter-fractional setup error was calculated by subtracting from bone matching by megavoltage computed tomography (MVCT) as ground truth., Results: From the phantom experiments, the average difference between the measured values of Voxelan from the truth was within 1 mm and 1 degree. In all cases, inter-fractional setup error based on MVCT was not significantly different between LS and SS by Welch's t-test (P > 0.05). The vector offset of the LS for head and neck, chest, and pelvis were 6.5, 9.6, and 9.6 mm, respectively, and that of the SS were 5.8, 8.6, and 12.6 mm, respectively. Slight improvement was observed for the head and neck, and chest cases, however, pelvis cases were not improved because the umbilical region could not be clearly visualized as a reference., Conclusion: The results show that SS in Voxelan with an installation angle of 74 degrees is equal to or better than LS., (© 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine.)

The goal of this work was to build an anthropomorphic thorax phantom capable of breathing motion with materials mimicking human tissues in x-ray imaging applications. The thorax phantom, named Casper, was composed of resin (body), foam (lungs), glow polyactic acid (bones) and natural polyactic acid (tumours placed in the lungs). X-ray attenuation properties of all materials prior to manufacturing were evaluated by means of photon-counting computed tomography (CT) imaging on a table-top system. Breathing motion was achieved by a scotch-yoke mechanism with diaphragm motion frequencies of 10-20 rpm and displacements of 1 to 2 cm. Casper was manufactured by means of 3D printing of moulds and ribs and assembled in a complex process. The final phantom was then scanned using a clinical CT scanner to evaluate material CT numbers and the extent of tumour motion. Casper CT numbers were close to human CT numbers for soft tissue (46 HU), ribs (125 HU), lungs (-840 HU) and tumours (-45 HU). For a 2 cm diaphragm displacement the largest tumour displacement was 0.7 cm. The five tumour volumes were accurately assessed in the static CT images with a mean absolute error of 4.3%. Tumour sizes were either underestimated for smaller tumours or overestimated for larger tumours in dynamic CT images due to motion blurring with a mean absolute difference from true volumes of 10.3%. More Casper information including a motion movie and manufacturing data can be downloaded from http://web.uvic.ca/~bazalova/Casper/., (Creative Commons Attribution license.)

Anatomical segmentation is a fundamental task in medical image computing, generally tackled with fully convolutional neural networks which produce dense segmentation masks. These models are often trained with loss functions such as cross-entropy or Dice, which assume pixels to be independent of each other, thus ignoring topological errors and anatomical inconsistencies. We address this limitation by moving from pixel-level to graph representations, which allow to naturally incorporate anatomical constraints by construction. To this end, we introduce HybridGNet, an encoder-decoder neural architecture that leverages standard convolutions for image feature encoding and graph convolutional neural networks (GCNNs) to decode plausible representations of anatomical structures. We also propose a novel image-to-graph skip connection layer which allows localized features to flow from standard convolutional blocks to GCNN blocks, and show that it improves segmentation accuracy. The proposed architecture is extensively evaluated in a variety of domain shift and image occlusion scenarios, and audited considering different types of demographic domain shift. Our comprehensive experimental setup compares HybridGNet with other landmark and pixel-based models for anatomical segmentation in chest x-ray images, and shows that it produces anatomically plausible results in challenging scenarios where other models tend to fail.

Accurate and rapid detection of COVID-19 pneumonia is crucial for optimal patient treatment. Chest X-Ray (CXR) is the first-line imaging technique for COVID-19 pneumonia diagnosis as it is fast, cheap and easily accessible. Currently, many deep learning (DL) models have been proposed to detect COVID-19 pneumonia from CXR images. Unfortunately, these deep classifiers lack the transparency in interpreting findings, which may limit their applications in clinical practice. The existing explanation methods produce either too noisy or imprecise results, and hence are unsuitable for diagnostic purposes. In this work, we propose a novel explainable CXR deep neural Network (CXR-Net) for accurate COVID-19 pneumonia detection with an enhanced pixel-level visual explanation using CXR images. An Encoder-Decoder-Encoder architecture is proposed, in which an extra encoder is added after the encoder-decoder structure to ensure the model can be trained on category samples. The method has been evaluated on real world CXR datasets from both public and private sources, including healthy, bacterial pneumonia, viral pneumonia and COVID-19 pneumonia cases. The results demonstrate that the proposed method can achieve a satisfactory accuracy and provide fine-resolution activation maps for visual explanation in the lung disease detection. Compared to current state-of-the-art visual explanation methods, the proposed method can provide more detailed, high-resolution, visual explanation for the classification results. It can be deployed in various computing environments, including cloud, CPU and GPU environments. It has a great potential to be used in clinical practice for COVID-19 pneumonia diagnosis.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Khan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Automated multi-organ segmentation plays an essential part in the computer-aided diagnostic (CAD) of chest X-ray fluoroscopy. However, developing a CAD system for the anatomical structure segmentation remains challenging due to several indistinct structures, variations in the anatomical structure shape among different individuals, the presence of medical tools, such as pacemakers and catheters, and various artifacts in the chest radiographic images. In this paper, we propose a robust deep learning segmentation framework for the anatomical structure in chest radiographs that utilizes a dual encoder-decoder convolutional neural network (CNN). The first network in the dual encoder-decoder structure effectively utilizes a pre-trained VGG19 as an encoder for the segmentation task. The pre-trained encoder output is fed into the squeeze-and-excitation (SE) to boost the network's representation power, which enables it to perform dynamic channel-wise feature calibrations. The calibrated features are efficiently passed into the first decoder to generate the mask. We integrated the generated mask with the input image and passed it through a second encoder-decoder network with the recurrent residual blocks and an attention the gate module to capture the additional contextual features and improve the segmentation of the smaller regions. Three public chest X-ray datasets are used to evaluate the proposed method for multi-organs segmentation, such as the heart, lungs, and clavicles, and single-organ segmentation, which include only lungs. The results from the experiment show that our proposed technique outperformed the existing multi-class and single-class segmentation methods., (© 2023. The Author(s).)

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL&#95;Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL&#95;Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL&#95;Net model is also compared with the default FL configurations. The proposed DMFL&#95;Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

Pregnant women and children sometimes had to undergo chest computed tomography (CT) scans during the Corona Virus Disease 2019 (COVID-19) pandemic. This study estimated the fetal and pediatric doses from chest CT scans. Organ doses and effective doses were calculated using the VirtualDose-CT software. Two groups of computational human phantoms, pregnant females and pediatric patients were used in this study. The results of doses normalized to volumetric CT Dose Index (CTDIvol) can be used universally for other dosimetry studies. Based on our calculations and international survey data of CTDIvol, fetal absorbed doses from COVID-19-related chest CT were found to be 0.04-0.36, 0.05-0.44 and 0.07-0.61 mGy for 3, 6 and 9 months of pregnancy, respectively. When the scan range is extended to the abdominal region, fetal doses increase by almost 4-fold. Effective doses for COVID-19-related chest CT were 1.62-13.77, 1.58-13.46, 1.57-13.33 and 1.29-10.98 mSv for the newborn, 1-, 5- and 10-y-old children, respectively. In addition, the effects of specific axial scan ranges exceeding the thorax region were evaluated. Although doses from chest CT scans are small, such data allow radiologists and patients to be informed of the dose levels and ways to avoid unnecessary radiation., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Background: Quantitative regional assessment of thoracic function would enable clinicians to better understand the regional effects of therapy and the degree of deviation from normality in patients with thoracic insufficiency syndrome (TIS). The purpose of this study was to determine the regional functional effects of surgical treatment in TIS via quantitative dynamic magnetic resonance imaging (MRI) in comparison with healthy children., Methods: Volumetric parameters were derived via 129 dynamic MRI scans from 51 normal children (November 2017 to March 2019) and 39 patients with TIS (preoperatively and postoperatively, July 2009 to May 2018) for the left and right lungs, the left and right hemi-diaphragms, and the left and right hemi-chest walls during tidal breathing. Paired t testing was performed to compare the parameters from patients with TIS preoperatively and postoperatively. Mahalanobis distances between parameters of patients with TIS and age-matched normal children were assessed to evaluate the closeness of patient lung function to normality. Linear regression functions were utilized to estimate volume deviations of patients with TIS from normality, taking into account the growth of the subjects., Results: The mean Mahalanobis distances for the right hemi-diaphragm tidal volume (RDtv) were -1.32 ± 1.04 preoperatively and -0.05 ± 1.11 postoperatively (p = 0.001). Similarly, the mean Mahalanobis distances for the right lung tidal volume (RLtv) were -1.12 ± 1.04 preoperatively and -0.10 ± 1.26 postoperatively (p = 0.01). The mean Mahalanobis distances for the ratio of bilateral hemi-diaphragm tidal volume to bilateral lung tidal volume (BDtv/BLtv) were -1.68 ± 1.21 preoperatively and -0.04 ± 1.10 postoperatively (p = 0.003). Mahalanobis distances decreased after treatment, suggesting reduced deviations from normality. Regression results showed that all volumes and tidal volumes significantly increased after treatment (p < 0.001), and the tidal volume increases were significantly greater than those expected from normal growth for RDtv, RLtv, BDtv, and BLtv (p < 0.05)., Conclusions: Postoperative tidal volumes of bilateral lungs and bilateral hemi-diaphragms of patients with TIS came closer to those of normal children, indicating positive treatment effects from the surgical procedure. Quantitative dynamic MRI facilitates the assessment of regional effects of a surgical procedure to treat TIS., Level of Evidence: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence., Competing Interests: Disclosure: The Disclosure of Potential Conflicts of Interest forms are provided with the online version of the article (http://links.lww.com/JBJS/H325)., (Copyright © 2022 by The Journal of Bone and Joint Surgery, Incorporated.)

Oncology patients can present with acute, life-threatening conditions that may arise either due to underlying malignancy or secondary to cancer therapy. Select oncologic emergencies show characteristic imaging findings on radiographs, ultrasound, computed tomography, and MRI that helps in timely diagnosis. Radiologists need to be aware of typical imaging findings in such patients in an emergency setting and should be able to guide the clinicians for proper patient management. Appropriate knowledge of the treatment and its timing is pivotal in diagnosing treatment-related complications., (Copyright © 2022 Elsevier Inc. All rights reserved.)

This study has demonstrated the changing volume of both the anterior and posterior thorax in normal adolescents (without spinal or thoracic deformity), differentiating for both sex and age, to further understand how the thorax grows, along with the differences in growth between the anterior and posterior thorax. The thorax was measured on axial CT slices at every vertebral level from T3 to T12 in a series of scans previous taken for routine clinical care. Measurements taken were the anteroposterior thoracic distance and the area of the anterior and posterior rib prominences on either side of the thorax. Data was analyzed per vertebral level, differentiating for age and sex. There were 486 CT scans analyzed (257 males and 229 females) between the ages of 8 and 18 years. The analysis identified that for the anterior thorax, there are three phases of growth with an initial slow increase in volume, followed by a stabilization of little growth, followed by another phase of a more rapid increase in volume. For the posterior thorax, there was a gradual increase in area with increasing age. This study demonstrates that the shape of the thorax is age and sex dependent, with males having both a greater width and depth of thorax compared to females. Of particular note is the difference in patterns of growth between the anterior and posterior thorax. This information will add to the understanding of normal growth, which will aid in the management of conditions where that growth is disturbed., (© 2022 American Association of Clinical Anatomists and British Association of Clinical Anatomists.)

Purpose: To compare lung ultrasound (US) and computed tomography (CT) in the assessment of pregnant women with COVID-19., Methods: Prospective study comprising 39 pregnant inpatients with COVID-19 who underwent pulmonary assessment with CT and US with a maximum span of 48 h between the exams. The thorax was divided into 12 regions and assessed in terms of the following: the presence of B-lines (>2), coalescent B-lines, consolidation on US; presence of interlobular thickening, ground glass, consolidation on CT. The two methods were scored by adding up the scores from each thoracic region., Results: A significant correlation was found between the scores obtained by the two methods (rICC = 0.946; p < 0.001). They were moderately in agreement concerning the frequency of altered pulmonary regions (weighted kappa = 0.551). In US, a score over 15, coalescent B-lines, and consolidation were predictors of the need for oxygen, whereas the predictors in CT were a lung score over 16 and consolidation. The two methods, US (p < 0.001; AUC = 0.915) and CT (p < 0.001; AUC = 0.938), were fairly accurate in predicting the need for oxygen., Conclusion: In pregnant women, lung US and chest CT are of similar accuracy in assessing lungs affected by COVID-19 and can predict the need for oxygen., (© 2022 Wiley Periodicals LLC.)

Rapid developments and increasing technological knowledge have changed perspectives on ultrasonography. Previously, ultrasonography was used to evaluate chest wall pathologies, to distinguish between pleural effusion and consolidation, to evacuate pleural effusion, or to evaluate diaphragm movement. Today, it is also used in a wide range of pleural and parenchymal diseases. Ultrasonography is not just used in the emergency department and the intensive care units, it is also utilized in many clinical branches dealing with the respiratory system-due to its ease of use, fast access, price advantage, non-radiation exposure, higher diagnostic sensitivity, and specificity in many clinical situations-and has become a part of the examination. In this review, we have assessed not just transthoracic ultrasonography but also more focal and targeted sonographic applications, such as the endobronchial ultrasound and esophageal ultrasound., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Immediate verification of whether a patient being examined is correct is desirable, even if the scan ranges change during different examinations for the same patient. This study proposes an advanced biological fingerprint technique for the rapid and reliable verification of various scan ranges in computed tomography (CT) scans of the torso of the same patient. The method comprises the following steps: geometric correction of different scans, local feature extraction, mismatch elimination, and similarity evaluation. The geometric magnification correction was aligned at the scanner table height in the first two steps, and the local maxima were calculated as the local features. In the third step, local features from the follow-up scout image are matched to those in the corresponding baseline scout image via template matching and outlier elimination via a robust estimator. We evaluated the correspondence rate based on the inlier ratio between corresponding scout images. The ratio of inliers between the baseline and follow-up scout images was assessed as the similarity score. The clinical dataset, including chest, abdomen-pelvis, and chest-abdomen-pelvis scans, included 600 patients (372 men, 68 ± 12 years) who underwent two routine torso CT examinations. The highest area under the receiver operating characteristic curve (AUC) was 0.996, which was sufficient for patient verification. Moreover, the verification results were comparable to the conventional method, which uses scout images in the same scan range. Patient identity verification was achieved before the main scan, even in follow-up torso CT, under different scan ranges., (© 2022. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.)

Objective: To determine the incremental yield of standardized addition of chest CT to abdominal CT to detect COVID-19 in patients presenting with primarily acute gastrointestinal symptoms requiring abdominal imaging. Summary Background Data: Around 20% of patients with COVID-19 present with gastrointestinal symptoms. COVID-19 might be neglected in these patients, as the focus could be on finding abdominal pathology. During the COVID-19 pandemic, several centers have routinely added chest CT to abdominal CT to detect possible COVID-19 in patients presenting with gastrointestinal symptoms. However, the incremental yield of this strategy is unknown., Methods: This multicenter study in 6 Dutch centers included consecutive adult patients presenting with acute nontraumatic gastrointestinal symptoms, who underwent standardized combined abdominal and chest CT between March 15, 2020 and April 30, 2020. All CT scans were read for signs of COVID-19 related pulmonary sequelae using the СО-RADS score. The primary outcome was the yield of high COVID-19 suspicion (СО-RADS 4-5) based on chest CT., Results: A total of 392 patients were included. Radiologic suspicion for COVID-19 (СО-RADS 4-5) was present in 17 (4.3%) patients, eleven of which were diagnosed with COVID-19. Only 5 patients with СО-RADS 4-5 presented without any respiratory symptoms and were diagnosed with COVID-19. No relation with community prevalence could be detected., Conclusion: The yield of adding chest CT to abdominal CT to detect COVID-19 in patients presenting with acute gastrointestinal symptoms is extremely low with an additional detection rate of around 1%., Competing Interests: The authors report no conflicts of interest., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

Automated chest X-ray analysis has a great potential for diagnosing thorax diseases since errors in diagnosis have always been a concern among radiologists. Being a multi-label classification problem, achieving accurate classification remains challenging. Several studies have focused on accurately segmenting the lung regions from the chest X-rays to deal with the challenges involved. The features extracted from the lung regions typically provide precise clues for diseases like nodules. However, such methods ignore the features outside the lung regions, which have been shown to be crucial for diagnosing conditions like cardiomegaly. Therefore, in this work, we explore a dual-branch network-based framework that relies on features extracted from the lung regions as well as the entire chest X-rays. The proposed framework uses a novel network named R-I UNet for segmenting the lung regions. The dual-branch network in the proposed framework employs two pre-trained AlexNet models to extract discriminative features, forming two feature vectors. Each feature vector is fed into a recurrent neural network consisting of a stack of gated recurrent units with skip connections. Finally, the resulting feature vectors are concatenated for classification. The proposed models achieve state-of-the-art performance for both segmentation and classification tasks on the benchmark datasets. Specifically, our lung segmentation model achieves a 5-fold cross-validation accuracy of 98.18 % and 99.14 % on Montgomery (MC) and JSRT datasets. For classification, the proposed approach achieves state-of-the-art AUC for 9 out of 14 diseases with a mean AUC of 0.842 on the NIH ChestXray14 dataset.

Since two years ago, the COVID-19 virus has spread strongly in the world and has killed more than 6 million people directly and has affected the lives of more than 500 million people. Early diagnosis of the virus can help to break the chain of transmission and reduce the death rate. In most cases, the virus spreads in the infected person's chest. Therefore, the analysis of a chest CT scan is one of the most efficient methods for diagnosing a patient. Until now, various methods have been presented to diagnose COVID-19 disease in chest CT-scan images. Most recent studies have proposed deep learning-based methods. But handcrafted features provide acceptable results in some studies too. In this paper, an innovative approach is proposed based on the combination of low-level and deep features. First of all, local neighborhood difference patterns are performed to extract handcrafted texture features. Next, deep features are extracted using MobileNetV2. Finally, a two-level decision-making algorithm is performed to improve the detection rate especially when the proposed decisions based on the two different feature set are not the same. The proposed approach is evaluated on a collected dataset of chest CT scan images from June 1, 2021, to December 20, 2021, of 238 cases in two groups of patient and healthy in different COVID-19 variants. The results show that the combination of texture and deep features can provide better performance than using each feature set separately. Results demonstrate that the proposed approach provides higher accuracy in comparison with some state-of-the-art methods in this scope., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Azita Yazdani et al.)