Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries.

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Evaluating GPT-V4 (GPT-4 with Vision) on Detection of Radiologic Findings on Chest Radiographs.

Background Generating radiologic findings from chest radiographs is pivotal in medical image analysis. The emergence of OpenAI's generative pretrained transformer, GPT-4 with vision (GPT-4V), has opened new perspectives on the potential for automated image-text pair generation. However, the application of GPT-4V to real-world chest radiography is yet to be thoroughly examined. Purpose To investigate the capability of GPT-4V to generate radiologic findings from real-world chest radiographs. Materials and Methods In this retrospective study, 100 chest radiographs with free-text radiology reports were annotated by a cohort of radiologists, two attending physicians and three residents, to establish a reference standard. Of 100 chest radiographs, 50 were randomly selected from the National Institutes of Health (NIH) chest radiographic data set, and 50 were randomly selected from the Medical Imaging and Data Resource Center (MIDRC). The performance of GPT-4V at detecting imaging findings from each chest radiograph was assessed in the zero-shot setting (where it operates without prior examples) and few-shot setting (where it operates with two examples). Its outcomes were compared with the reference standard with regards to clinical conditions and their corresponding codes in the International Statistical Classification of Diseases, Tenth Revision (ICD-10), including the anatomic location (hereafter, laterality). Results In the zero-shot setting, in the task of detecting ICD-10 codes alone, GPT-4V attained an average positive predictive value (PPV) of 12.3%, average true-positive rate (TPR) of 5.8%, and average F1 score of 7.3% on the NIH data set, and an average PPV of 25.0%, average TPR of 16.8%, and average F1 score of 18.2% on the MIDRC data set. When both the ICD-10 codes and their corresponding laterality were considered, GPT-4V produced an average PPV of 7.8%, average TPR of 3.5%, and average F1 score of 4.5% on the NIH data set, and an average PPV of 10.9%, average TPR of 4.9%, and average F1 score of 6.4% on the MIDRC data set. With few-shot learning, GPT-4V showed improved performance on both data sets. When contrasting zero-shot and few-shot learning, there were improved average TPRs and F1 scores in the few-shot setting, but there was not a substantial increase in the average PPV. Conclusion Although GPT-4V has shown promise in understanding natural images, it had limited effectiveness in interpreting real-world chest radiographs. © RSNA, 2024 Supplemental material is available for this article.

Quantitative analysis of lung lesions using unenhanced chest computed tomography images.

INTRODUCTION: Chest radiograph and computed tomography (CT) scans can accidentally reveal pulmonary nodules. Malignant and benign pulmonary nodules can be difficult to distinguish without specific imaging features, such as calcification, necrosis, and contrast enhancement. However, these lesions may exhibit different image texture characteristics which cannot be assessed visually. Thus, a computer-assisted quantitative method like histogram analysis (HA) of Hounsfield unit (HU) values can improve diagnostic accuracy, reducing the need for invasive biopsy. METHODS: In this exploratory control study, nonenhanced chest CT images of 20 patients with benign (10) and cancerous (10) lesion were selected retrospectively. The appearances of benign and malignant lesions were very similar in chest CT images, and only pathology report was used to discriminate them. Free hand region of interest (ROI) was inserted inside the lesion for all slices of each lesion. Mean, minimum, maximum, and standard deviations of HU values were recorded and used to make HA. RESULTS: HA showed that the most malignant lesions have a mean HU value between 30 and 50, a maximum HU less than 150, and a minimum HU between -30 and 20. Lesions outside these ranges were mostly benign. CONCLUSION: Quantitative CT analysis may differentiate malignant from benign lesions without specific malignancy patterns on unenhanced chest CT image.

DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing the progression of this incurable disease. It is also a challenging task due to large variations in appearance, size and location of lesions in the lung regions as well as inter-class similarity and intra-class variance. Compared to traditional methods, Convolutional Neural Networks-based methods have shown improved results; however, these methods are still not applicable in clinical practice due to limited performance. In some cases, limited computing resources make it impractical to develop a model using whole CXR images. To address this problem, the lung fields are divided into six zones, each zone is classified separately and the zone classification results are then aggregated into an image classification score, based on state-of-the-art. In this study, we propose a dual lesion attention network (DLA-Net) for the classification of pneumoconiosis that can extract features from affected regions in a lung. This network consists of two main components: feature extraction and feature refinement. Feature extraction uses the pre-trained Xception model as the backbone to extract semantic information. To emphasise the lesion regions and improve the feature representation capability, the feature refinement component uses a DLA module that consists of two sub modules: channel attention (CA) and spatial attention (SA). The CA module focuses on the most important channels in the feature maps extracted by the backbone model, and the SA module highlights the spatial details of the affected regions. Thus, both attention modules combine to extract discriminative and rich contextual features to improve classification performance on pneumoconiosis. Experimental results show that the proposed DLA-Net outperforms state-of-the-art methods for pneumoconiosis classification.

COVID­19 detection from chest X-ray images using transfer learning.

COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 is based on swabs from the throat and nose, their sensitivity is not high enough and so they are prone to errors. Early diagnosis of COVID-19 disease is important to provide the chance of quick isolation of the suspected cases and to decrease the opportunity of infection in healthy people. In this research, a framework for chest X-ray image classification tasks based on deep learning is proposed to help in early diagnosis of COVID-19. The proposed framework contains two phases which are the pre-processing phase and classification phase which uses pre-trained convolution neural network models based on transfer learning. In the pre-processing phase, different image enhancements have been applied to full and segmented X-ray images to improve the classification performance of the CNN models. Two CNN pre-trained models have been used for classification which are VGG19 and EfficientNetB0. From experimental results, the best model achieved a sensitivity of 0.96, specificity of 0.94, precision of 0.9412, F1 score of 0.9505 and accuracy of 0.95 using enhanced full X-ray images for binary classification of chest X-ray images into COVID-19 or normal with VGG19. The proposed framework is promising and achieved a classification accuracy of 0.935 for 4-class classification.

Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

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.

Potential of photon counting computed tomography derived spectral reconstructions to reduce beam-hardening artifacts in chest CT.

PURPOSE: Aim of the recent study is to point out a method to optimize quality of CT scans in oncological patients with port systems. This study investigates the potential of photon counting computed tomography (PCCT) for reduction of beam hardening artifacts caused by port-implants in chest imaging by means of spectral reconstructions. METHOD: In this retrospective single-center study, 8 ROIs for 19 spectral reconstructions (polyenergetic imaging, monoenergetic reconstructions from 40 to 190 keV as well as iodine maps and virtual non contrast (VNC)) of 49 patients with pectoral port systems undergoing PCCT of the chest for staging of oncologic disease were measured. Mean values and standard deviation (SD) Hounsfield unit measurements of port-chamber associated hypo- and hyperdense artifacts, bilateral muscles and vessels has been carried out. Also, a structured assessment of artifacts and imaging findings was performed by two radiologists. RESULTS: A significant association of keV with iodine contrast as well as artifact intensity was noted (all p < 0.001). In qualitative assessment, utilization of 120 keV monoenergetic reconstructions could reduce severe and pronounced artifacts completely, as compared to lower keV reconstructions (p < 0.001). Regarding imaging findings, no significant difference between monoenergetic reconstructions was noted (all p > 0.05). In cases with very high iodine concentrations in the subclavian vein, image distortions were noted at 40 keV images (p < 0.01). CONCLUSIONS: The present study demonstrates that PCCT derived spectral reconstructions can be used in oncological imaging of the thorax to reduce port-derived beam-hardening artefacts. When evaluating image data sets within a staging, it can be particularly helpful to consider the 120 keV VMIs, in which the artefacts are comparatively low.

Size specific dose estimation in pediatric CT: preliminary study and conversion factors.

The objective of this paper is to compare the differences between volumetric CT dose index (CTDIVOL) and size-specific dose estimate (SSDEWED) based on water equivalent diameter (WED) in radiation dose measurement, and explore a new method for fast calculation of SSDEWED. The imaging data of 1238 cases of head, 1152 cases of chest and 976 cases of abdominopelvic were analyzed retrospectively, and they were divided into five age groups: ≤ 0.5, 0.5 ~ ≤ 1, 1 ~ ≤ 5, 5 ~ ≤ 10 and 10 ~ ≤ 15 years according to age. The area of interest (AR), CT value (CTR), lateral diameter (LAT) and anteroposterior diameter (AP) of the median cross-sectional image of the standard scanning range and the SSDEWED were manually calculated, and a t-test was used to compare the differences between CTDIVOL and SSDEWED in different age groups. Pearson analyzed the correlations between DE and age, DE and WED, f and age, and counted the means of conversion factors in each age group, and analyze the error ratios between SSDE calculated based on the mean age group conversion factors and actual measured SSDE. The CTDIVOL in head was (9.41 ± 1.42) mGy and the SSDEWED was (8.25 ± 0.70) mGy: the difference was statistically significant (t = 55.04, P < 0.001); the CTDIVOL of chest was (2.68 ± 0.91) mGy and the SSDEWED was (5.16 ± 1.16) mGy, with a statistically significant difference (t = -218.78, P < 0.001); the CTDIVOL of abdominopelvic was (3.09 ± 1.58) mGy and the SSDEWED was (5.89 ± 2.19) mGy: the difference was also statistically significant (t = -112.28, P < 0.001). The CTDIVOL was larger than the SSDEWED in the head except for the ≤ 0.5 year subgroup, and CTDIVOL was smaller than SSDEWED within each subgroup in chest and abdominopelvic. There were strong negative correlations between f and age (head: r = -0.81; chest: r = -0.89; abdominopelvic: r = -0.86; P < 0.001). The mean values of f at each examination region were 0.81 ~ 1.01 for head, 1.65 ~ 2.34 for chest and 1.71 ~ 2.35 for abdominopelvic region. The SSDEWED could be accurately estimated using the mean f of each age subgroup. SSDEWED can more accurately measure the radiation dose of children. For children of different ages and examination regions, the SSDEWED conversion factors based on age subgroup can be quickly adjusted and improve the accuracy of radiation dose estimation.

Reducing unwarranted chest x-rays in bronchiolitis: Importance of a robust analysis.

AIM: Bronchiolitis is the commonest reason for hospitalisation amongst infants and is often a target for low-value care (LVC) reduction. We aimed to assess the impact of a multifaceted intervention (clinician education, parent engagement, audit-feedback) on rates of chest x-rays (CXR) in bronchiolitis. METHODS: Longitudinal study of CXRs ordered in infants (1-12 months) diagnosed with bronchiolitis in the Emergency Department (ED) of an Australian paediatric hospital between May 2016 and February 2023. We used logistic regression to measure the impact of the intervention on unwarranted CXR orders, controlling for other potential impacting variables such as time, patient characteristics (age/sex), clinical variables (fever, hypoxia, tachypnoea), seasonal factors (month, day of the week, business hours) and time passed since intervention. RESULTS: Ten thousand one hundred and nine infants were diagnosed with bronchiolitis in the ED over the study period, with 939 (9.3%) receiving a CXR, of which 69% (n = 651) were considered unwarranted. Rates of unwarranted CXRs reduced from 7.9% to 5.4% post-intervention (P < 0.0001). Logistic regression showed the intervention had no significant effect (OR 0.89, 95% CI 0.65-1.23) once other variables and underlying time-based trends were accounted for. CONCLUSIONS: Although pre-post rates appeared significantly improved, a robust analysis demonstrated that our multi-faceted intervention was not effective in reducing CXRs in bronchiolitis. The decision to order CXR was associated with clinical features that overlap with pneumonia suggesting ongoing misconceptions regarding the role of CXR for this indication. Our study highlights the value of large electronic medical record datasets and robust methodology to avoid falsely attributing underlying trends to the LVC intervention.

CNN application for automated determination of the patient's size to obtain the size-specific dose estimated in CT.

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.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study.

BACKGROUND: The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States. METHODS: In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists. RESULTS: The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings. CONCLUSION: This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

¿Es necesario utilizar protectores de gónadas durante la realización de radiografías de tórax en los lactantes? / Is the use of gonad protection protectors necessary during infants chest radiography?

Introducción y objetivos: Comparar las dosis de radiación en las gónadas con y sin protector gonadal y optimizar el uso de estos protectores al realizar radiografías de tórax a lactantes. Materiales y métodos: Se utilizan 2 maniquíes antropomórficos pediátricos, un sistema de rayos X KXO-50SS/DRX-3724HD, y un sistema de radiografía digital CALNEO Smart C12, con y sin protector de gónadas durante la realización de radiografías de tórax. Se coloca un dosímetro cutáneo en tiempo real en el sistema de rayos X y se inserta un dosímetro cutáneo en tiempo real en la cara anterior de la glándula mamaria, en la cara anterior y posterior de la pelvis verdadera, y en los ovarios y testículos. El sistema de rayos X se irradia 15 veces con maniquíes, con y sin el protector de gónadas. Se comparan los valores de las dosis de entrada del paciente medidos por el dosímetro cutáneo en tiempo real para cada maniquí, con y sin el protector de gónadas. Resultados: Los valores medios de las dosis a la entrada del paciente medidos para la cara anterior a nivel de la pelvis verdadera, con y sin el protector, son 10,00 y 5,00μGy en el recién nacido, y 10,00 y 0,00μGy al año, respectivamente. Los valores medios de las dosis a la entrada del paciente medidos para la cara posterior a nivel de la pelvis verdadera con y sin el protector son de 0,00 y 0,00μGy tanto en el recién nacido como al año, respectivamente. Las dosis a la entrada del paciente medidas no se pueden detectar en los ovarios y los testículos ni con el protector ni sin él. No se observan diferencias significativas en los valores de las dosis a la entrada del paciente medidas en la cara anterior y posterior de la pelvis, los ovarios y los testículos en el recién nacido y al año, con y sin el protector (p>0,05).(AU)

Introduction and objectives: To compare gonad doses with and without a gonad protector and to optimize the use of gonadal protectors in infants thorax radiography. Materials and methods: Two pediatric anthropomorphic phantoms are used: an X-ray system for KXO-50SS/DRX-3724HD, and a digital radiography system for CALNEO Smart C12, with and without a gonad protector during infants thorax radiography. A real time skin dosimeter is placed on the X-ray system, and a real time skin dosimeter is inserted on the front side of the mammary gland, the front and back sides of the true pelvis level, and on the ovaries and testes. The X-ray system is irradiated 15 times using phantoms with and without a gonad protector. The measured entrance patient doses values of for the real time skin dosimeter are compared for each phantom, with and without the gonad protector. Results: The medium of measured entrance patient doses values for front side dose of the true pelvis level with and without the protector are 10.00 and 5.00μGy at newborn, and 10.00 and 0.00μGy at one year, respectively. The medium of measured entrance patient doses values for the back side dose of the true pelvis level with and without the protector are 0.00 and 0.00μGy at both newborn one year, respectively. The measured entrance patient doses cannot be detected in the ovaries and testes with or without the protector. No significant differences are observed in the measured entrance patient doses values for the front and back side doses of the pelvis, ovaries, and testes at newborn and one year, with and without the protector (p>0.05). Conclusions: No significant difference was observed in gonad dose measurements with and without the gonad protector during infants chest radiography. We believe that gonadal protector wearing is not necessary.(AU)

Artifact reduction in low and ultra-low dose chest computed tomography for patients with pacemaker: A phantom study.

INTRODUCTION: Implanted pacemakers (PM) would decrease the detection of lung nodules in chest computed tomography (CT) due to the metal artifact. This study aimed to explore the computer-aided diagnosis (CAD) detectability of pulmonary nodules for the patients implanted with PMs in low- and ultra-low-dose chest CT screening. METHODS: Four different sizes of artificial nodules were placed in an anthropomorphic chest phantom with two alternative diameters utilized. A commercially available PM was placed on the surface of the left chest wall of the phantom. The image acquisitions were performed with 120 kV and 150 kV with a dedicated selective photon shield made of tin filter (Sn150 kV) at low- and ultra-low- radiation doses (1.0 and 0.5 mGy of volume CT dose index), and reconstructed with and without Iterative Metal Artifact Reduction (iMAR, Siemens Healthineers, Erlangen, Germany). The relative artifact index (AIr) was calculated as an index of metal artifacts, and the nodule detectability was evaluated with a CAD system. RESULTS: Sn150 kV reduced AIr in all acquisitions when comparing 120 kV and Sn150 kV. Although PM reduced the detectability of nodules, Sn150 kV showed higher detectability compared to 120 kV. The use of iMAR showed inconsistent results in nodule detectability. CONCLUSION: Sn150 kV reduced PM-induced metal artifacts and improved nodule detectability with CAD compared to 120 kV acquisition in many conditions including low and ultra-low doses and large phantoms, but iMAR did not improve the detectability. IMPLICATIONS FOR PRACTICE: Based on the results of the current phantom study, low and ultra-low dose with Sn150 kV acquisition reduced PM-induced metal artifacts and improved nodule detectability.

Improving Computer-Aided Thoracic Disease Diagnosis through Comparative Analysis Using Chest X-ray Images Taken at Different Times.

Medical professionals in thoracic medicine routinely analyze chest X-ray images, often comparing pairs of images taken at different times to detect lesions or anomalies in patients. This research aims to design a computer-aided diagnosis system that enhances the efficiency of thoracic physicians in comparing and diagnosing X-ray images, ultimately reducing misjudgments. The proposed system encompasses four key components: segmentation, alignment, comparison, and classification of lung X-ray images. Utilizing a public NIH Chest X-ray14 dataset and a local dataset gathered by the Chiayi Christian Hospital in Taiwan, the efficacy of both the traditional methods and deep-learning methods were compared. Experimental results indicate that, in both the segmentation and alignment stages, the deep-learning method outperforms the traditional method, achieving higher average IoU, detection rates, and significantly reduced processing time. In the comparison stage, we designed nonlinear transfer functions to highlight the differences between pre- and post-images through heat maps. In the classification stage, single-input and dual-input network architectures were proposed. The inclusion of difference information in single-input networks enhances AUC by approximately 1%, and dual-input networks achieve a 1.2-1.4% AUC increase, underscoring the importance of difference images in lung disease identification and classification based on chest X-ray images. While the proposed system is still in its early stages and far from clinical application, the results demonstrate potential steps forward in the development of a comprehensive computer-aided diagnostic system for comparative analysis of chest X-ray images.

Impact of beam collimation of z-overscanning on dose to the lens and thyroid gland in paediatric thoracic computed tomography imaging.

BACKGROUND: Adaptive collimation reduces the dose deposited outside the imaged volume along the z-axis. An increase in the dose deposited outside the imaged volume (to the lens and thyroid) in the z-axis direction is a concern in paediatric computed tomography (CT). OBJECTIVE: To compare the dose deposited outside the imaged volume (to the lens and thyroid) between 40-mm and 80-mm collimation during thoracic paediatric helical CT. MATERIALS AND METHODS: We used anthropomorphic phantoms of newborns and 5-year-olds with 40-mm and 80-mm collimation during helical CT. We compared the measured dose deposited outside the imaged volume using optically stimulated luminescence dosimeters (OSLD) at the surfaces of the lens and thyroid and the image noise between the 40-mm and 80-mm collimations. RESULTS: There were significant differences in the dose deposited outside the imaged volume (to the lens and thyroid) between the 40-mm and 80-mm collimations for both phantoms (P < 0.01). CONCLUSION: Compared with that observed for 80-mm collimation in helical CT scans of the paediatric thorax, the dose deposited outside the imaged volume (to the lens and thyroid) was significantly lower in newborns and 5-year-olds with 40-mm collimation.

Prediction of bronchopulmonary dysplasia by the chest radiographic thoracic area on day one in infants with exomphalos.

OBJECTIVES: To determine if infants with exomphalos had abnormal antenatal lung growth as indicated by lower chest radiographic thoracic areas (CRTA) on day one compared to controls and whether the CRTA could predict the development of bronchopulmonary dysplasia (BPD). METHODS: Infants with exomphalos cared for between January 2004 and January 2023 were included. The controls were term, newborn infants ventilated for absent respiratory drive at birth, without lung disease and had no supplemental oxygen requirement by 6 h of age. The radiographs were imported as digital image files by Sectra PACS software (Sectra AB, Linköping, Sweden). Free-hand tracing of the perimeter of the thoracic area was undertaken and the CRTA calculated by the software. RESULTS: Sixty-four infants with exomphalos and 130 controls were included. Infants with exomphalos had a lower median (IQR) CRTA (1,983 [1,657-2,471] mm2) compared to controls (2,547 [2,153-2,932] mm2, p<0.001). Following multivariable regression analysis, infants with exomphalos had lower CRTAs compared to controls (p=0.001) after adjusting for differences in gestational age and male sex. In the exomphalos group, the CRTAs were lower in those who developed BPD (n=14, 1,530 [1,307-1,941] mm2) compared to those who did not (2,168 [1,865-2,672], p<0.001). Following multivariable regression analysis, the CRTA was associated with BPD development (p=0.021) after adjusting for male sex and gestational age. CONCLUSIONS: Lower CRTAs on day one in the exomphalos infants compared to the controls predicted BPD development.

Portable Dynamic Chest Radiography: Literature Review and Potential Bedside Applications.

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.

Spectrum of Thoracic Imaging Findings in the Setting of Substance Abuse.

ABSTRACT: Substance abuse continues to be prevalent nationwide and can lead to a myriad of chest pathologies. Imaging findings are vast and can include nodules, masses, ground-glass opacities, airspace disease, and cysts. Radiologists with awareness of these manifestations can assist in early identification of disease in situations where information is unable to be obtained from the patient. This review focuses on thoracic imaging findings associated with various forms of substance abuse, which are organized by portal of entry into the thorax: inhalation, ingestion, and injection.