Segmentation-based cardiomegaly detection based on semi-supervised estimation of cardiothoracic ratio.

The successful integration of neural networks in a clinical setting is still uncommon despite major successes achieved by artificial intelligence in other domains. This is mainly due to the black box characteristic of most optimized models and the undetermined generalization ability of the trained architectures. The current work tackles both issues in the radiology domain by focusing on developing an effective and interpretable cardiomegaly detection architecture based on segmentation models. The architecture consists of two distinct neural networks performing the segmentation of both cardiac and thoracic areas of a radiograph. The respective segmentation outputs are subsequently used to estimate the cardiothoracic ratio, and the corresponding radiograph is classified as a case of cardiomegaly based on a given threshold. Due to the scarcity of pixel-level labeled chest radiographs, both segmentation models are optimized in a semi-supervised manner. This results in a significant reduction in the costs of manual annotation. The resulting segmentation outputs significantly improve the interpretability of the architecture's final classification results. The generalization ability of the architecture is assessed in a cross-domain setting. The assessment shows the effectiveness of the semi-supervised optimization of the segmentation models and the robustness of the ensuing classification architecture.

The Influence of Computed Tomography Contrast Agent on Radiation-Induced Gene Expression and Double-Strand Breaks.

After nuclear scenarios, combined injuries of acute radiation syndrome (ARS) with, e.g., abdominal trauma, will occur and may require contrast-enhanced computed tomography (CT) scans for diagnostic purposes. Here, we investigated the effect of iodinated contrast agents on radiation-induced gene expression (GE) changes used for biodosimetry (AEN, BAX, CDKN1A, EDA2R, APOBEC3H) and for hematologic ARS severity prediction (FDXR, DDB2, WNT3, POU2AF1), and on the induction of double-strand breaks (DSBs) used for biodosimetry. Whole blood samples from 10 healthy donors (5 males, 5 females, mean age: 28 ± 2 years) were irradiated with X rays (0, 1 and 4 Gy) with and without the addition of iodinated contrast agent (0.016 ml contrast agent/ml blood) to the blood prior to the exposure. The amount of contrast agent was set to be equivalent to the blood concentration of an average patient (80 kg) during a contrast-enhanced CT scan. After irradiation, blood samples were incubated at 37°C for 20 min (DSB) and 8 h (GE, DSB). GE was measured employing quantitative real-time polymerase chain reaction. DSB foci were revealed by γH2AX + 53BP1 immunostaining and quantified automatically in >927 cells/sample. Radiation-induced differential gene expression (DGE) and DSB foci were calculated using the respective unexposed sample without supplementation of contrast agent as the reference. Neither the GE nor the number of DSB foci was significantly (P = 0.07-0.94) altered by the contrast agent application. However, for some GE and DSB comparisons with/without contrast agent, there were weakly significant differences (P = 0.03-0.04) without an inherent logic and thus are likely due to inter-individual variation. In nuclear events, the diagnostics of combined injuries can require the use of an iodinated contrast agent, which, according to our results, does not alter or influence radiation-induced GE changes and the quantity of DSB foci. Therefore, the gene expression and γH2AX focus assay can still be applied for biodosimetry and/or hematologic ARS severity prediction in such scenarios.

Detection of caries lesions using a water-sensitive STIR sequence in dental MRI.

In clinical practice, diagnosis of suspected carious lesions is verified by using conventional dental radiography (DR), including panoramic radiography (OPT), bitewing imaging, and dental X-ray. The aim of this study was to evaluate the use of magnetic resonance imaging (MRI) for caries visualization. Fourteen patients with clinically suspected carious lesions, verified by standardized dental examination including DR and OPT, were imaged with 3D isotropic T2-weighted STIR (short tau inversion recovery) and T1 FFE Black bone sequences. Intensities of dental caries, hard tissue and pulp were measured and calculated as aSNR (apparent signal to noise ratio) and aHTMCNR (apparent hard tissue to muscle contrast to noise ratio) in both sequences. Imaging findings were then correlated to clinical examination results. In STIR as well as in T1 FFE black bone images, aSNR and aHTMCNR was significantly higher in carious lesions than in healthy hard tissue (p < 0.001). Using water-sensitive STIR sequence allowed for detecting significantly lower aSNR and aHTMCNR in carious teeth compared to healthy teeth (p = 0.01). The use of MRI for the detection of caries is a promising imaging technique that may complement clinical exams and traditional imaging.

Comparison of Two Contemporary Quantitative Atherosclerotic Plaque Assessment Tools for Coronary Computed Tomography Angiography: Single-Center Analysis and Multi-Center Patient Cohort Validation.

BACKGROUND: Coronary computed tomography angiography (CCTA) provides non-invasive quantitative assessments of plaque burden and composition. The quantitative assessment of plaque components requires the use of analysis software that provides reproducible semi-automated plaque detection and analysis. However, commercially available plaque analysis software can vary widely in the degree of automation, resulting in differences in terms of reproducibility and time spent. AIM: To compare the reproducibility and time spent of two CCTA analysis software tools using different algorithms for the quantitative assessment of coronary plaque volumes and composition in two independent patient cohorts. METHODS: The study population included 100 patients from two different cohorts: 50 patients from a single-center (Siemens Healthineers, SOMATOM Force (DSCT)) and another 50 patients from a multi-center study (5 different > 64 slice CT scanner types). Quantitative measurements of total calcified and non-calcified plaque volume of the right coronary artery (RCA), left anterior descending (LAD), and left circumflex coronary artery (LCX) were performed on a total of 300 coronaries by two independent readers, using two different CCTA analysis software tools (Tool #1: Siemens Healthineers, syngo.via Frontier CT Coronary Plaque Analysis and Tool #2: Siemens Healthineers, successor CT Coronary Plaque Analysis prototype). In addition, the total time spent for the analysis was recorded with both programs. RESULTS: The patients in cohorts 1 and 2 were 62.8 ± 10.2 and 70.9 ± 11.7 years old, respectively, 10 (20.0%) and 35 (70.0%) were female and 34 (68.0%) and 20 (40.0%), respectively, had hyperlipidemia. In Cohort #1, the inter- and intra-observer variabilities for the assessment of plaque volumes per patient for Tool #1 versus Tool #2 were 22.8%, 22.0%, and 26.0% versus 2.3%, 3.9%, and 2.5% and 19.7%, 21.4%, and 22.1% versus 0.2%, 0.1%, and 0.3%, respectively, for total, noncalcified, and calcified lesions (p < 0.001 for all between Tools #1 and 2 both for inter- and intra-observer). The inter- and intra-observer variabilities using Tool #2 remained low at 2.9%, 2.7%, and 3.0% and 3.8%, 3.7%, and 4.0%, respectively, for total, non-calcified, and calcified lesions in Cohort #2. For each dataset, the median processing time was higher for Tool #1 versus Tool #2 (459.5 s IQR = 348.0-627.0 versus 208.5 s; IQR = 198.0-216.0) (p < 0.001). CONCLUSION: The plaque analysis Tool #2 (CT-guided PCI) encompassing a higher degree of automated support required less manual editing, was more time-efficient, and showed a higher intra- and inter-observer reproducibility for the quantitative assessment of plaque volumes both in a representative single-center and in a multi-center validation cohort.

Applicability of the CT Radiomics of Skeletal Muscle and Machine Learning for the Detection of Sarcopenia and Prognostic Assessment of Disease Progression in Patients with Gastric and Esophageal Tumors.

PURPOSE: Sarcopenia is considered a negative prognostic factor in patients with malignant tumors. Among other diagnostic options, computed tomography (CT), which is repeatedly performed on tumor patients, can be of further benefit. The present study aims to establish a framework for classifying the impact of sarcopenia on the prognosis of patients diagnosed with esophageal or gastric cancer. Additionally, it explores the significance of CT radiomics in both diagnostic and prognostic methodologies. MATERIALS AND METHODS: CT scans of 83 patients with esophageal or gastric cancer taken at the time of diagnosis and during a follow-up period of one year were evaluated retrospectively. A total of 330 CT scans were analyzed. Seventy three of these patients received operative tumor resection after neoadjuvant chemotherapy, and 74% of the patients were male. The mean age was 64 years (31-83 years). Three time points (t) were defined as a basis for the statistical analysis in order to structure the course of the disease: t1 = initial diagnosis, t2 = following (neoadjuvant) chemotherapy and t3 = end of the first year after surgery in the "surgery" group or end of the first year after chemotherapy. Sarcopenia was determined using the psoas muscle index (PMI). The additional analysis included the analysis of selected radiomic features of the psoas major, quadratus lumborum, and erector spinae muscles at the L3 level. Disease progression was monitored according to the response evaluation criteria in solid tumors (RECIST 1.1). CT scans and radiomics were used to assess the likelihood of tumor progression and their correlation to sarcopenia. For machine learning, the established algorithms decision tree (DT), K-nearest neighbor (KNN), and random forest (RF) were applied. To evaluate the performance of each model, a 10-fold cross-validation as well as a calculation of Accuracy and Area Under the Curve (AUC) was used. RESULTS: During the observation period of the study, there was a significant decrease in PMI. This was most evident in patients with surgical therapy in the comparison between diagnosis and after both neoadjuvant therapy and surgery (each p < 0.001). Tumor progression (PD) was not observed significantly more often in the patients with sarcopenia compared to those without sarcopenia at any time point (p = 0.277 to p = 0.465). On average, PD occurred after 271.69 ± 104.20 days. The time from initial diagnosis to PD in patients "with sarcopenia" was not significantly shorter than in patients "without sarcopenia" at any of the time points (p = 0.521 to p = 0.817). The CT radiomics of skeletal muscle could predict both sarcopenia and tumor progression, with the best results for the psoas major muscle using the RF algorithm. For the detection of sarcopenia, the Accuracy was 0.90 ± 0.03 and AUC was 0.96 ± 0.02. For the prediction of PD, the Accuracy was 0.88 ± 0.04 and the AUC was 0.93 ± 0.04. CONCLUSIONS: In the present study, the CT radiomics of skeletal muscle together with machine learning correlated with the presence of sarcopenia, and this can additionally assist in predicting disease progression. These features can be classified as promising alternatives to conventional methods, with great potential for further research and future clinical application. However, when sarcopenia was diagnosed with PMI, no significant correlation between sarcopenia and PD could be observed.

Arterial Spin Labeling (ASL) in Neuroradiological Diagnostics - Methodological Overview and Use Cases.

BACKGROUND: Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI)-based technique using labeled blood-water of the brain-feeding arteries as an endogenous tracer to derive information about brain perfusion. It enables the assessment of cerebral blood flow (CBF). METHOD: This review aims to provide a methodological and technical overview of ASL techniques, and to give examples of clinical use cases for various diseases affecting the central nervous system (CNS). There is a special focus on recent developments including super-selective ASL (ssASL) and time-resolved ASL-based magnetic resonance angiography (MRA) and on diseases commonly not leading to characteristic alterations on conventional structural MRI (e. g., concussion or migraine). RESULTS: ASL-derived CBF may represent a clinically relevant parameter in various pathologies such as cerebrovascular diseases, neoplasms, or neurodegenerative diseases. Furthermore, ASL has also been used to investigate CBF in mild traumatic brain injury or migraine, potentially leading to the establishment of imaging-based biomarkers. Recent advances made possible the acquisition of ssASL by selective labeling of single brain-feeding arteries, enabling spatial perfusion territory mapping dependent on blood flow of a specific preselected artery. Furthermore, ASL-based MRA has been introduced, providing time-resolved delineation of single intracranial vessels. CONCLUSION: Perfusion imaging by ASL has shown promise in various diseases of the CNS. Given that ASL does not require intravenous administration of a gadolinium-based contrast agent, it may be of particular interest for investigations in pediatric cohorts, patients with impaired kidney function, patients with relevant allergies, or patients that undergo serial MRI for clinical indications such as disease monitoring. KEY POINTS: · ASL is an MRI technique that uses labeled blood-water as an endogenous tracer for brain perfusion imaging.. · It allows the assessment of CBF without the need for administration of a gadolinium-based contrast agent.. · CBF quantification by ASL has been used in several pathologies including brain tumors or neurodegenerative diseases.. · Vessel-selective ASL methods can provide brain perfusion territory mapping in cerebrovascular diseases.. · ASL may be of particular interest in patient cohorts with caveats concerning gadolinium administration..

On-site CT-derived cFFR in patients with suspected coronary artery disease: Feasibility on a 128-row CT scanner in everyday clinical practice.

PURPOSE: Technical feasibility of CT-based calculation of fractional flow reserve (cFFR) using a 128-row computed tomography scanner in an everyday routine setting. Post-processing and everyday practicability should be analyzed on the scanner on-site in connection with clinical parameters. MATERIALS AND METHODS: This single-center retrospective analysis included 230 patients (74 female; mean age 63.8 years) with CCTA within 21 months between 01/2018 and 09/2019 without non-pathological examinations. cFFR values were obtained using a deep learning-based non-commercial research prototype (cFFR Version3.5.0; Siemens Healthineers GmbH, Erlangen). cFFR values were evaluated at two points: at the maximum point of the stenosis and 1.0 cm distal to the stenosis. Comparison with invasive coronary angiography in 57/230 patients (24.7 %) was performed. CT parameters and quality were evaluated. Further subgroup classification concerning criteria of technical postprocessing was performed: no changes necessary, minor corrections necessary, major corrections necessary, and no evaluation was possible. The required time from starting the software to the final result was evaluated. RESULTS: A total of 116/448 (25.9 %) mild, 223/448 (49.8 %) moderate, and 109/448 (24.3 %) obstructive stenoses was found. The mean cFFR at the maximum point of the stenosis was 0.92 ±â€Š0.09 and significantly higher than the cFRR value of 0.89 ±â€Š0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ±â€Š26.99 % (range: 1-99 %) with an area of 5.39 ±â€Š3.30 mm2. In a total of 45 patients (19.1 %), a relevant reduction in cFFR below 0.80 was determined. Overall, in 57/230 patients (24.8 %), catheter angiography was performed. No significant difference in the degree of maximal stenosis (CAD-RADS 0-2/3/4) was detected between the classification of CCTA and ICA (p = 0.171). The mean post-processing time varied significantly with 8.34 ±â€Š4.66 min. in single-vessel CAD vs. 12.91 ±â€Š3.92 min. in two-vessel CAD vs. 21.80 ± 5.94 min. in three-vessel CAD (each p < 0.001). CONCLUSION: Noninvasive onsite quantification of cFFR is feasible with minimal observer interaction in a routine real-world setting on a 128-row scanner. Deep learning-based algorithms allow a robust and semi-automatic on-site determination of cFFR based on data from standard CT scanners. KEY POINTS: · Non-invasive on-site quantification of cFFR is feasible with minimal observer interaction.. · Deep-learning based algorithms allow robust and semi-automatic on-site determination of cFFR.. · The mean follow-up time varied significantly with the extent of vascular CAD..

Radiomics and Clinicopathological Characteristics for Predicting Lymph Node Metastasis in Testicular Cancer.

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment.

Segmental quantification of hepatic lipid content based on volumetric MRI data in patients with suspected iron overload.

PURPOSE: To investigate the segmental distribution of hepatic fat fraction, determined with MRI (MR proton density fat fraction, short MR-PDFF) in patients suspected of having liver iron overload. METHODS: The liver of 44 patients examined with MRI using a 3D multi-echo gradient-echo sequence was segmented semiautomatically and subdivided into nine segments (segment 4 divided in 4a and 4b). Segmental fat content was determined on MR-PDFF maps. Whole-liver steatosis grades were compared to those found in individual segments. Segmental MR-PDFF differences were tested for statistical significance. RESULTS: The most common diseases were thalassemia, various forms of anemia, and hereditary hemochromatosis. No patients suffered from fat metabolism disease. Iron overload was present in 37/44 (84 %) patients. For the whole liver, 22 patients showed a steatosis grade of 0, 21 patients were graded S1, and one patient had a steatosis grade of 2. The grade of steatosis was underestimated in 5 of 21 patients (24 %) in segment 8 and in 8 of 21 patients (38 %) in segment 7. Highly significant segmental MR-PDFF differences were detected with p < 0.00 001, e. g., comparing segment 2 to 5. Segments 1 to 3 had the highest fat content, segments 7 and 8 had the lowest. CONCLUSION: Our results suggest that the storage of fat in the liver is inhomogeneous, so that segment-wise differing fat concentrations were found. Fat distribution in patients with suspected hepatic iron overload was similar to living liver donors. However, it showed significant differences compared with the values published for NAFLD patients, which were less pronounced in the group with high average hepatic MR-PDFF values than in the group with normal lipid content. In patients suspected of having iron overload, segment 8, which is mainly targeted for biopsy, and segment 7 may underestimate steatosis grade. KEY POINTS: · A volumetric analysis of 3D MRI data of patients with suspected hepatic iron overload yielded a markedly elevated MR proton density fat fraction (MR-PDFF) in hepatic segments 1 to 3.. · This hepatic fat distribution, observed for the whole patient cohort, is similar to healthy living liver donors.. · The subgroup of patients with a high average MR-PDFF ≥ 6.5 % shows this effect with lower segmental deviations.. · In patients without fat metabolic disorders, the steatosis grade may be underestimated when taking biopsies in segment 8 or 7..

A Critical Assessment of Generative Models for Synthetic Data Augmentation on Limited Pneumonia X-ray Data.

In medical imaging, deep learning models serve as invaluable tools for expediting diagnoses and aiding specialized medical professionals in making clinical decisions. However, effectively training deep learning models typically necessitates substantial quantities of high-quality data, a resource often lacking in numerous medical imaging scenarios. One way to overcome this deficiency is to artificially generate such images. Therefore, in this comparative study we train five generative models to artificially increase the amount of available data in such a scenario. This synthetic data approach is evaluated on a a downstream classification task, predicting four causes for pneumonia as well as healthy cases on 1082 chest X-ray images. Quantitative and medical assessments show that a Generative Adversarial Network (GAN)-based approach significantly outperforms more recent diffusion-based approaches on this limited dataset with better image quality and pathological plausibility. We show that better image quality surprisingly does not translate to improved classification performance by evaluating five different classification models and varying the amount of additional training data. Class-specific metrics like precision, recall, and F1-score show a substantial improvement by using synthetic images, emphasizing the data rebalancing effect of less frequent classes. However, overall performance does not improve for most models and configurations, except for a DreamBooth approach which shows a +0.52 improvement in overall accuracy. The large variance of performance impact in this study suggests a careful consideration of utilizing generative models for limited data scenarios, especially with an unexpected negative correlation between image quality and downstream classification improvement.

Imaging of Pathologies of the Temporal Bone and Middle Ear: Inflammatory Diseases, Their Mimics and Potential Complications-Pictorial Review.

Imaging of the temporal bone and middle ear is challenging for radiologists due to the abundance of distinct anatomical structures and the plethora of possible pathologies. The basis for a precise diagnosis is knowledge of the underlying anatomy as well as the clinical presentation and the individual patient's otological status. In this article, we aimed to summarize the most common inflammatory lesions of the temporal bone and middle ear, describe their specific imaging characteristics, and highlight their differential diagnoses. First, we introduce anatomical and imaging fundamentals. Additionally, a point-to-point comparison of the radiological and histological features of the wide spectrum of inflammatory diseases of the temporal bone and middle ear in context with a review of the current literature and current trends is given.

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging.

Deep learning in medical imaging has the potential to minimize the risk of diagnostic errors, reduce radiologist workload, and accelerate diagnosis. Training such deep learning models requires large and accurate datasets, with annotations for all training samples. However, in the medical imaging domain, annotated datasets for specific tasks are often small due to the high complexity of annotations, limited access, or the rarity of diseases. To address this challenge, deep learning models can be pre-trained on large image datasets without annotations using methods from the field of self-supervised learning. After pre-training, small annotated datasets are sufficient to fine-tune the models for a specific task. The most popular self-supervised pre-training approaches in medical imaging are based on contrastive learning. However, recent studies in natural image processing indicate a strong potential for masked autoencoder approaches. Our work compares state-of-the-art contrastive learning methods with the recently introduced masked autoencoder approach "SparK" for convolutional neural networks (CNNs) on medical images. Therefore, we pre-train on a large unannotated CT image dataset and fine-tune on several CT classification tasks. Due to the challenge of obtaining sufficient annotated training data in medical imaging, it is of particular interest to evaluate how the self-supervised pre-training methods perform when fine-tuning on small datasets. By experimenting with gradually reducing the training dataset size for fine-tuning, we find that the reduction has different effects depending on the type of pre-training chosen. The SparK pre-training method is more robust to the training dataset size than the contrastive methods. Based on our results, we propose the SparK pre-training for medical imaging tasks with only small annotated datasets.

[Value of PET imaging in head and neck cancer]. / Stellenwert der PET-Bildgebung bei Kopf-Hals-Tumoren.

The combination of positron-emission tomography (PET) with cross-sectional imaging in particular is becoming increasingly important in the diagnosis of head and neck tumors because, in addition to pure anatomy, the metabolic activity of tissue can be visualized and assessed. The combination of PET and computed tomography (CT) is already an established procedure in head and neck tumor patients in some indications, e.g., for primary tumor detection in cancer of unknown primary (CUP) syndrome or also after completed primary radio(chemo)therapy for evaluation of response, especially also with regard to nodal status. In some cases, salvage neck dissection can thus be avoided in the case of PET-negative findings. In the context of primary diagnosis, PET/CT imaging can be used primarily to evaluate distant metastasis. According to current guidelines, PET-based imaging is not (yet) of value in determining the local extent at initial diagnosis. A challenge is the still limited reimbursement by health insurance companies, which currently allow only certain indications, and the still lack of nationwide coverage.

Organ-based tube current modulation versus spectral shaping via tin prefiltration: What does bismuth breast shielding add in low-dose lung CT?

PURPOSE: Since organ-based tube current modulation (OBTCM) and tin prefiltration are limited on their own in lowering the dose of lung CT examinations, this experimental study was designed to investigate whether combinations with anterior patient shielding can increase the dose reduction potential. MATERIAL AND METHODS: Three pairs of scan protocols without/with breast shield (P1/P2: standard 120kVp, P3/P4: OBTCM at 100 kVp, P5/P6: Sn 100 kVp) were employed for radiation exposure and image quality comparisons on an anthropomorphic Alderson-Rando phantom. Equivalent doses were measured in eleven sites via thermoluminescent dosimetry and the effective dose was obtained by summation of the weighted organ doses. Dose-weighted contrast-to-noise ratios (CNRD) were calculated and four radiologists independently assessed the quality of images generated with each protocol. RESULTS: While no significant difference was determined between standard and OBTCM protocols regardless of breast shield (p ≥ 0.068), equivalent doses with spectral shaping were substantially lower (p ≤ 0.003). The highest effective dose was ascertained for standard scans (P1/P2: 7.3/6.8 mSv) with a dose reduction of 8.0 % via breast shielding. The use of a bismuth shield was more beneficial in OBTCM (P3/P4: 6.6/5.3 mSv) and spectral shaping (P5/P6: 0.7/0.6 mSv), reducing the effective dose by 19.8 % and 13.9 %, respectively. Subjective assessment favoured standard protocol P1 over tin prefiltration low-dose scans (p ≤ 0.032), however, no scan protocol entailed diagnostically insufficient image quality. CONCLUSIONS: Whereas breast shielding is particularly beneficial in combination with OBTCM, spectral shaping via tin prefiltration facilitates the most pronounced dose reduction in lung CT imaging with acceptable image quality.

Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.

Since the beginning of the COVID-19 pandemic, many different machine learning models have been developed to detect and verify COVID-19 pneumonia based on chest X-ray images. Although promising, binary models have only limited implications for medical treatment, whereas the prediction of disease severity suggests more suitable and specific treatment options. In this study, we publish severity scores for the 2358 COVID-19 positive images in the COVIDx8B dataset, creating one of the largest collections of publicly available COVID-19 severity data. Furthermore, we train and evaluate deep learning models on the newly created dataset to provide a first benchmark for the severity classification task. One of the main challenges of this dataset is the skewed class distribution, resulting in undesirable model performance for the most severe cases. We therefore propose and examine different augmentation strategies, specifically targeting majority and minority classes. Our augmentation strategies show significant improvements in precision and recall values for the rare and most severe cases. While the models might not yet fulfill medical requirements, they serve as an appropriate starting point for further research with the proposed dataset to optimize clinical resource allocation and treatment.

Discharge of iodine-containing contrast media into the environment - problem analysis and implementation of measures to reduce discharge by means of separation toilets - experience from a pilot project. / Herausforderung Eintrag jodhaltiger Röntgenkontrastmitteln in die Umwelt ­ Problemanalyse sowie Umsetzung erster Maßnahmen zur Reduktion des Eintrags mittels Trenntoiletten ­ Erfahrungen aus einem Pilotprojekt.

PURPOSE: Environmental aspects and sustainability are becoming increasingly important. In addition to energy consumption, the consumption and environmental discharge of contrast agents pose a particular challenge. Because of their desired stability, X-ray contrast agents (XCAs) are deposited in surface water at a rate of up to 400 tons per year. MATERIALS AND METHODS: In a pilot project, a set of measures (installation of specific separation toilets, the establishment of feedback systems, interviews, questionnaires, and observation) was implemented to sensitize patients and staff to the problem of XCAs during outpatient CT examinations and a retention and recovery system for XCAs was evaluated. RESULTS: In the initial baseline phase, a separation toilet with an additional collection system and a feedback/button system was installed. The built-in feedback system indicated that the separation toilets were used by approx. 16 % of patients without measures. In two subsequent intervention phases, accompanying measures significantly (p < 0.01) increased the use of these separation toilets to 21 % and 25 %, respectively. The measures to reduce the discharge of XCAs were positively assessed by both staff and patients. CONCLUSION: Measures to reduce the discharge of XCAs into the environment have a high acceptance among staff and patients. The subsequent installation of separation toilets is one possibility to achieve on-site retention of XCAs. However, this measure is likely to be of high value only if patients stay on site for a correspondingly long time, as is the case in cardiology, for example. KEY POINTS: · The input of X-ray contrast agents into the environment is relevant in light of the quantity. · Measures to reduce the discharge of X-ray contrast agents into the environment have been investigated in pilot projects. · The (subsequent) installation of separation toilets is possible and allows retention of X-ray contrast agents. · This measure is considered useful by patients and staff. · The financing of these measures needs to be clarified. CITATION FORMAT: · Beer M, Schuler J, Kraus E et al. Discharge of iodine-containing contrast media into the environment - problem analysis and implementation of measures to reduce discharge by means of separation toilets - experience from a pilot project. Fortschr Röntgenstr 2023; 195: 1122 - 1127.

Gastroenteropancreatic Neuroendocrine Tumors-Current Status and Advances in Diagnostic Imaging.

Gastroenteropancreatic neuroendocrine neoplasia (GEP-NEN) is a heterogeneous and complex group of tumors that are often difficult to classify due to their heterogeneity and varying locations. As standard radiological methods, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography-computed tomography (PET/CT) are available for both localization and staging of NEN. Nuclear medical imaging methods with somatostatin analogs are of great importance since radioactively labeled receptor ligands make tumors visible with high sensitivity. CT and MRI have high detection rates for GEP-NEN and have been further improved by developments such as diffusion-weighted imaging. However, nuclear medical imaging methods are superior in detection, especially in gastrointestinal NEN. It is important for radiologists to be familiar with NEN, as it can occur ubiquitously in the abdomen and should be identified as such. Since GEP-NEN is predominantly hypervascularized, a biphasic examination technique is mandatory for contrast-enhanced cross-sectional imaging. PET/CT with somatostatin analogs should be used as the subsequent method.