Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach.

PURPOSE: Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data. METHODS: Our study included 132 thoracic CT scans from clinical practice, annotated by 13 radiologists. In three iterative training experiments, we aimed to improve and accelerate segmentation of the heart and mediastinum. Each experiment started with manual segmentation of 5-25 CT scans, which served as training data for a nnU-Net. Further iterations incorporated AI pre-segmentation and human correction to improve accuracy, accelerate the annotation process, and reduce human involvement over time. RESULTS: Results showed consistent improvement in AI model quality with each iteration. Resampled datasets improved the Dice similarity coefficients for both the heart (DCS 0.91 [0.88; 0.92]) and the mediastinum (DCS 0.95 [0.94; 0.95]). Our AI models reduced human interaction time by 50 % for heart and 70 % for mediastinum segmentation in the most potent iteration. A model trained on only five datasets achieved satisfactory results (DCS > 0.90). CONCLUSIONS: The iterative training workflow provides an efficient method for training AI-based segmentation models in multi-center studies, improving accuracy over time and simultaneously reducing human intervention. Future work will explore the use of fewer initial datasets and additional pre-processing methods to enhance model quality.

Multiparametric Cardiovascular MRI Assessment of Post-COVID Syndrome in Children in Comparison to Matched Healthy Individuals.

BACKGROUND: Post-COVID syndrome (PCS) can adversely affect the quality of life of patients and their families. In particular, the degree of cardiac impairment in children with PCS is unknown. OBJECTIVE: The aim of this study was to identify potential cardiac inflammatory sequelae in children with PCS compared with healthy controls. METHODS: This single-center, prospective, intraindividual, observational study assesses cardiac function, global and segment-based strains, and tissue characterization in 29 age- and sex-matched children with PCS and healthy children using a 3 T magnetic resonance imaging (MRI). RESULTS: Cardiac MRI was carried out over 36.4 ± 24.9 weeks post-COVID infection. The study cohort has an average age of 14.0 ± 2.8 years, for which the majority of individuals experience from fatigue, concentration disorders, dyspnea, dizziness, and muscle ache. Children with PSC in contrast to the control group exhibited elevated heart rate (83.7 ± 18.1 beats per minute vs 75.2 ± 11.2 beats per minute, P = 0.019), increased indexed right ventricular end-diastolic volume (95.2 ± 19.2 mlm -2 vs 82.0 ± 21.5 mlm -2 , P = 0.018) and end-systolic volume (40.3 ± 7.9 mlm -2 vs 34.8 ± 6.2 mlm -2 , P = 0.005), and elevated basal and midventricular T1 and T2 relaxation times ( P < 0.001 to P = 0.013). Based on the updated Lake Louise Criteria, myocardial inflammation is present in 20 (69%) children with PCS. No statistically significant difference was observed for global strains. CONCLUSIONS: Cardiac MRI revealed altered right ventricular volumetrics and elevated T1 and T2 mapping values in children with PCS, suggestive for a diffuse myocardial inflammation, which may be useful for the diagnostic workup of PCS in children.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study.

RATIONALE AND OBJECTIVES: The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting. MATERIALS AND METHODS: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed. RESULTS: Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes. CONCLUSION: The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.

Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology.

Data-driven machine learning in medical research and diagnostics needs large-scale datasets curated by clinical experts. The generation of large datasets can be challenging in terms of resource consumption and time effort, while generalizability and validation of the developed models significantly benefit from variety in data sources. Training algorithms on smaller decentralized datasets through federated learning can reduce effort, but require the implementation of a specific and ambitious infrastructure to share data, algorithms and computing time. Additionally, it offers the opportunity of maintaining and keeping the data locally. Thus, data safety issues can be avoided because patient data must not be shared. Machine learning models are trained on local data by sharing the model and through an established network. In addition to commercial applications, there are also numerous academic and customized implementations of network infrastructures available. The configuration of these networks primarily differs, yet adheres to a standard framework composed of fundamental components. In this technical note, we propose basic infrastructure requirements for data governance, data science workflows, and local node set-up, and report on the advantages and experienced pitfalls in implementing the local infrastructure with the German Radiological Cooperative Network initiative as the use case example. We show how the infrastructure can be built upon some base components to reflect the needs of a federated learning network and how they can be implemented considering both local and global network requirements. After analyzing the deployment process in different settings and scenarios, we recommend integrating the local node into an existing clinical IT infrastructure. This approach offers benefits in terms of maintenance and deployment effort compared to external integration in a separate environment (e.g., the radiology department). This proposed groundwork can be taken as an exemplary development guideline for future applications of federated learning networks in clinical and scientific environments.

Work expectations, their fulfillment, and exhaustion among radiologists of all career levels: what can be learned from the example of Germany.

OBJECTIVES: To evaluate work expectations of radiologists at different career levels, their fulfillment, prevalence of exhaustion, and exhaustion-associated factors. METHODS: A standardized digital questionnaire was distributed internationally to radiologists of all career levels in the hospital and in ambulatory care via radiological societies and sent manually to 4500 radiologists of the largest German hospitals between December 2020 and April 2021. Statistics were based on age- and gender-adjusted regression analyses of respondents working in Germany (510 out of 594 total respondents). RESULTS: The most frequent expectations were "joy at work" (97%) and a "good working atmosphere" (97%), which were considered fulfilled by at least 78%. The expectation of a "structured residency within the regular time interval" (79%) was more frequently judged fulfilled by senior physicians (83%, odds ratio (OR) 4.31 [95% confidence interval (95% CI) 1.95-9.52]), chief physicians (85%, 6.81 [95% CI 1.91-24.29]), and radiologists outside the hospital (88%, 7.59 [95% CI 2.40-24.03]) than by residents (68%). Exhaustion was most common among residents (physical exhaustion: 38%; emotional exhaustion: 36%), in-hospital specialists (29%; 38%), and senior physicians (30%; 29%). In contrast to paid extra hours, unpaid extra hours were associated with physical exhaustion (5-10 extra hours: OR 2.54 [95% CI 1.54-4.19]). Fewer opportunities to shape the work environment were related to a higher probability of physical (2.03 [95% CI 1.32-3.13]) and emotional (2.15 [95% CI 1.39-3.33]) exhaustion. CONCLUSIONS: While most radiologists enjoy their work, residents wish for more training structure. Ensuring payment of extra hours and employee empowerment may help preventing burnout in high-risk groups. KEY POINTS: • Most important work expectations of radiologists who work in Germany are "joy at work," a "good working atmosphere," "support for further qualification," and a "structured residency within the regular time interval," with the latter containing potential for improvement according to residents. • Physical and emotional exhaustion are common at all career levels except for chief physicians and for radiologists who work outside the hospital in ambulatory care. • Exhaustion as a major burnout criterion is associated with unpaid extra hours and reduced opportunities to shape the work environment.

AI support for accurate and fast radiological diagnosis of COVID-19: an international multicenter, multivendor CT study.

OBJECTIVES: Differentiation between COVID-19 and community-acquired pneumonia (CAP) in computed tomography (CT) is a task that can be performed by human radiologists and artificial intelligence (AI). The present study aims to (1) develop an AI algorithm for differentiating COVID-19 from CAP and (2) evaluate its performance. (3) Evaluate the benefit of using the AI result as assistance for radiological diagnosis and the impact on relevant parameters such as accuracy of the diagnosis, diagnostic time, and confidence. METHODS: We included n = 1591 multicenter, multivendor chest CT scans and divided them into AI training and validation datasets to develop an AI algorithm (n = 991 CT scans; n = 462 COVID-19, and n = 529 CAP) from three centers in China. An independent Chinese and German test dataset of n = 600 CT scans from six centers (COVID-19 / CAP; n = 300 each) was used to test the performance of eight blinded radiologists and the AI algorithm. A subtest dataset (180 CT scans; n = 90 each) was used to evaluate the radiologists' performance without and with AI assistance to quantify changes in diagnostic accuracy, reporting time, and diagnostic confidence. RESULTS: The diagnostic accuracy of the AI algorithm in the Chinese-German test dataset was 76.5%. Without AI assistance, the eight radiologists' diagnostic accuracy was 79.1% and increased with AI assistance to 81.5%, going along with significantly shorter decision times and higher confidence scores. CONCLUSION: This large multicenter study demonstrates that AI assistance in CT-based differentiation of COVID-19 and CAP increases radiological performance with higher accuracy and specificity, faster diagnostic time, and improved diagnostic confidence. KEY POINTS: • AI can help radiologists to get higher diagnostic accuracy, make faster decisions, and improve diagnostic confidence. • The China-German multicenter study demonstrates the advantages of a human-machine interaction using AI in clinical radiology for diagnostic differentiation between COVID-19 and CAP in CT scans.

Lightweight Techniques to Improve Generalization and Robustness of U-Net Based Networks for Pulmonary Lobe Segmentation.

Lung lobe segmentation in chest CT is relevant to a wide range of clinical applications. However, existing segmentation pipelines often exhibit vulnerabilities and performance degradations when applied to external datasets. This is usually attributed to the size of the available dataset or model. We show that it is possible to enhance generalizability without huge resources by carefully curating the dataset and combining machine learning with medical expertise. Multiple machine learning techniques (self-supervision (SSL), attention (A), and data augmentation (DA)) are used to train a fast and fully-automated lung lobe segmentation model based on 2D U-Net. Our study involved evaluating these techniques on a diverse dataset collected under the RACOON project, encompassing 100 CT chest scans from patients with bacterial, viral, or SARS-CoV2 infections. We compare our model to a baseline U-Net trained on the same dataset. Our approach significantly improved segmentation accuracy (Dice score of 92.8% vs. 82.3%, p < 0.001). Moreover, our model achieved state-of-the-art performance (Dice score of 92.8% vs. 90.8% for the literature's state-of-the-art, p = 0.102) with reduced training examples (69 vs. 231 CT Scans). Among the techniques, data augmentation with expert knowledge displayed the most significant impact, enhancing the Dice score by +0.056. Notably, these enhancements are not limited to lobe segmentation but can be seamlessly integrated into various medical imaging segmentation tasks, demonstrating their versatility and potential for broader applications.

Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation.

Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD) data. We propose a lightweight OOD detection method that leverages the Mahalanobis distance in the feature space and seamlessly integrates into state-of-the-art segmentation pipelines. The simple approach can even augment pre-trained models with clinically relevant uncertainty quantification. We validate our method across four chest CT distribution shifts and two magnetic resonance imaging applications, namely segmentation of the hippocampus and the prostate. Our results show that the proposed method effectively detects far- and near-OOD samples across all explored scenarios.

CODEX Meets RACOON - A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data.

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Work and Training Conditions of German Residents and Young Radiologists in Interventional Radiology - A Nationwide Survey.

With the increasing need for minimally invasive procedures based on lower complication rates, higher patient acceptance, and technical developments, there is a growing focus on the sound interventional training of young radiologists. This survey aimed to analyze the current situation in interventional radiology (IR) training in Germany to detect shortcomings and identify areas for improvement.From November 1-30, 2020, an online questionnaire was distributed to representative radiological associations and societies with the request to forward it to radiology residents and radiologists < 40 years. The 44 questions covered six distinct areas from personal working conditions to the characterization of the IR department, training conditions, role of women in IR, and attendance at congresses/external training.A total of 330 participants completed the questionnaire. 77 % of participants expressed a high interest in IR, and 47 % could even imagine subspecializing in interventional radiology. Most institutions provided the necessary learning conditions and infrastructure. The rate of overall satisfaction with IR training conditions was 45 % (vs. a dissatisfaction rate of 39 %). However, females showed a lower satisfaction rate with their training environment than male participants (28 % vs. 51 %; P = 0.06). Positive correlations with work satisfaction were found for the presence and duration of the IR rotation, the number of partly independently/mentored performed interventions, and structured feedback. Moreover, the need for a structured training curriculum was expressed by 67 % of participants.Radiological residents and young radiologists expressed a high interest in interventional radiology, and they rate the infrastructure of German hospitals regarding IR as sufficient. However, they expressed the need for consistent IR rotations and better-structured resident and postgraduate education (curricula & interviews).Interest in interventional radiology among radiological residents and young radiologists in Germany is high, but satisfaction with interventional radiology training leaves room for improvement. The most frequently mentioned aspects that can improve IR training were · organized rotations of at least 6 months. · structured curriculums with face-to-face feedback. · structured guidance by senior interventionists during procedures. CITATION FORMAT: · Sieren M, Katoh M, Mahnken AH et al. Work and Training Conditions of German Residents and Young Radiologists in Interventional Radiology - A Nationwide Survey. Fortschr Röntgenstr 2022; 194: 1346 - 1357.

Image Fusion Improves Interdisciplinary Communication in the Treatment of Head and Neck Cancer.

INTRODUCTION: The diagnosis and therapy of oral squamous cell carcinoma (Osee) in Germany is according to guidelines and relies on interdisciplinary board meetings. Standard examination techniques are computed tomography (CT) and magnet resonance imaging (MRI). These technologies are used as objective tools for serial presentation in an oncologic board meeting. The presentation of multiple series at different time points can be time consuming and might not often depict a patients case clearly for all involved disciplinaries. A conclusive image fusion could improve the communication. Thus, this study aims to introduce a novel idea of image fusion into the field of craniomaxillofacial surgery in order to ease understanding and improve therapy in complex Osee patients' cases. MATERIALS AND METHODS: Three key data sets of a patient with OSCC at the right tongue have been merged by image fusion of 3 MRi of head and neck with 3 CT thorax and abdomen using Syngo via (Siemens). Fused images were used as at a glance picture for presenting and discussion a patients case. Focus was on presenting a case of a primary manifestation of OSCC with the potential of a local relapse and distant metastases in an interdisciplinary oncol-ogic board meeting. RESULTS: Image fusion enabled to visualize the primary tumor, local relapse as well as distant pulmonary metastasis and within the suprarenal gland, which have been occurred in a linear time line of 13 months. DISCUSSION: Image fusion of different modalities that is CT and MRi, which were gathered at different time points, presents a new approach within the field of craniomaxillofacial surgery and helped to understand cancer localization and relapse at 1 glance. This new approach will enable a compact data set of patients oncological history as a more decisive tool for all involved disciplinaries. CONCLUSIONS: Image fusion might have the potential to become a standard approach in order to ease multiple therapists to make therapy decisions in oncologic board meetings on basis of current three-dimensional ready CT imaging and MRI.

Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave.

OBJECTIVES: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement. MATERIALS AND METHODS: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used. RESULTS: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]. CONCLUSION: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement. KEY POINTS: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.. CITATION FORMAT: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82.

Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. / Phantomstudie zum Vergleich zwischen Computertomografie- und C-Arm-Computertomografie-gesteuertem Punktionsverfahren bei Anwendung durch Weiterbildungsassistenten in der Radiologie.

PURPOSE: Comparison of puncture deviation and puncture duration between computed tomography (CT)- and C-arm CT (CACT)-guided puncture performed by residents in training (RiT). METHODS: In a cohort of 25 RiTs enrolled in a research training program either CT- or CACT-guided puncture was performed on a phantom. Prior to the experiments, the RiT's level of training, experience playing a musical instrument, video games, and ball sports, and self-assessed manual skills and spatial skills were recorded. Each RiT performed two punctures. The first puncture was performed with a transaxial or single angulated needle path and the second with a single or double angulated needle path. Puncture deviation and puncture duration were compared between the procedures and were correlated with the self-assessments. RESULTS: RiTs in both the CT guidance and CACT guidance groups did not differ with respect to radiologic experience (p = 1), angiographic experience (p = 0.415), and number of ultrasound-guided puncture procedures (p = 0.483), CT-guided puncture procedures (p = 0.934), and CACT-guided puncture procedures (p = 0.466). The puncture duration was significantly longer with CT guidance (without navigation tool) than with CACT guidance with navigation software (p < 0.001). There was no significant difference in the puncture duration between the first and second puncture using CT guidance (p = 0.719). However, in the case of CACT, the second puncture was significantly faster (p = 0.006). Puncture deviations were not different between CT-guided and CACT-guided puncture (p = 0.337) and between the first and second puncture of CT-guided and CACT-guided puncture (CT: p = 0.130; CACT: p = 0.391). The self-assessment of manual skills did not correlate with puncture deviation (p = 0.059) and puncture duration (p = 0.158). The self-assessed spatial skills correlated positively with puncture deviation (p = 0.011) but not with puncture duration (p = 0.541). CONCLUSION: The RiTs achieved a puncture deviation that was clinically adequate with respect to their level of training and did not differ between CT-guided and CACT-guided puncture. The puncture duration was shorter when using CACT. CACT guidance with navigation software support has a potentially steeper learning curve. Spatial skills might accelerate the learning of image-guided puncture. KEY POINTS: · The CT-guided and CACT-guided puncture experience of the RiTs selected as part of the program "Researchers for the Future" of the German Roentgen Society was adequate with respect to the level of training.. · Despite the lower collective experience of the RiTs with CACT-guided puncture with navigation software assistance, the learning curve regarding CACT-guided puncture may be faster compared to the CT-guided puncture technique.. · If the needle path is complex, CACT guidance with navigation software assistance might have an advantage over CT guidance.. CITATION FORMAT: · Meine TC, Hinrichs JB, Werncke T et al. Phantom study for comparison between computed tomography- and C-Arm computed tomography-guided puncture applied by residents in radiology. Fortschr Röntgenstr 2022; 194: 272 - 280.

DNA methylation-based prediction of response to immune checkpoint inhibition in metastatic melanoma.

BACKGROUND: Therapies based on targeting immune checkpoints have revolutionized the treatment of metastatic melanoma in recent years. Still, biomarkers predicting long-term therapy responses are lacking. METHODS: A novel approach of reference-free deconvolution of large-scale DNA methylation data enabled us to develop a machine learning classifier based on CpG sites, specific for latent methylation components (LMC), that allowed for patient allocation to prognostic clusters. DNA methylation data were processed using reference-free analyses (MeDeCom) and reference-based computational tumor deconvolution (MethylCIBERSORT, LUMP). RESULTS: We provide evidence that DNA methylation signatures of tumor tissue from cutaneous metastases are predictive for therapy response to immune checkpoint inhibition in patients with stage IV metastatic melanoma. CONCLUSIONS: These results demonstrate that LMC-based segregation of large-scale DNA methylation data is a promising tool for classifier development and treatment response estimation in cancer patients under targeted immunotherapy.

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

Generative Adversarial Networks: A Primer for Radiologists.

Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs). GANs consist of two artificial neural networks that are jointly optimized but with opposing goals. One neural network, the generator, aims to synthesize images that cannot be distinguished from real images. The second neural network, the discriminator, aims to distinguish these synthetic images from real images. These deep learning models allow, among other applications, the synthesis of new images, acceleration of image acquisitions, reduction of imaging artifacts, efficient and accurate conversion between medical images acquired with different modalities, and identification of abnormalities depicted on images. The authors provide an introduction to GANs and adversarial deep learning methods. In addition, the different ways in which GANs can be used for image synthesis and image-to-image translation tasks, as well as the principles underlying conditional GANs and cycle-consistent GANs, are described. Illustrated examples of GAN applications in radiologic image analysis for different imaging modalities and different tasks are provided. The clinical potential of GANs, future clinical GAN applications, and potential pitfalls and caveats that radiologists should be aware of also are discussed in this review. The online slide presentation from the RSNA Annual Meeting is available for this article. ©RSNA, 2021.

Statement of the German Roentgen Society, German Society of Neuroradiology, and Society of German-speaking Pediatric Radiologists on Requirements for the Performance and Reporting of MR Imaging Examinations Outside of Radiology. / Positionspapier der Deutschen Röntgengesellschaft (DRG), der Deutschen Gesellschaft für Neuroradiologie (DGNR) und der Gesellschaft für Pädiatrische Radiologie (GPR) zu den fachlichen Anforderungen an Durchführung und Befundung von MRT-Untersuchungen außerhalb des Fachgebietes Radiologie.

BACKGROUND: Magnetic Resonance Imaging (MRI) is a very innovative, but at the same time complex and technically demanding diagnostic method in radiology. It plays an increasing role in high-quality and efficient patient management. Quality assurance in MRI is of utmost importance to avoid patient risks due to errors before and during the examination and when reporting the results. Therefore, MRI requires higher physician qualification and expertise than any other diagnostic imaging technique in medicine. This holds true for indication, performance of the examination itself, and in particular for image evaluation and writing of the report. In Germany, the radiologist is the only specialist who is systematically educated in all aspects of MRI during medical specialty training and who must document a specified, high number of examinations during this training. However, also non-radiologist physicians are increasingly endeavoring to conduct and bill MRI examinations on their own. METHOD: In this position statement, the following aspects of quality assurance for MRI examinations and billing by radiologists and non-radiologist physician specialists are examined scientifically: Requirements for specialist physician training, MRI risks and contraindications, radiation protection in the case of non-ionizing radiation, application of MR contrast agents, requirements regarding image quality, significance of image artifacts and incidental findings, image evaluation and reporting, interdisciplinary communication and multiple-eyes principle, and impact on healthcare system costs. CONCLUSION: The German Roentgen Society, German Society of Neuroradiology, and Society of German-speaking Pediatric Radiologists are critical with regard to MRI performance by non-radiologists in the interest of quality standards, patient welfare, and healthcare payers. The 24-month additional qualification in MRI as defined by the physician specialization regulations (Weiterbildungsordnung) through the German state medical associations (Landesärztekammern) is the only competence-based and quality-assured training program for board-certified specialist physicians outside radiology. This has to be required as the minimum standard for performance and reporting of MRI exams. Exclusively unstructured MRI training outside the physician specialization regulations has to be strictly rejected for reasons of patient safety. The performance and reporting of MRI examinations must be reserved for adequately trained and continuously educated specialist physicians. KEY POINTS: · MR imaging plays an increasing role due to its high diagnostic value and serves as the reference standard in many indications.. · MRI is a complex technique that implies patient risks in case of inappropriare application or lack of expertise.. · In Germany, the radiologist is the only specialist physician that has been systematically trained in all aspects of MRI such as indication, performance, and reporting of examinations in specified, high numbers.. · The only competence-based and quality-assured MRI training program for specialist physicians outside radiology is the 24-month additional qualification as defined by the regulations through the German state medical associations.. · In view of quality-assurance and patient safety, a finalized training program following the physician specialization regulations has to be required for the performance and reporting of MRI examinations.. CITATION FORMAT: · Hunold P, Bucher AM, Sandstede J et al. Statement of the German Roentgen Society, German Society of Neuroradiology, and Society of German-speaking Pediatric Radiologists on Requirements for the Performance and Reporting of MR Imaging Examinations Outside of Radiology. Fortschr Röntgenstr 2021; 193: 1050 - 1060.

Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.

OBJECTIVES: To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data. METHODS: We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into sub cohorts with or without necessity of intensive care unit (ICU) treatment. Sub cohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test. RESULTS: We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all p < 0.001 if not otherwise stated) with occurrence of ICU stay (R = 0.74), length of ICU stay (R = 0.81), lethal outcome (R = 0.56) and length of hospital stay (R = 0.33, p < 0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R = 0.60), lactate dehydrogenase (LDH) (R = 0.60), troponin (TNTHS) (R = 0.55) and c-reactive protein (CRP) (R = 0.51). Differences (p < 0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher opacity score (12.50 vs. 4.96) and severity of laboratory data changes such as c-reactive protein (11.64 vs. 5.07 mg/dl, p < 0.01). CONCLUSIONS: Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.