Background Acute thrombotic pulmonary embolism (PE) is a common and potentially fatal event with imaging playing a pivotal role in the diagnosis and management of these patients. Method This review discusses imaging techniques, diagnostic algorithms, imaging findings and endovascular treatment of acute thrombotic PE, and illustrates important differential diagnoses relating to the spectrum of acute non-thrombotic PE and non-embolic pulmonary artery disease. The review emphasizes information relevant for everyday radiological practice and highlights recent advances that can be readily applied in the clinical routine. Results/Conclusion Computed tomography pulmonary angiography (CTPA) is the current reference standard for the diagnosis of acute PE. Ventilation and perfusion (VQ) scanning or – in centers with adequate expertise – magnetic resonance imaging (MRI) is indicated in pregnant or young patients and patients with contraindications to iodinated contrast. Invasive angiography is reserved for patients with intended endovascular treatment. Artifacts, acute non-thrombotic PE, chronic PE and non-embolic pulmonary artery diseases should always be considered as differential diagnoses. Key points: Citation Format

Fibrolamellar hepatocellular carcinoma (FLC) is a rare malignancy occurring in young patients without cirrhosis. Objectives of our study were to analyze contrast material uptake in hepatobiliary phase imaging (HBP) in gadoxetic acid-enhanced liver MRI in patients with FLC and to characterize imaging features in sequence techniques other than HBP. In this retrospective study on histology-proven FLC, contrast material uptake in HBP was quantitatively assessed by calculating the corrected FLC enhancement index (CEI) using mean signal intensities of FLC and lumbar muscle on pre-contrast imaging and HBP, respectively. Moreover, enhancement patterns in dynamic contrast-enhanced MRI and relative signal intensities compared with background liver parenchyma were determined by two radiologists in consensus for HBP, diffusion-weighted imaging using high b-values (DWI), and T2 and T1 weighted pre-contrast imaging. In 6 of 13 patients with FLC gadoxetic acid-enhanced liver MRI was available. The CEI suggested presence of HBP contrast material uptake in all FLCs. A mean CEI of 1.35 indicated FLC signal increase of 35% in HBP compared with pre-contrast imaging. All FLCs were hypointense in HBP compared with background liver parenchyma. Three of 6 FLCs had arterial hyperenhancement and venous wash-out. In DWI and T2 weighted imaging, 5 of 6 FLCs were hyperintense. In T1 weighted imaging, 5 of 6 FLCs were hypointense. Hepatobiliary uptake of gadoxetic acid was quantitatively measurable in all FLCs investigated in our study. The observation of hypointensity of FLCs in HBP compared with background liver parenchyma emphasizes the role of gadoxetic acid-enhanced liver MRI for non-invasive diagnosis of FLC and its importance in the diagnostic work-up of indeterminate liver lesions.

Asynchronous calibration could allow opportunistic screening based on routine CT for early osteoporosis detection. In this phantom study, a bone mineral density (BMD) calibration phantom and multi-energy CT (MECT) phantom were imaged on eight different CT scanners with multiple tube voltages (80–150 kV_p) and image reconstruction settings (e.g. soft/hard kernel). Reference values for asynchronous BMD estimation were calculated from the BMD-phantom and validated with six calcium composite inserts of the MECT-phantom with known ground truth. Relative errors/changes in estimated BMD were calculated and investigated for influence of tube voltage, CT scanner and reconstruction setting. Reference values for 282 acquisitions were determined, resulting in an average relative error between calculated BMD and ground truth of − 9.2% ± 14.0% with a strong correlation (R² = 0.99; p < 0.0001). Tube voltage and CT scanner had a significant effect on calculated BMD (p < 0.0001), with relative differences in BMD of 3.8% ± 28.2% when adapting reference values for tube voltage, − 5.6% ± 9.2% for CT scanner and 0.2% ± 0.2% for reconstruction setting, respectively. Differences in BMD were small when using reference values from a different CT scanner of the same model (0.0% ± 1.4%). Asynchronous phantom-based calibration is feasible for opportunistic BMD assessment based on CT images with reference values adapted for tube voltage and CT scanner model.

Background Sustainability is becoming increasingly important in radiology. Besides climate protection – economic, ecological, and social aspects are integral elements of sustainability. An overview of the scientific background of the sustainability and environmental impact of radiology as well as possibilities for future concepts for more sustainable diagnostic and interventional radiology are presented below.The three elements of sustainability:1. EcologyWith an annually increasing number of tomographic images, Germany is in one of the leading positions worldwide in a per capita comparison. The energy consumption of an MRI system is comparable to 26 four-person households annually. CT and MRI together make a significant contribution to the overall energy consumption of a hospital. In particular, the energy consumption in the idle or inactive state is responsible for a relevant proportion.2. EconomyA critical assessment of the indications for radiological imaging is important not only because of radiation protection, but also in terms of sustainability and “value-based radiology”. As part of the “Choosing Wisely” initiative, a total of 600 recommendations for avoiding unnecessary examinations were compiled from various medical societies, including specific indications in radiological diagnostics.3. Social SustainabilityThe alignment of radiology to the needs of patients and referring physicians is a core aspect of the social component of sustainability. Likewise, ensuring employee loyalty by supporting and maintaining motivation, well-being, and job satisfaction is an essential aspect of social sustainability. In addition, sustainable concepts are of relevance in teaching and research, such as the educational curriculum for residents in radiology, RADUCATION or the recommendations of the International Committee of Medical Journal Editors. Key points Citation Format

Background/Objectives: Despite the growing popularity of large language models (LLMs), there remains a notable lack of research examining their role in workplace management. This study aimed to address this gap by evaluating the performance of ChatGPT-3.5, ChatGPT-4.0, Gemini, and Gemini Advanced as famous LLMs in responding to workplace management questions specific to radiology. Methods: ChatGPT-3.5 and ChatGPT-4.0 (both OpenAI, San Francisco, CA, USA) and Gemini and Gemini Advanced (both Google Deep Mind, Mountain View, CA, USA) generated answers to 31 pre-selected questions on four different areas of workplace management in radiology: (1) patient management, (2) imaging and radiation management, (3) learning and personal development, and (4) administrative and department management. Two readers independently evaluated the answers provided by the LLM chatbots. Three 4-point scores were used to assess the quality of the responses: (1) overall quality score (OQS), (2) understandabilityscore (US), and (3) implementability score (IS). The mean quality score (MQS) was calculated from these three scores. Results: The overall inter-rater reliability (IRR) was good for Gemini Advanced (IRR 79%), Gemini (IRR 78%), and ChatGPT-3.5 (IRR 65%), and moderate for ChatGPT-4.0 (IRR 54%). The overall MQS averaged 3.36 (SD: 0.64) for ChatGPT-3.5, 3.75 (SD: 0.43) for ChatGPT-4.0, 3.29 (SD: 0.64) for Gemini, and 3.51 (SD: 0.53) for Gemini Advanced. The highest OQS, US, IS, and MQS were achieved by ChatGPT-4.0 in all categories, followed by Gemini Advanced. ChatGPT-4.0 was the most consistently superior performer and outperformed all other chatbots (p < 0.001–0.002). Gemini Advanced performed significantly better than Gemini (p = 0.003) and showed a non-significant trend toward outperforming ChatGPT-3.5 (p = 0.056). ChatGPT-4.0 provided superior answers in most cases compared with the other LLM chatbots. None of the answers provided by the chatbots were rated “insufficient”. Conclusions: All four LLM chatbots performed well on workplace management questions in radiology. ChatGPT-4.0 outperformed ChatGPT-3.5, Gemini, and Gemini Advanced. Our study revealed that LLMs have the potential to improve workplace management in radiology by assisting with various tasks, making these processes more efficient without requiring specialized management skills.

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.