Since the approval of the first dual-source photon-counting detector CT (PCD-CT) in the fall of 2021, significant insights have been gained in its application for cardiovascular imaging. This review aims to provide a comprehensive overview of the current state of knowledge and the growing body of research literature, illustrating innovative applications and perspectives through case examples.We conducted a structured literature review, identifying relevant studies via Google Scholar and PubMed, using the keywords "photon-counting detector", "cardiovascular CT", "cardiac CT", and "ultra-high-resolution CT". We analyzed studies published since January 2015. Additionally, we integrated our own clinical experiences and case examples.In addition to the well-known benefit of increased temporal resolution offered by dual-source scanners, dual-source PCD-CT provides three key advantages: 1) Optimized geometric dose efficiency with an improved contrast-to-noise ratio, 2) intrinsic spectral sensitivity, and 3) the ability for ultrahigh-resolution CT. This technology enables improved image quality or radiation dose reduction in established cardiovascular protocols. Its use in non-invasive cardiac diagnostics for obese patients, those with a high plaque burden, or after stent implantation appears technically feasible, potentially expanding the scope of CT. The spectral sensitivity also allows tailored image acquisition, reducing metallic artifacts and contrast agent doses in patients with renal impairment. Early studies and clinical experience support these potential applications of PCD-CT in cardiovascular diagnostics, suggesting workflow optimization and improved patient management.However, challenges remain, including high costs, large data volumes, somewhat longer reconstruction times, and technical difficulties in combining spectral sensitivity with ultra-high resolution. Prospective randomized studies with clinical endpoints are lacking to confirm the clear advantage over conventional scanners. Future research should focus on endpoint-based studies and robust cost-benefit analyses to evaluate the potential of this technology and facilitate its evidence-based integration in clinical practice. · Photon-counting detector CT represents a technological advancement in computed tomography.. · Spectral sensitivity enhances iodine signal and minimizes artifacts.. · Ultra-high-resolution CT allows precise imaging, even in stents and advanced sclerosis.. · This technology must be validated through endpoint-based, randomized studies.. · Hagar MT, Schlett CL, Oechsner T et al. Photon-Counting Detector CT: Advances and Clinical Applications in Cardiovascular Imaging. Fortschr Röntgenstr 2024; DOI 10.1055/a-2452-0288.
Chronic pelvic pain may cause a significant reduction in the quality of life for affected women, and the extent of the limitation is often underestimated. Chronic pain can lead to lifelong problems. Currently, PCS is probably significantly underdiagnosed as a cause of chronic pelvic pain in women. To diagnose PCS, other common causes of chronic pelvic pain must be excluded and typical changes in pelvic vein syndrome must be detected, ideally using MR phlebography. If the indication is correct and the procedure is carried out appropriately - ideally by experienced interventionalists certified according to DeGIR/EBIR - the symptoms can be eliminated in the long term with a high success rate and few complications. Simultaneous psychotherapeutic treatment should always be discussed with the patients. · PCS is a common cause of chronic pelvic pain in women.. · Imaging requires Doppler sonography and contrast-enhanced magnetic resonance angiography.. · Typical symptoms and dilated pelvic veins (>8 mm) indicate interventional therapy.. · Pathological pelvic veins are embolized using coils and alcohol foam.. · Published success rates and long-term results suggest propagating interventional therapy.. · Paulus T, Minko P, Petersen T et al. Pelvic venous disorders in women - diagnosis and therapy. Fortschr Röntgenstr 2024; DOI 10.1055/a-2446-0749.
Elbow pain can result in significant morbidity. MRI can help diagnosing the cause of elbow pain.Based on a systematic literature search as well as knowledge gained through frequent participation in conferences dedicated to advances in musculoskeletal imaging, this review aims to give a brief overview of normal anatomy and common pathologies of tendons and ligaments of the elbow on magnetic resonance imaging.Stabilization of the elbow joint is provided by osseous structures and passive ligamentous and active muscular support. Loss of these important stabilizers, due to trauma and overuse, can result in elbow instability. Additional MR views or intra-articular contrast media can be useful for the detection of specific pathologies.MRI is frequently used to detect posttraumatic or chronic conditions, which can lead to posterolateral or posteromedial elbow instability. Knowledge of normal anatomy, variants, pathologies, as well as appropriate imaging is crucial to make the diagnosis. · Epicondylitis occurs due to chronic degeneration with tendinosis and partial tendon tearing and is not related to an acute inflammatory reaction.. · Posterolateral or posteromedial elbow instability can be the result of trauma with loss of passive ligamentous and active muscular stabilization.. · The most common elbow instability is posterolateral rotatory instability with the LUCL being the most important stabilizer affected by injury.. · Schinnerl C, Weber M, Benninger E et al. MRI of the Elbow - Update 2024. Fortschr Röntgenstr 2024; DOI 10.1055/a-2416-1491.
Contrast-enhanced CT is the standard imaging technique in oncological objectives. Rates of missed pathologies depend on work experience of the respective radiologists. Thus the aim of this study is to analyze the eye movements of professionals while reading CT images in order to evaluate whether the eye-fixation patterns and search strategies of experienced radiologists could explain higher detection rates of pathologies and whether such patterns can be learned.Anonymized images of 10 patients were presented to three medical students and six radiologists with different levels of work experience. During image analysis, ocular fixation positions were recorded using an eye-tracking software tool. The CT scans were analyzed retrospectively, considering the individual course of disease with the issue of successful detection of all pathologies. Visual attention and dwell time of ocular fixation on clinically important abnormalities or areas with pathological findings, general search patterns, and time efficiency were assessed. For statistical analysis, interobserver variability and accuracy of lesion detection were evaluated taking into account individual experience.The results revealed that observer sensitivity depends on work experience due to a more systematic order of inspection and a well-known course of disease, e.g. in case of metastatic spread. The areas of missed pathologies mostly included secondary findings. Inexperienced readers changed the stratification considerably more often and required more time for reporting or detecting pathologies.Our results suggest that experienced radiological physicians reduce their amount of missed findings by looking more systematically at images and by applying a more targeted inspection of clinically important regions. · CT interpretation by radiology residents is faster and less error-prone compared to postgraduate residents. · systematic image analysis is trainable. · engrams tend to be acquired through experience. · Kluge S, Schülke C, Ites HC et al. Different Evaluation Strategies of Oncological CT Examinations with Regard to Professional Experience: A Clinical Study Using Eye-tracking. Fortschr Röntgenstr 2024; DOI 10.1055/a-2452-2180.
The evolving field of medical education is being shaped by technological advancements, including the integration of Large Language Models (LLMs) like ChatGPT. These models could be invaluable resources for medical students, by simplifying complex concepts and enhancing interactive learning by providing personalized support. LLMs have shown impressive performance in professional examinations, even without specific domain training, making them particularly relevant in the medical field. This study aims to assess the performance of LLMs in radiology examinations for medical students, thereby shedding light on their current capabilities and implications.This study was conducted using 151 multiple-choice questions, which were used for radiology exams for medical students. The questions were categorized by type and topic and were then processed using OpenAI's GPT-3.5 and GPT- 4 via their API, or manually put into Perplexity AI with GPT-3.5 and Bing. LLM performance was evaluated overall, by question type and by topic.GPT-3.5 achieved a 67.6% overall accuracy on all 151 questions, while GPT-4 outperformed it significantly with an 88.1% overall accuracy (p<0.001). GPT-4 demonstrated superior performance in both lower-order and higher-order questions compared to GPT-3.5, Perplexity AI, and medical students, with GPT-4 particularly excelling in higher-order questions. All GPT models would have successfully passed the radiology exam for medical students at our university.In conclusion, our study highlights the potential of LLMs as accessible knowledge resources for medical students. GPT-4 performed well on lower-order as well as higher-order questions, making ChatGPT-4 a potentially very useful tool for reviewing radiology exam questions. Radiologists should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses. · ChatGPT demonstrated remarkable performance, achieving a passing grade on a radiology examination for medical students that did not include image questions.. · GPT-4 exhibits significantly improved performance compared to its predecessors GPT-3.5 and Perplexity AI with 88% of questions answered correctly.. · Radiologists as well as medical students should be aware of ChatGPT's limitations, including its tendency to confidently provide incorrect responses.. · Gotta J, Le Hong QA, Koch V et al. Large language models (LLMs) in radiology exams for medical students: Performance and consequences. Fortschr Röntgenstr 2024; DOI 10.1055/a-2437-2067.
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