Rofo-fortschritte Auf Dem Gebiet Der Rontgenstrahlen Und Der Bildgebenden Verfahren最新文献

Artificial intelligence (AI) is emerging as a transformative force in radiology, offering the potential to revolutionize the field by enabling sophisticated analysis of complex radiological data and uncovering previously unknown information in medical images.About a decade after the introduction of clinically applicable AI tools, this article explores the current status, opportunities, and limitations of AI integration in radiological practice. We discuss the growing demand for imaging services, increasing complexity of imaging data, and anticipated workforce shortages. Moreover, the role of large language models, computer vision, and automation in improving diagnostic accuracy, workflow efficiency, and patient communication is highlighted. We also examine the evolving European regulatory framework, including the AI Act, MDR (Medical Device Regulation), and EHDS (European Health Data Space), and their implications for the safe and ethical deployment of AI in clinical settings.Radiology, as a highly digitalized and data-rich specialty, is uniquely positioned to benefit from AI-driven innovations across the entire clinical workflow - from patient scheduling to diagnosis and report generation. Challenges, such as the increasing complexity of imaging data or workforce shortages, further underscore the need for selective, well-validated AI-supported solutions. Despite its promise, current limitations such as data quality, model interpretability, or integration barriers, as well as lack of reimbursement, remain critical challenges.This review underscores the need for thoughtful implementation to fully realize AI's potential as an enabling infrastructure in radiology that makes imaging-based healthcare more efficient, accurate, and accessible. · Artificial intelligence is emerging as a transformative force in diagnostic and interventional radiology.. · This article explores the status, opportunities, and limitations of clinically applicable AI tools.. · The article reviews the evolving European regulatory framework for AI deployment.. · The review highlights the need for interdisciplinary collaboration and well-planned AI implementation based on benefits and evidence.. · Bamberg F, Adam G, Antoch G et al. Artificial Intelligence in Radiology: Unlocking New Dimensions of Value. Rofo 2026; DOI 10.1055/a-2794-9496.

Diagnostic reference levels (DRLs) serve to optimize diagnostic and interventional X-ray applications, which account for over 95% of man-made radiation exposure in Germany. The national DRLs are regularly adjusted to reflect advances in device technology and clinical practice. The aim of this article is to present the basic concept of DRLs, their methodological development, and the resulting improvements in patient protection.The relevant regulations of the new radiation protection legislation in Germany are summarized, the development of DRLs between 2003 and 2022 is described, guidance for the practical application of DRLs and the implementation of supplementary facility-specific measures is provided, and finally an outlook on future issues is given.Since the first official announcement of DRLs in Germany, they have been reduced remarkably through continuous protocol optimization and the use of more advanced hardware and software. Over the period of review of almost 20 years, the catalog of X-ray exposures for which DRLs have been established has been expanded considerably, although some DRLs have been removed for various exposures that are no longer routinely indicated.The consistent implementation of the DRL concept has proven effective in practice and has led to a substantial reduction in patient exposure over the years. This positive development in terms of patient protection justifies the personnel effort as well as the potential need to update hardware and software. Despite the success achieved, the DRL concept requires continuous further development in the future. · Diagnostic reference levels (DRLs) are an effective tool to optimize X-ray exposures.. · In Germany, many DRLs have been more than halved in nearly 20 years.. · With good equipment and practice, DRLs can and should be considerably undercut.. · Dose management systems considerably facilitate the practical implementation of the DRL concept.. · The national DLRs should be complemented by facility-specific local DRLs.. · de las Heras Gala H, Giussani A, Schlattl H et al. Reference Levels for Diagnostic and Interventional X-ray Exposures in Germany: Development, Handling, and Perspective. Rofo 2026; DOI 10.1055/a-2790-4742.

Metabolic dysfunction-associated steatohepatitis (MASH) is a progressive liver disease associated with an increased risk of liver fibrosis, liver cirrhosis, and the development of hepatocellular carcinoma. Magnetic resonance elastography (MRE) has been established for non-invasively grading hepatic fibrosis and is increasingly used for assessing inflammation in research studies. The first pharmaceutical treatment of MASH -Resmetirom - was approved in the United States in March 2024 and received approval in Europe in August 2025. This circumstance will increase the demand for clinical and research MRE examinations. Therefore, this scoping review aims to provide an overview of the current research on the use of MRE in MASH for assessing fibrosis and inflammation.A structured literature search was carried out within the MEDLINE and Embase + Embase Classic databases. A total of 11 studies within the last five years were included based on predefined criteria, such as prospective study design, histopathological validation as reference standard or comparisons with other non-invasive procedures.The included studies encompassed diverse populations, methods, and outcome criteria. MRE demonstrated excellent diagnostic performance for liver fibrosis (AUC up to 0.97), outperforming many other non-invasive fibrosis tests. Additionally, preliminary studies suggest good performance for hepatic inflammation (AUC up to 0.83). Multiparametric approaches that combine MRE with other quantitative MRI-based methods, such as proton density fat fraction (PDFF) and corrected T1 (cT1), could further improve diagnostic performance in MASH.These findings show that MRE is a reliable tool for non-invasively assessing hepatic fibrosis in MASH and shows promise to assess inflammation in the future. · MRE shows excellent accuracy for fibrosis and good accuracy for inflammation in MASH.. · Multiparametric MRI improves diagnostic accuracy for steatosis, fibrosis, and inflammation.. · Further research needed to establish MRE as biopsy alternative for MASH.. · Widmann FP, Häseli SP, Schattenfroh J et al. Current Evidence on MR Elastography in Metabolic Dysfunction-Associated Steatohepatitis: A Scoping Review. Rofo 2024; DOI 10.1055/a-2807-9946.

To describe a series of patients with thoracic myelopathy (TM) resulting from ventral spinal cord displacement (VSCD) with special consideration of magnetic resonance imaging (MRI) findings.Retrospective analysis of patients with TM as a consequence of VSCD and consecutive surgery with regard to sex and age, neurological symptoms and, in particular, MRI findings.Inclusion of nine patients (3 women, mean age 52 years [range 28-74]) from a 15-year period (2010-2025). Most of the patients (6/9 [67 %]) were initially symptomatic with Brown-Séquard syndrome. MRI findings demonstrated Th 5/6 as the most affected segment (33%). We found surgically confirmed herniation (78%) more frequent than adhesion (22%). A direct anatomical relationship between the maximum ventral displacement of the spinal cord and an intervertebral disc was found in most cases (89%). Other findings in MRI were duplication of the ventral dura in four patients (44%) and in one case longitudinally extensive syringohydromyelia (11%) as well as focal T2 hyperintensity (11%) and vertebral body fracture (11%). All cases with dural duplication on imaging were associated with cord herniation. Postsurgical improvement of symptoms was achieved in five of nine patients (56 %; range of follow-up period: 3-84 months).VSCD is a rare cause of TM. Ventral cord herniations and adhesions must be differentiated from intradural arachnoid cysts and arachnoid webs, among other conditions. High-resolution MRI allows detection of VSCD and, together with clinical symptoms, helps to determine the indication for surgical treatment. Dural duplication might be a reliable radiological sign of cord herniation on MRI. · Ventral spinal cord displacement is a rare cause of thoracic myelopathy.. · Cord herniations and adhesions must be distinguished from arachnoid cysts and webs.. · MRI is the gold standard for diagnosing ventral spinal cord displacement.. · Dural duplication on MRI may be a radiological sign of cord herniation.. · Neumann A, Gliemroth J, Schramm P et al. Ventral Spinal Cord Displacement as a Cause of Thoracic Myelopathy: A Case Series of Nine Patients with Special Consideration of Magnetic Resonance Imaging Findings. Rofo 2026; DOI 10.1055/a-2810-5055.

To investigate the opportunistic use of diagnostic focused computed tomography (CT) imaging for differentiating intraocular foreign body (FB) types.From 2020 to 2024, 49 eyes of 41 consecutive patients with a diagnosis of FB who underwent diagnostic focused CT were prospectively included in this study. CT studies of patients who had direct contact with a known FB and those who had indirect contact with an FB and then underwent surgery were examined. The volume and density (in HU) of the FBs were measured and statistically analyzed.The type of foreign body was determined by surgery in 34 patients with indirect injuries and by history in 7 patients with direct injuries. Metal was found in 21 eyes, rock in 8 eyes, bullet core in 10 eyes, wood in 2 eyes, glass in 7 eyes, and plastic in 1 eye. Industrial accidents were the cause in 20 patients, traffic accidents in 6 patients, gunshot injuries in 6 patients, and other types of injuries in 9 patients. 21 left eyes, 15 right eyes, and 5 bilateral eyes were affected. Statistical analyses revealed a significant difference in terms of density (HU) between metal and bullet core, rock-bullet core, wood-bullet core, and glass-bullet core (p<0.05).The majority of intraocular FBs were the result of an industrial accident and were found in the left eye. Bilateral eyes were primarily affected by gunshot injuries. Opportunistic use of CT measurements could accurately differentiate bullet core from metal, glass, stone, wood, or plastic. Left eye protection is particularly important in industrial labor. · Identification of the material in addition to the site is a significant concern for ophthalmologists.. · CT is the method of choice for the imaging of intraocular foreign bodies.. · Beam hardening artifacts accurately differentiate any metal objects and gunshot bullets from other intraocular foreign bodies with a hyperdense appearance.. · Opportunistic use of routine clinical care CT scans by measuring CT densities aids preoperative differentiation of gunshot bullets from other metal objects.. · Erdem Toslak İ, Erol MK, Karagol Aydinyurt G et al. Use of Opportunistic Computed Tomography for Differentiating Intraocular Foreign Bodies. Rofo 2026; DOI 10.1055/a-2781-9014.

3D bone surface model reconstructions of CT exams are used for surgical planning. However, the accuracy of these models in tin-filtered ultra-low-dose CT examinations is unknown. Thus, we evaluated the accuracy of 3D-reconstructed bone models obtained from tin-filtered ultra-low-dose CT (tfCT) compared to standard CT (sCT) and calculated the radiation dose reduction.IRB-approved prospective study with informed consent. Patients undergoing a pelvic sCT examination were scanned additionally with tfCT. 3D surface model reconstructions (left and right hemipelvis, and left and right proximal femur) of both scans were created, for each patient independently by two readers. The average error between the surface models of both methods was obtained using root mean square error (RMSE). The dose parameters that were used were volume CT dose index (CTDIvol) and dose length product (DLP). Radiation dose differences of both scans were evaluated using the Wilcoxon signed-rank test.23 subjects (9 males and 14 females; mean age 31±8 years) were included. The mean RMSE was 0.47-0.48 mm for the right hemipelvis (left hemipelvis: 0.48-0.5 mm) and 0.52-0.56 for the proximal right femur (left femur: 0.5-0.57 mm). The average CTDIvol was 8.19±4.45 mGy for sCT examinations and 1.09±0.02 mGy for tfCT exams. The mean DLP was 220.45±124.36 mGy*cm for sCT examinations, and 29.12±1.54 mGy*cm for tfCT exams. The average CTDIvol and DLP were substantially lower (-87%) for tfCT compared to sCT (p<0.001).3D surface model reconstruction from tfCT is feasible and has similar accuracy to that of sCT but with a substantially lower radiation dose. · Pelvic 3D bone surface model reconstructions are feasible with tin-filtered ultra-low-dose CT.. · Reconstructions from tin-filtered ultra-low-dose CT have a similar accuracy as standard CT.. · Tin-filtered ultra-low-dose CT has a significantly lower radiation dose than standard CT.. · Falkowski AL, Carrillo F, Novina D et al. 3D bone reconstruction of the pelvis and hip through segmentation of tin-filtered ultra-low-dose CT compared with standard CT - a feasibility study. Rofo 2026; DOI 10.1055/a-2811-9609.

Digital breast tomosynthesis (DBT) increases sensitivity and specificity compared to digital mammography (DM) in the early detection of breast cancer. The potential of artificial intelligence (AI) for mammographic interpretation resulting in workload reduction is increasingly being reported.Presentation of the current evidence on the efficacy of DBT versus DM in population-based breast cancer screening and its support by AI. Narrative review with topic-led literature search of comparative studies of DBT and DM and review of the use of AI in PubMed from 01/2016 to 09/2025.In 42 international studies, the breast cancer detection rate was significantly higher with DBT + DM (6.4‰) and DBT + synthetic mammography (SM) (7.4‰) than with DM (4.7‰). Concordantly, the randomized TOSYMA study reported a higher invasive breast cancer detection rate with DBT + SM (7.1‰) versus DM (4.8‰) with a lower false-positive recall rate (first round -15.6‰). The positive predictive value (PPV) of recall was - consistent with meta-analyses - higher (+4.9%), as was the reading time. With a smaller number of AI studies on DBT than DM, a DBT meta-analysis reported a higher sensitivity of AI alone (89%) than by readers (78%), with a lower specificity of AI. DBT with AI-assisted reading compared to human reading alone increased the detection rate in a prospective study by +3.8‰ without a marked change in the recall rate (+0.8%).DBT increases breast cancer detection compared to DM with more favorable process parameters. AI reporting strategies can further increase sensitivity and reduce human workload. The influence of DBT and AI on reducing the interval cancer rate as a measure of efficiency has not yet been proven. · DBT implementation in population-based breast cancer screening is being reviewed internationally.. · In case of DM replacement, AI reading concepts will become more important.. · Evidence of the long-term effectiveness of DBT and AI is limited.. · Weigel S, Wunderlich P, Sommer A et al. Digital breast tomosynthesis and reading with artificial intelligence. Rofo 2026; DOI 10.1055/a-2796-5225.

Identifying completely unknown individuals is a major challenge in forensic and emergency medicine. Radiology offers a promising solution by using unique anatomical features on medical images to identify both living and deceased persons. Although emergency or postmortem images could be matched against large clinical databases, such applications remain largely experimental. This review examines current methods in automatic radiology-based personal identification, evaluates their performance, and highlights potential applications in forensic and clinical settings.A narrative review of studies published from 2018 onwards was conducted using PubMed and Google Scholar. Included studies applied automated or semi-automated personal identification to panoramic radiographs (PR) or computed tomography (CT) using reference datasets. A narrative approach was used to synthesize results descriptively due to heterogeneity in study design, dataset size, and methodology.Of the 32 included studies, 15 focused on PR-to-PR, 8 on head CT-to-CT, 7 on body CT-to-CT, and 2 on CT-to-PR identification. The most commonly applied approach was descriptor-based computer vision (CV), used in 9 studies. Deep learning was applied in 8 studies for feature extraction, and in 2 studies each for classification and bone segmentation.Several methods perform well in controlled settings. Descriptor-based CV provides the most flexibility and strongest evidence, especially for large-database comparisons and postmortem applications. Deep learning approaches, including feature extraction, classification, and automatic bone segmentation, also show promise for cross-individual matching but require further validation. Automatic radiology-based personal identification holds significant potential for forensic and clinical use, yet the development of standardized large-scale reference databases and robust automated pipelines remains a key challenge. · Radiological images enable automated personal identification of unknown individuals.. · Descriptor-based computer vision is flexible and robust for large database matching.. · Deep learning shows promise for cross-individual matching, but requires further validation.. · Postmortem applications are feasible, yet under-investigated.. · Ethical frameworks are necessary for handling sensitive imaging data.. · Heinrich A. A Review on Automatic Personal Identification Using Panoramic Radiographs and Computed Tomography. Rofo 2026; DOI 10.1055/a-2808-8851.