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

Bone marrow edema (BME) is a significant imaging finding in musculoskeletal and emergency radiology, often associated with trauma or nontraumatic etiologies such as inflammation, infection, or neoplasms. Magnetic resonance imaging (MRI) remains the gold standard for BME evaluation. However, dual-energy CT (DECT) has emerged as a valuable alternative due to its faster acquisition times, lower costs, and more rapid access in emergency settings (when compared with MRI), facilitating timely decision-making when MRI is impractical or contraindicated. Despite its benefits, accurate interpretation of BME on DECT requires careful understanding of its limitations and potential pitfalls. This article addresses the technical and clinical challenges in DECT-based BME assessment and proposes strategies to enhance diagnostic accuracy.A review of the literature was performed by searching the PubMed and ScienceDirect databases, using the keywords ("DECT" or "Dual-Energy") and ("BME" or "bone marrow edema") and ("musculoskeletal" or "bone" or "skeleton") for the title and abstract query. The inclusion criteria were scientific papers presented in the English language. Exclusion criteria included articles which had no relevant focus on BME and case reports. Of the 168 articles initially identified, 75 were deemed relevant and were reviewed in detail. Insight from this literature search and the authors' clinical experience forms the basis of this review, highlighting key pitfalls and strategies for accurate BME interpretation.DECT provides significant advantages for detecting BME, such as material-specific color overlays and high anatomical correlation. However, key pitfalls include the misinterpretation of artifacts, difficulties in cases of severe displacement or sclerosis, and challenges posed by imaging artifacts in large-sized patients or those with metallic implants. Radiologists can improve diagnostic accuracy by understanding the limitations and pitfalls of DECT, and by adopting the solutions outlined in the article to optimize its use. · DECT effectively identifies BME in both traumatic and non-traumatic conditions, with sensitivity and specificity comparable to magnetic resonance imaging (MRI).. · Key interpretation pitfalls include artifacts from photon starvation, metallic implants, severe displacement, and motion, as well as limitations in algorithm processing.. · Misdiagnoses can arise due to mimics of BME, such as sclerosis, red marrow, or pathological fractures, necessitating clinical and imaging correlation.. · Parameter optimization (e.g., spectral FOV, kernel selection, image calibration) enhances diagnostic accuracy and reduces errors.. · Yap JA, Ong YX, Weber M. Pitfalls in Bone Marrow Edema Interpretation on Dual-Energy CT: Challenges and Solutions. Rofo 2025; DOI 10.1055/a-2653-9256.

Multiple sclerosis (MS) is characterized by clinical and radiological heterogeneity. Recent refinements in the McDonald criteria and the integration of advanced MRI biomarkers, such as the central vein sign and paramagnetic rim lesions, can enhance diagnostic precision but may also push the manual radiological workload to an unsustainable level. This growing diagnostic complexity makes artificial intelligence (AI) a critical area of development in MS radiology to address the dual challenges of feasibility and personalized care.Relevant studies were identified through a literature search in PubMed of articles published between January 1, 2020, and August 31, 2025. Additional studies were included through manual searching of references.This review examines the current landscape of AI applications in this field, with a particular focus on deep learning. It details how AI can automate lesion quantification and aid differential diagnosis, as well as how it is being developed to make the evaluation of complex biomarkers clinically practical. The review also analyzes the emerging evidence for AI in prognostic modelling and treatment optimization. We argue that robust development of AI in MS depends on the integration of multimodal data. Although commercial volumetric tools exist, the integration into clinical practice presents recognized challenges, including the need for large-scale validation datasets and ethical frameworks.AI is thus positioned as an essential technological response to the evolving demands of modern, personalized MS care. · Recent MS criteria create an increasing radiological workload. · Artificial intelligence offers a possible solution. · Robust AI development needs multimodal data integration. · Commercial tools already automate lesion segmentation and volumetric analysis. · Clinical adoption requires large-scale validation and ethical frameworks. · Müller D, Bellenberg B, Lukas C. Artificial Intelligence in Multiple Sclerosis: Possibilities in Radiological Diagnostics and Progression Assessment. Rofo 2024; DOI 10.1055/a-2808-0083.

To assess the diagnostic value of ultrasound vector flow imaging in children with acute gastroenteritis.Pediatric patients diagnosed with acute gastroenteritis at our hospital were recruited. US examinations were carried out using a Recho R9S system. Ultrasound vector flow imaging was utilized to measure the wall shear stress (WSS) and turbulence (Tur) index in the superior mesenteric artery (SMA). A control group of healthy children was established for comparison. Receiver operating characteristic (ROC) curves were constructed to determine the critical values of WSS for predicting acute gastroenteritis.The Tur values, mean WSS, and max WSS values in the disease group were significantly higher than those in the healthy group. ROC analysis indicated that the area under the curve (AUC) reached its maximum when the cutoff values for mean WSS and max WSS were 0.43 Pa and 2.41 Pa, respectively. Among these, the mean WSS exhibited the highest sensitivity and specificity, at 79.6% and 77.4%, respectively.Ultrasound vector flow imaging offers an objective, quantitative, noninvasive, and well-tolerated diagnostic indicator with high clinical value for children with acute gastroenteritis, thus justifying its wider application in clinical practice. · V Flow was first applied to assess the SMA in children with AG.. · In the AG group, both the WSS and Tur intensity were increased.. · The mean WSS exhibited the highest sensitivity and specificity.. · V flow provides valuable guidance for the clinical diagnosis of diseases.. · Zheng R, Lu H, Li Y et al. Evaluation of Wall Shear Stress and Turbulence Characteristics of the Superior Mesenteric Artery in Children with Acute Gastroenteritis by Ultrasound Vector Flow Imaging. Rofo 2026; DOI 10.1055/a-2807-1797.