European Radiology最新文献

{"title":"ESR Essentials: imaging of middle ear cholesteatoma-practice recommendations by the European Society of Head and Neck Radiology.","authors":"Philip Touska, Steve E J Connor","doi":"10.1007/s00330-024-11021-x","DOIUrl":"10.1007/s00330-024-11021-x","url":null,"abstract":"<p><p>Although non-malignant, middle ear cholesteatoma can result in significant complications due to local bone erosion and infection. The treatment of cholesteatoma is surgical, but residual disease is common and may be clinically occult, particularly when the canal wall is preserved or reconstructive techniques are employed. Imaging plays a pivotal role in the management of patients with middle ear cholesteatoma-aiding clinical diagnosis, identifying complications, planning surgery, and detecting residual disease at follow-up. Computed tomography is the primary imaging tool in the preoperative setting since it can provide both a surgical roadmap and detect erosive complications of cholesteatoma. The ability of magnetic resonance imaging with non-echoplanar diffusion-weighted sequences to accurately detect residual disease has led to a shift in the diagnostic paradigm for post-surgical follow-up of cholesteatoma, such that routine \"second-look\" surgery is no longer required. The following practice recommendations are aimed at helping the radiologist choose appropriate imaging approaches and understand the key diagnostic considerations for the evaluation of pre- and post-surgical middle ear cholesteatoma. KEY POINTS: In the preoperative setting, CT is the first-line imaging modality and MRI is reserved for rare clinical scenarios (low evidence). Non-echoplanar imaging (EPI) DWI is the optimal MRI sequence for the detection of residual cholesteatoma (moderate evidence). Non-EPI DWI plays an important role in the postoperative surveillance of cholesteatoma (moderate evidence).</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2053-2064"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142055288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Further insights into the use of contrast-enhanced imaging for breast cancer follow-up: the pros view.","authors":"Elisabetta Giannotti, Matteo Lambertini","doi":"10.1007/s00330-024-11096-6","DOIUrl":"10.1007/s00330-024-11096-6","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2141-2143"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142461254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Letter to the Editor: Current role of [18 F] FDG-PET/CT in pulmonary sarcoidosis: a meta-analysis.","authors":"Yingwei Guo, Yingying Guan, Jian Liao, Lan Yue, Shengzhang Li","doi":"10.1007/s00330-024-11189-2","DOIUrl":"10.1007/s00330-024-11189-2","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2233-2234"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142766686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"One solid step to general neuroradiology AI.","authors":"Yuanyuan Cui, Jie Feng","doi":"10.1007/s00330-024-11020-y","DOIUrl":"10.1007/s00330-024-11020-y","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1933-1934"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The interaction of lipomatous hypertrophy of the interatrial septum with pericardial adipose tissue biomarkers by computed tomography.","authors":"Pietro G Lacaita, Thomas Senoner, Valentin Bilgeri, Stefan Rauch, Fabian Barbieri, Benedikt Kindl, Fabian Plank, Wolfgang Dichtl, Johannes Deeg, Gerlig Widmann, Gudrun M Feuchtner","doi":"10.1007/s00330-024-11061-3","DOIUrl":"10.1007/s00330-024-11061-3","url":null,"abstract":"<p><strong>Objective: </strong>Novel pericardial adipose tissue imaging biomarkers are currently under investigation for cardiovascular risk stratification. However, a specific compartment of the epicardial adipose tissue (EAT), lipomatous hypertrophy of the interatrial septum (LHIS), is included in the pericardial fat volume (PCFV) quantification software. Our aim was to evaluate LHIS by computed tomography angiography (CTA), to elaborate differences to other pericardial adipose tissue components (EAT) and paracardial adipose tissue (PAT), and to compare CT with [¹⁸F]FDG-PET.</p><p><strong>Materials and methods: </strong>Of 6983 patients screened who underwent coronary CTA for clinical indications, 190 patients with LHIS were finally included (age 62.8 years ± 9.6, 31.6% females, BMI 28.5 kg/cm² ± 4.7) in our retrospective cohort study. CT images were quantified for LHIS, EAT, and PAT density (HU), and total PCFV, with and without LHIS, was calculated. CT was compared with [¹⁸F]FDG-PET if available.</p><p><strong>Results: </strong>CT-density of LHIS was higher (- 22.4 HU ± 22.8) than all other pericardial adipose tissue components: EAT right and left (97.4 HU ± 13 and - 95.1 HU ± 13) PAT right and left (- 107.5 HU ± 13.4 and - 106.3 HU ± 14.5) and PCFV density -83.3 HU ± 5.6 (p < 0.001). There was a mild association between LHIS and PAT right (Beta 0.338, p = 0.006, 95% CI: 0.098-577) and PAT left (Beta 0.249, p = 0.030; 95% CI: 0.024-0.474) but not EAT right (p = 0.325) and left (p = 0.351), and not with total PCFV density (p = 0.164). The segmented LHIS volume comprised 3.01% of the total PCFV, and 4.3% (range, 2.16-11.7%) in those with LHIS > 9 mm. [¹⁸F]FDG-PET: LHIS was tracer uptake positive in 83.3% (37.5%: mild and 45.8%: minimal) of 24 patients.</p><p><strong>Conclusions: </strong>LHIS is a distinct compartment of PCFV with higher density suggesting brown fat and has no consistent association with EAT, but rather with PAT.</p><p><strong>Clinical relevance statement: </strong>LHIS should be recognized as a distinct compartment of the EAT, when using EAT for cardiovascular risk stratification.</p><p><strong>Key points: </strong>LHIS is currently included in EAT quantification software. LHIS density is relatively high, it is not associated with EAT, and has a high [¹⁸F]FDG-PET positive rate suggesting brown fat. LHIS is a distinct compartment of the EAT, and it may act differently as an imaging biomarker for cardiovascular risk stratification.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2189-2199"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914247/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142139715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based lumbar central canal stenosis classification on sagittal MR images is comparable to experienced radiologists using axial images.","authors":"Jasper W van der Graaf, Liron Brundel, Miranda L van Hooff, Marinus de Kleuver, Nikolas Lessmann, Bas J Maresch, Myrthe M Vestering, Jacco Spermon, Bram van Ginneken, Matthieu J C M Rutten","doi":"10.1007/s00330-024-11080-0","DOIUrl":"10.1007/s00330-024-11080-0","url":null,"abstract":"<p><strong>Objectives: </strong>The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs.</p><p><strong>Methods: </strong>A pre-existing 3D AI algorithm was utilized to segment the spinal canal and intervertebral discs (IVDs), enabling quantitative measurements at each IVD level. Four musculoskeletal radiologists graded 683 IVD levels from 186 LCCS patients using the 4-class Lee grading system. A second consensus reading was conducted by readers 1 and 2, which, along with automatic measurements, formed the training dataset for a multiclass (grade 0-3) and binary (grade 0-1 vs. 2-3) random forest classifier with tenfold cross-validation.</p><p><strong>Results: </strong>The multiclass model achieved a Cohen's weighted kappa of 0.86 (95% CI: 0.82-0.90), comparable to readers 3 and 4 with 0.85 (95% CI: 0.80-0.89) and 0.73 (95% CI: 0.68-0.79) respectively. The binary model demonstrated an AUC of 0.98 (95% CI: 0.97-0.99), sensitivity of 93% (95% CI: 91-96%), and specificity of 91% (95% CI: 87-95%). In comparison, readers 3 and 4 achieved a specificity of 98 and 99% and sensitivity of 74 and 54%, respectively.</p><p><strong>Conclusion: </strong>Both the multiclass and binary models, while only using sagittal MR images, perform on par with experienced radiologists who also had access to axial sequences. This underscores the potential of this novel algorithm in enhancing diagnostic accuracy and efficiency in medical imaging.</p><p><strong>Key points: </strong>Question How can the classification of lumbar central canal stenosis (LCCS) be made more efficient? Findings Multiclass and binary AI models, using only sagittal MR images, performed on par with experienced radiologists who also had access to axial sequences. Clinical relevance Our AI algorithm accurately classifies LCCS from sagittal MRI, matching experienced radiologists. This study offers a promising tool for automated LCCS assessment from sagittal T2 MRI, potentially reducing the reliance on additional axial imaging.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2298-2306"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142282498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of ultrasound-guided attenuation parameter and 2D shear wave elastography in patients with metabolic dysfunction-associated steatotic liver disease.","authors":"Roberto Cannella, Francesco Agnello, Giorgia Porrello, Alessandro Umberto Spinello, Giuseppe Infantino, Grazia Pennisi, Daniela Cabibi, Salvatore Petta, Tommaso Vincenzo Bartolotta","doi":"10.1007/s00330-024-11076-w","DOIUrl":"10.1007/s00330-024-11076-w","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the performance and the reproducibility of ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) in patients with biopsy-proven metabolic dysfunction-associated steatotic liver disease (MASLD).</p><p><strong>Methods: </strong>This study included consecutive adult patients with MASLD who underwent ultrasound with UGAP, 2D-SWE and percutaneous liver biopsy. The median values of 12 consecutive UGAP measurements were acquired by two independent radiologists (R1 and R2). Hepatic steatosis was graded by liver biopsy as: (0) < 5%; (1) 5-33%; (2) > 33-66%; (3) > 66%. Areas under the curve (AUCs) were calculated to determine the diagnostic performance. Inter- and intra-observer reliability was assessed with intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>A hundred patients (median age 55.0 years old) with MASLD were prospectively enrolled. At histopathology, 70 and 42 patients had grade ≥ 2 and 3 steatosis, respectively. Median UGAP was 0.78 dB/cm/MHz (IQR/Med: 5.55%). For the diagnosis of grade ≥ 2 steatosis, the AUCs of UGAP were 0.828 (95% CI: 0.739, 0.896) for R1 and 0.779 (95% CI: 0.685, 0.856) for R2. The inter- and intra-operator reliability of UGAP were excellent, with an ICC of 0.92 (95% CI: 0.87-0.95) and 0.95 (95% CI: 0.92-0.96), respectively. The median liver stiffness was 6.76 kPa (IQR/Med: 16.30%). For the diagnosis of advanced fibrosis, 2D-SWE had an AUC of 0.862 (95% CI: 0.757, 0.934), and the optimal cutoff value was > 6.75 kPa with a sensitivity of 80.6% and a specificity of 75.7%.</p><p><strong>Conclusion: </strong>UGAP and 2D-SWE provide a good performance for the staging of steatosis and fibrosis in patients with MASLD with an excellent intra-operator reliability of UGAP.</p><p><strong>Key points: </strong>Question How well do ultrasound-guided attenuation parameter (UGAP) and two-dimensional shear wave elastography (2D-SWE) perform for quantifying hepatic steatosis and fibrosis? Findings UGAP had a maximum AUC of 0.828 for the diagnosis of grade ≥ 2 steatosis, and 2D-SWE had an AUC of 0.862 for diagnosing advanced fibrosis. Clinical relevance UGAP and 2D-SWE allow rapid, reproducible, and accurate quantification of hepatic steatosis and fibrosis that can be used for the noninvasive assessment of patients with metabolic dysfunction-associated steatotic liver disease.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2339-2350"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.","authors":"Sophie Bachanek, Paul Wuerzberg, Lorenz Biggemann, Tanja Yani Janssen, Manuel Nietert, Joachim Lotz, Philip Zeuschner, Alexander Maßmann, Annemarie Uhlig, Johannes Uhlig","doi":"10.1007/s00330-024-11026-6","DOIUrl":"10.1007/s00330-024-11026-6","url":null,"abstract":"<p><strong>Objectives: </strong>To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated confidence to promote clinical applicability.</p><p><strong>Materials and methods: </strong>The training dataset included solid renal tumor patients from two tertiary centers undergoing surgical resection and receiving CT in the corticomedullary or nephrogenic contrast media (CM) phase. Manual tumor segmentation was performed on all axial CT slices serving as reference standard for automatic segmentations. Independent testing was performed on the publicly available KiTS 2019 dataset. Ensembles of neural networks (ENN, DeepLabV3) were used for automatic renal tumor segmentation, and their performance was quantified with DICE score. ENN average foreground entropy measured segmentation confidence (binary: successful segmentation with DICE score > 0.8 versus inadequate segmentation ≤ 0.8).</p><p><strong>Results: </strong>N = 639/n = 210 patients were included in the training and independent test dataset. Datasets were comparable regarding age and sex (p > 0.05), while renal tumors in the training dataset were larger and more frequently benign (p < 0.01). In the internal test dataset, the ENN model yielded a median DICE score = 0.84 (IQR: 0.62-0.97, corticomedullary) and 0.86 (IQR: 0.77-0.96, nephrogenic CM phase), and the segmentation confidence an AUC = 0.89 (sensitivity = 0.86; specificity = 0.77). In the independent test dataset, the ENN model achieved a median DICE score = 0.84 (IQR: 0.71-0.97, corticomedullary CM phase); and segmentation confidence an accuracy = 0.84 (sensitivity = 0.86 and specificity = 0.81). ENN segmentations were visualized with color-coded voxelwise tumor probabilities and thresholds superimposed on clinical CT images.</p><p><strong>Conclusions: </strong>ENN-based renal tumor segmentation robustly performs in external test data and might aid in renal tumor classification and treatment planning.</p><p><strong>Clinical relevance statement: </strong>Ensembles of neural networks (ENN) models could automatically segment renal tumors on routine CTs, enabling and standardizing downstream image analyses and treatment planning. Providing confidence measures and segmentation overlays on images can lower the threshold for clinical ENN implementation.</p><p><strong>Key points: </strong>Ensembles of neural networks (ENN) segmentation is visualized by color-coded voxelwise tumor probabilities and thresholds. ENN provided a high segmentation accuracy in internal testing and in an independent external test dataset. ENN models provide measures of segmentation confidence which can robustly discriminate between successful and inadequate segmentations.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2147-2156"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142035596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing GPT-4 multimodal performance in radiological image analysis.","authors":"Dana Brin, Vera Sorin, Yiftach Barash, Eli Konen, Benjamin S Glicksberg, Girish N Nadkarni, Eyal Klang","doi":"10.1007/s00330-024-11035-5","DOIUrl":"10.1007/s00330-024-11035-5","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical regions, and pathologies to explore the potential of zero-shot generative AI in enhancing diagnostic processes in radiology.</p><p><strong>Methods: </strong>We analyzed 230 anonymized emergency room diagnostic images, consecutively collected over 1 week, using GPT-4V. Modalities included ultrasound (US), computerized tomography (CT), and X-ray images. The interpretations provided by GPT-4V were then compared with those of senior radiologists. This comparison aimed to evaluate the accuracy of GPT-4V in recognizing the imaging modality, anatomical region, and pathology present in the images.</p><p><strong>Results: </strong>GPT-4V identified the imaging modality correctly in 100% of cases (221/221), the anatomical region in 87.1% (189/217), and the pathology in 35.2% (76/216). However, the model's performance varied significantly across different modalities, with anatomical region identification accuracy ranging from 60.9% (39/64) in US images to 97% (98/101) and 100% (52/52) in CT and X-ray images (p < 0.001). Similarly, pathology identification ranged from 9.1% (6/66) in US images to 36.4% (36/99) in CT and 66.7% (34/51) in X-ray images (p < 0.001). These variations indicate inconsistencies in GPT-4V's ability to interpret radiological images accurately.</p><p><strong>Conclusion: </strong>While the integration of AI in radiology, exemplified by multimodal GPT-4, offers promising avenues for diagnostic enhancement, the current capabilities of GPT-4V are not yet reliable for interpreting radiological images. This study underscores the necessity for ongoing development to achieve dependable performance in radiology diagnostics.</p><p><strong>Clinical relevance statement: </strong>Although GPT-4V shows promise in radiological image interpretation, its high diagnostic hallucination rate (> 40%) indicates it cannot be trusted for clinical use as a standalone tool. Improvements are necessary to enhance its reliability and ensure patient safety.</p><p><strong>Key points: </strong>GPT-4V's capability in analyzing images offers new clinical possibilities in radiology. GPT-4V excels in identifying imaging modalities but demonstrates inconsistent anatomy and pathology detection. Ongoing AI advancements are necessary to enhance diagnostic reliability in radiological applications.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1959-1965"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ESR Essentials: advanced MR safety in vulnerable patients-practice recommendations by the European Society for Magnetic Resonance in Medicine and Biology.","authors":"Francesco Santini, Michele Pansini, Xeni Deligianni, Maria Eugenia Caligiuri, Edwin H G Oei","doi":"10.1007/s00330-024-11055-1","DOIUrl":"10.1007/s00330-024-11055-1","url":null,"abstract":"<p><p>For every patient, the MR safety evaluation should include the assessment of risks in three key areas, each corresponding to a specific hazard posed by the electromagnetic fields generated by the MR scanner: ferromagnetic attraction and displacement by the static field; stimulation, acoustic noise, and device interaction by the gradient fields; and bulk and focal heating by the radiofrequency field. MR safety guidelines and procedures are typically designed around the \"average\" patient: adult, responsive, and of typical habitus. For this type of patient, we can safely expect that a detailed history can identify metallic objects inside and outside the body, verbal contact during the scan can detect signs of discomfort from heating or acoustic noise, and safety calculations performed by the scanner can prevent hyperthermia. However, for some less common patient categories, these assumptions do not hold. For instance, patients with larger habitus, febrile patients, or pregnant people are more subject to bulk heating and require more conservative MR protocols, while at the same time presenting challenges during positioning and preparation. Other vulnerable categories are infants, children, and patients unable to communicate, who might require screening for ferromagnetic objects with other imaging modalities or dedicated equipment. This paper will provide guidance to implement appropriate safety margins in the workflow and scanning protocols in various vulnerable patient categories that are sometimes overlooked in basic MR safety guidance documents. CLINICAL RELEVANCE STATEMENT: Special care in the implementation of MR safety procedures is of paramount importance in the handling of patients. While most institutions have streamlined operations in place, some vulnerable patient categories require specific considerations to obtain images of optimal quality while minimizing the risks derived by exposure to the MR environment. KEY POINTS: Patients unable to effectively communicate need to be carefully screened for foreign objects. Core temperature management is important in specific patient categories. There are no hard quantitative criteria that make a patient fall into a specific vulnerable category. Protocols and procedures need to be adaptable.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1785-1793"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11913975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142139713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}