European Radiology最新文献

{"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}

{"title":"Speed and efficiency: evaluating pulmonary nodule detection with AI-enhanced 3D gradient echo imaging.","authors":"Sebastian Ziegelmayer, Alexander W Marka, Maximilian Strenzke, Tristan Lemke, Hannah Rosenkranz, Bernadette Scherer, Thomas Huber, Kilian Weiss, Marcus R Makowski, Dimitrios C Karampinos, Markus Graf, Joshua Gawlitza","doi":"10.1007/s00330-024-11027-5","DOIUrl":"10.1007/s00330-024-11027-5","url":null,"abstract":"<p><strong>Objectives: </strong>Evaluating the diagnostic feasibility of accelerated pulmonary MR imaging for detection and characterisation of pulmonary nodules with artificial intelligence-aided compressed sensing.</p><p><strong>Materials and methods: </strong>In this prospective trial, patients with benign and malignant lung nodules admitted between December 2021 and December 2022 underwent chest CT and pulmonary MRI. Pulmonary MRI used a respiratory-gated 3D gradient echo sequence, accelerated with a combination of parallel imaging, compressed sensing, and deep learning image reconstruction with three different acceleration factors (CS-AI-7, CS-AI-10, and CS-AI-15). Two readers evaluated image quality (5-point Likert scale), nodule detection and characterisation (size and morphology) of all sequences compared to CT in a blinded setting. Reader agreement was determined using the intraclass correlation coefficient (ICC).</p><p><strong>Results: </strong>Thirty-seven patients with 64 pulmonary nodules (solid n = 57 [3-107 mm] part-solid n = 6 [ground glass/solid 8 mm/4-28 mm/16 mm] ground glass nodule n = 1 [20 mm]) were analysed. Nominal scan times were CS-AI-7 3:53 min; CS-AI-10 2:34 min; CS-AI-15 1:50 min. CS-AI-7 showed higher image quality, while quality remained diagnostic even for CS-AI-15. Detection rates of pulmonary nodules were 100%, 98.4%, and 96.8% for CS-AI factors 7, 10, and 15, respectively. Nodule morphology was best at the lowest acceleration and was inferior to CT in only 5% of cases, compared to 10% for CS-AI-10 and 23% for CS-AI-15. The nodule size was comparable for all sequences and deviated on average < 1 mm from the CT size.</p><p><strong>Conclusion: </strong>The combination of compressed sensing and AI enables a substantial reduction in the scan time of lung MRI while maintaining a high detection rate of pulmonary nodules.</p><p><strong>Clinical relevance statement: </strong>Incorporating compressed sensing and AI in pulmonary MRI achieves significant time savings without compromising nodule detection or characteristics. This advancement holds clinical promise, enhancing efficiency in lung cancer screening without sacrificing diagnostic quality.</p><p><strong>Key points: </strong>Lung cancer screening by MRI may be possible but would benefit from scan time optimisation. Significant scan time reduction, high detection rates, and preserved nodule characteristics were achieved across different acceleration factors. Integrating compressed sensing and AI in pulmonary MRI offers efficient lung cancer screening without compromising diagnostic quality.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"2237-2244"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11914225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141995559","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":"Site effects in multisite fetal brain MRI: morphological insights into early brain development.","authors":"Xinyi Xu, Cong Sun, Hong Yu, Guohui Yan, Qingqing Zhu, Xianglei Kong, Yibin Pan, Haoan Xu, Tianshu Zheng, Chi Zhou, Yutian Wang, Jiaxin Xiao, Ruike Chen, Mingyang Li, Songying Zhang, Hongjie Hu, Yu Zou, Jingshi Wang, Guangbin Wang, Dan Wu","doi":"10.1007/s00330-024-11084-w","DOIUrl":"10.1007/s00330-024-11084-w","url":null,"abstract":"<p><strong>Objective: </strong>To evaluate multisite effects on fetal brain MRI. Specifically, to identify crucial acquisition factors affecting fetal brain structural measurements and developmental patterns, while assessing the effectiveness of existing harmonization methods in mitigating site effects.</p><p><strong>Materials and methods: </strong>Between May 2017 and March 2022, T2-weighted fast spin-echo sequences in-utero MRI were performed on healthy fetuses from retrospectively recruited pregnant volunteers on four different scanners at four sites. A generalized additive model (GAM) was used to quantitatively assess site effects, including field strength (FS), manufacturer (M), in-plane resolution (R), and slice thickness (ST), on subcortical volume and cortical morphological measurements, including cortical thickness, curvature, and sulcal depth. Growth models were selected to elucidate the developmental trajectories of these morphological measurements. Welch's test was performed to evaluate the influence of site effects on developmental trajectories. The comBat-GAM harmonization method was applied to mitigate site-related biases.</p><p><strong>Results: </strong>The final analytic sample consisted of 340 MRI scans from 218 fetuses (mean GA, 30.1 weeks ± 4.4 [range, 21.7-40 weeks]). GAM results showed that lower FS and lower spatial resolution led to overestimations in selected brain regions of subcortical volumes and cortical morphological measurements. Only the peak cortical thickness in developmental trajectories was significantly influenced by the effects of FS and R. Notably, ComBat-GAM harmonization effectively removed site effects while preserving developmental patterns.</p><p><strong>Conclusion: </strong>Our findings pinpointed the key acquisition factors in in-utero fetal brain MRI and underscored the necessity of data harmonization when pooling multisite data for fetal brain morphology investigations.</p><p><strong>Key points: </strong>Question How do specific site MRI acquisition factors affect fetal brain imaging? Finding Lower FS and spatial resolution overestimated subcortical volumes and cortical measurements. Cortical thickness in developmental trajectories was influenced by FS and in-plane resolution. Clinical relevance This study provides important guidelines for the fetal MRI community when scanning fetal brains and underscores the necessity of data harmonization of cross-center fetal studies.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1830-1842"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142282502","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":"Low dose optimization for total-body 2-[¹⁸F]FDG PET/CT imaging: a single-center study on feasibility based on body mass index stratification.","authors":"Taoying Gu, Siwei Liu, Xiaoguang Hou, Liwei Zhao, Yee Ling Ng, Jingyi Wang, Hongcheng Shi","doi":"10.1007/s00330-024-11039-1","DOIUrl":"10.1007/s00330-024-11039-1","url":null,"abstract":"<p><strong>Objectives: </strong>Implementing personalization protocol in clinical routine necessitates diverse low-dose PET/CT scan protocols. This study explores the clinical feasibility of one-third (1/3) dose regimen and evaluates the diagnostic image quality and lesion detectability of BMI-based 1/3-injection doses for 2-[¹⁸F]FDG PET/CT imaging.</p><p><strong>Methods: </strong>Seventy-four cancer patients underwent total-body 2-[¹⁸F]FDG PET/CT examination, with 37 retrospectively enrolled as full-dose group (3.7 MBq/kg) and 37 prospectively enrolled as the 1/3-dose group (1.23 MBq/kg). The 1/3-dose group was stratified by BMI, with an acquisition time of 5 min (G5), 6 min (G6), and 8 min (G8) for BMI < 25, 25 ≤ BMI ≤ 29, and BMI > 29, respectively. Image quality was subjectively and objectively assessed, and lesion detectability was quantitatively analyzed.</p><p><strong>Results: </strong>Subjective assessments of 1/3-dose and full-dose PET images showed strong agreement among readers (κ > 0.88). In the 1/3-dose group, the Likert scores were above 4. G5, G6, and G8 showed comparable image quality, with G5 demonstrating higher lesion conspicuity than G6 and G8 (p = 0.045). Objective evaluation showed no significant differences in SUV_max, liver SUV_mean and TBR between 1/3- and full-dose groups (p > 0.05). No statistical differences were observed in the SUV_max of primary tumor, SUV_mean of liver and TBR across all BMI categories between the 1/3-dose and full-dose groups. Lesion detection rates showed no significant difference between the 1/3-dose (93.24%, 193/207) and full-dose groups (94.73%, 198/209) (p = 0.520).</p><p><strong>Conclusion: </strong>A BMI-stratified 1/3-dose regimen is a feasible low-dose alternative with clinically acceptable lesion detectability equivalent to full-dose protocol, potentially expanding the applicability of personalized protocols.</p><p><strong>Clinical relevance statement: </strong>This study demonstrated that BMI-stratified 1/3-dose regimens for [¹⁸F]FDG total-body PET/CT yielded equivalent outputs compared to the full-dose regimen, which aligns with clinical needs for personalization in dose and BMI.</p><p><strong>Key points: </strong>Currently, limited personalized low-dose total-body PET/CT protocols are available, particularly for patients with varied BMI. Reducing the radiotracer dose to 1/3 the standard demonstrated comparable image quality and lesion detectability equivalent to full dose. BMI-stratified 1/3-dose regimen is a clinically feasible low-dose alternative.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1881-1893"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142105760","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":"Unlocking the future of leukodystrophy diagnosis: the promise and challenges of quantitative MRI.","authors":"Loukas G Astrakas","doi":"10.1007/s00330-024-11184-7","DOIUrl":"10.1007/s00330-024-11184-7","url":null,"abstract":"","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"1843-1844"},"PeriodicalIF":4.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142603779","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}