Neuroradiology最新文献

{"title":"Magnetic resonance imaging morphological features of the cisternal segment of the trigeminal nerve in Fabry disease.","authors":"Taku Gomi, Satoshi Matsushima, Akira Baba, Toshiki Tsunogai, Tetsuya Shimizu, Hideto Kuribayashi, Hikaru Nishida, Ken Sakurai, Masahisa Kobayashi, Hiroshi Kobayashi, Hiroya Ojiri","doi":"10.1007/s00234-025-03720-z","DOIUrl":"https://doi.org/10.1007/s00234-025-03720-z","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the magnetic resonance imaging (MRI) characteristics of the trigeminal nerve in Fabry disease (FD) by comparing the morphology of the trigeminal nerve in patients with and without FD.</p><p><strong>Methods: </strong>This retrospective study included 40 patients with FD and 40 age- and sex-matched controls who underwent 3T MRI with constructive interference in steady-state sequence. Two neuroradiologists measured the short-axis length on axial and oblique coronal images and the long-axis length and cross-sectional area on oblique coronal images of the cisternal segment of the trigeminal nerve bilaterally. Morphological differences were assessed between groups and between sides within each group using appropriate t-tests.</p><p><strong>Results: </strong>All measurements were significantly smaller in the FD group than in the control group: axial images: short-axis length (3.20 ± 0.52 mm vs. 3.51 ± 0.51 mm, p = 0.0101); oblique coronal images: short-axis length (2.41 ± 0.28 mm vs. 2.61 ± 0.35 mm, p = 0.007); long-axis length (3.75 ± 0.53 mm vs. 4.14 ± 0.44 mm, p < 0.001); and cross-sectional area (7.71 ± 1.68 mm² vs. 8.93 ± 1.30 mm², p < 0.001). Left-right differences were observed in both groups; the left side was generally larger. Receiver operating characteristic curve analysis showed moderate diagnostic performance (area under the curve, 0.66-0.72).</p><p><strong>Conclusion: </strong>The trigeminal nerves in the cisternal region were significantly smaller in patients with FD than in those without FD. These findings may support its potential utility as an imaging biomarker.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144743341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sex-specific trajectories of hippocampal aging: structural changes and asymmetry across the lifespan.","authors":"Sebastiano Vacca, Antonella Balestrieri, Carola Politi, Alessandra Serra, Luca Saba","doi":"10.1007/s00234-025-03717-8","DOIUrl":"https://doi.org/10.1007/s00234-025-03717-8","url":null,"abstract":"","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144743342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-modality image-to-image translation from MR to synthetic ¹⁸F-FDOPA PET/MR fusion images using conditional GAN in brain cancer.","authors":"Youngbeom Seo, Heesung Yang, Eunjung Kong, Vivek Sanker, Atman Desai, Jungwon Lee, So Hee Park, You Seon Song, Ikchan Jeon","doi":"10.1007/s00234-025-03704-z","DOIUrl":"https://doi.org/10.1007/s00234-025-03704-z","url":null,"abstract":"<p><strong>Objective: </strong>This study aims to identify the possibility of cross-modality image-to-image translation from magnetic resonance (MR) to synthetic positron emission tomography (PET)/MR fusion images using conditional generative adversarial networks (CGAN).</p><p><strong>Methods: </strong>Retrospective study was conducted involving 32 simultaneous 6-[¹⁸F]-fluoro-L-3,4-dihydroxyphenylalanine (¹⁸F-FDOPA) PET/MR imaging examinations from 27 patients diagnosed with brain cancer. We applied paired axial T1-weighted contrast MR (T1C) and PET/T1C fusion images to translate from T1C to synthetic PET/T1C fusion images using the Pix2Pix algorithm of CGAN. To access the image similarity between real and synthetic PET/T1C fusion images, we calculated correlation coefficients for the maximum/mean tumor-to-background ratio (TBR_max/mean) and quantitative analyses were performed using peak signal-to-noise ratio (PSNR), mean squared error (MSE), structural similarity index (SSIM), and feature similarity index measure (FSIM).</p><p><strong>Results: </strong>Total 2167 pairs of T1C and PET/T1C fusion images were obtained, which were randomly assigned to training and test datasets in 9:1 ratio (1950 and 217 pairs), and training data were further divided into training and validation datasets in 4:1 ratio (1560 and 390 pairs). The correlation coefficients were 0.706 (CI:0.533-0.822) for TBR_max (p < 0.001) and 0.901 (CI:0.831-0.943) for TBR_mean (p < 0.001). The quantitative analyses were PSNR of 31.075 ± 3.976, MSE of 0.001 ± 0.001, SSIM of 0.868 ± 0.079, and FSIM of 0.922 ± 0.044, respectively.</p><p><strong>Conclusion: </strong>CGAN based on simultaneous ¹⁸F-FDOPA PET/MR imaging data demonstrated the potential for cross-modality image-to-image translation from T1C to PET/T1C fusion images, though limitations in small dataset and lack of external validation requiring further research.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144668119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating brain tumor classification using MRI: a scientometric analysis of selected articles from 2015 to 2024.","authors":"Gunde Mounika, Sreedhar Kollem, Srinivas Samala","doi":"10.1007/s00234-025-03685-z","DOIUrl":"https://doi.org/10.1007/s00234-025-03685-z","url":null,"abstract":"<p><strong>Background: </strong>Magnetic resonance imaging (MRI) is a non-invasive method widely used to evaluate abnormal tissues, especially in the brain. While many studies have examined brain tumor classification using MRI, a comprehensive scientometric analysis remains limited.</p><p><strong>Objective: </strong>This study aimed to investigate brain tumor classification based on magnetic resonance imaging (MRI), using scientometric approaches, from 2015 to 2024.</p><p><strong>Methods: </strong>A total of 348 peer-reviewed articles were extracted from the Scopus database. Tools such as CiteSpace and VOSviewer were employed to analyze key metrics, including citation frequency, author collaboration, and publication trends.</p><p><strong>Results: </strong>The analysis revealed top authors, top-cited journals, and international collaborations. Co-occurrence networks identified the top research topics and bibliometric coupling revealed knowledge advancements in the domain.</p><p><strong>Conclusion: </strong>Deep learning methods are increasingly used in brain tumor classification research. This study outlines the current trends, uncovers research gaps, and suggests future directions for researchers in the domain of MRI-based brain tumor classification.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144659750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinguishing symptomatic and asymptomatic trigeminal nerves through radiomics and deep learning: A microstructural study in idiopathic TN patients and asymptomatic control group.","authors":"Ferhat Cüce, Gokalp Tulum, Ömer Karadaş, Muhammet İkbal Işik, Merve Dur İnce, Sajjad Nematzadeh, Marziye Jalili, Niray Baş, Berza Özcan, Onur Osman","doi":"10.1007/s00234-025-03691-1","DOIUrl":"https://doi.org/10.1007/s00234-025-03691-1","url":null,"abstract":"<p><strong>Purpose: </strong>The relationship between mild neurovascular conflict (NVC) and trigeminal neuralgia (TN) remains ill-defined, especially as mild NVC is often seen in asymptomatic population without any facial pain. We aim to analyze the trigeminal nerve microstructure using artificial intelligence (AI) to distinguish symptomatic and asymptomatic nerves between idiopathic TN (iTN) and the asymptomatic control group with incidental grade‑1 NVC.</p><p><strong>Methods: </strong>Seventy-eight symptomatic trigeminal nerves with grade-1 NVC in iTN patients, and an asymptomatic control group consisting of Bell's palsy patients free from facial pain (91 grade-1 NVC and 91 grade-0 NVC), were included in the study. Three hundred seventy-eight radiomic features were extracted from the original MRI images and processed with Laplacian-of-Gaussian filters. The dataset was split into 80% training/validation and 20% testing. Nested cross-validation was employed on the training/validation set for feature selection and model optimization. Furthermore, using the same pipeline approach, two customized deep learning models, Dense Atrous Spatial Pyramid Pooling (ASPP) -201 and MobileASPPV2, were classified using the same pipeline approach, incorporating ASPP blocks.</p><p><strong>Results: </strong>Performance was assessed over ten and five runs for radiomics-based and deep learning-based models. Subspace Discriminant Ensemble Learning (SDEL) attained an accuracy of 78.8%±7.13%, Support Vector Machines (SVM) reached 74.8%±9.2%, and K-nearest neighbors (KNN) achieved 79%±6.55%. Meanwhile, DenseASPP-201 recorded an accuracy of 82.0 ± 8.4%, and MobileASPPV2 achieved 73.2 ± 5.59%.</p><p><strong>Conclusion: </strong>The AI effectively distinguished symptomatic and asymptomatic nerves with grade‑1 NVC. Further studies are required to fully elucidate the impact of vascular and nonvascular etiologies that may lead to iTN.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"European Society of Neuroradiology (ESNR).","authors":"","doi":"10.1007/s00234-025-03681-3","DOIUrl":"10.1007/s00234-025-03681-3","url":null,"abstract":"","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual- energy CT versus single-energy CT for estimation of hematocrit and hemoglobin in the brain: an in vivo analysis.","authors":"Richard Dagher, Megan C Jacobsen, Satvik Vasireddy, Rick R Layman, Dong-Eog Kim, Max Wintermark, Dawid Schellingerhout","doi":"10.1007/s00234-025-03700-3","DOIUrl":"https://doi.org/10.1007/s00234-025-03700-3","url":null,"abstract":"<p><strong>Purpose: </strong>Hematocrit (Hct) and hemoglobin (HB) levels in blood are known to be correlated with vascular attenuation values on single-energy computed tomography (SECT). Dual-energy computed tomography (DECT) is likely to have even better correlations than SECT, given its richer information content, but this remains unproven clinically. We compare and contrast DECT and SECT correlations between attenuation in the superior sagittal sinus (SSS) to patient Hct/HB levels, and explore the use of iodine/water decomposition maps for the same purpose.</p><p><strong>Methods: </strong>Brain SECT and DECT were acquired contemporaneously in 83 patients and attenuation was measured in the SSS on SECT, monoenergetic DECT images (40 to 140 keV in 5 keV increments) and DECT material decomposition images (water and iodine). Hct/HB values were from complete blood counts (CBC) within 30 days of imaging. Linear regressions were performed to Hct/HB using the measured attenuations as explanatory variables.</p><p><strong>Results: </strong>Hct and HB were strongly mutually correlated (r = 0.964). Hct/HB were moderately correlated (r = 0.493/0.458) with SSS attenuation on SECT, and moderately to strongly correlated for DECT (Pearson's r ranging 0.331-0.656) over a range of monoenergetic levels (40 to 140 keV). Above 60 keV, DECT monoenergetic images were better correlated to Hct/HB than SECT, with correlation maximized at 95 keV (r = 0.656, p < 0.001). Material decomposition water images had moderate correlation (r = 0.51), improving to strong correlation (r = 0.659) for a two-variable water and iodine regression, similar to the monoenergetic results.</p><p><strong>Conclusion: </strong>DECT has better correlations to Hct/HB than SECT for all monoenergetic energies above 60 keV, with best correlations at 95 keV.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144626706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neuroimaging characteristics of single Large-Scale mitochondrial DNA deletion syndromes.","authors":"Tamer Sobeh, Tal Granek, Omer Bar-Yosef, Elad Jacoby, Chen Hoffmann, Shai Shrot","doi":"10.1007/s00234-025-03689-9","DOIUrl":"https://doi.org/10.1007/s00234-025-03689-9","url":null,"abstract":"<p><strong>Background and purpose: </strong>Single large-scale mitochondrial DNA deletion syndromes (SLSMDSs) are rare mitochondrial disorders that present a continuum of phenotypes, including Pearson syndrome, Kearns-Sayre syndrome, and progressive external ophthalmoplegia. Neuroimaging findings in SLSMDSs are underreported, and their role in diagnosis and disease monitoring remains inadequately defined. This study aims to characterize clinical features and analyze neuroimaging findings, including spectroscopy and diffusion imaging, in patients with SLSMDSs.</p><p><strong>Methods: </strong>A retrospective review of 11 patients diagnosed with SLSMDSs at a tertiary referral center between 2013 and 2024 was conducted. Clinical, genetic, and neuroimaging data were analyzed. MRI scans were reviewed for abnormalities in various brain regions, including white matter, basal ganglia, thalami, corpus callosum, cerebellum, and brainstem.</p><p><strong>Results: </strong>The cohort had a mean age of 8.3 years (63.6% female). MRI was normal in 4 patients. Among the remaining 7, symmetrical T2/FLAIR hyperintensities, with or without diffusion alterations, were frequently observed, involving the dorsal brainstem in 7/7 and the cerebellum in 6/7 of patients. Globi pallidi involvement was also present in 6 of 7 patients. MR basal ganglia spectroscopy demonstrated elevated lactate in 3 of 7 patients with available spectroscopy. Subcortical and deep white matter abnormalities were identified in 3 patients, sparing the periventricular regions. Imaging progression was noted in patients with serial studies (4 patients).</p><p><strong>Conclusions: </strong>Neuroimaging in SLSMDSs typically demonstrates characteristic involvement of the dorsal brainstem, cerebellum, and basal ganglia, and may show diffusion alterations, a finding suggestive of metabolic injury. The observed pattern of subcortical white matter involvement with periventricular sparing may aid in differentiating this disorder from others. Normal imaging may be present in early or less severe disease. MRI, including diffusion imaging and spectroscopy, can support diagnosis and longitudinal monitoring.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MRI-based interpretable clinicoradiological and radiomics machine learning model for preoperative prediction of pituitary macroadenomas consistency: a dual-center study.","authors":"Meiheng Liang, Fei Wang, Yan Yang, Li Wen, Shunan Wang, Dong Zhang","doi":"10.1007/s00234-025-03698-8","DOIUrl":"https://doi.org/10.1007/s00234-025-03698-8","url":null,"abstract":"<p><strong>Purpose: </strong>To establish an interpretable and non-invasive machine learning (ML) model using clinicoradiological predictors and magnetic resonance imaging (MRI) radiomics features to predict the consistency of pituitary macroadenomas (PMAs) preoperatively.</p><p><strong>Methods: </strong>Total 350 patients with PMA (272 from Xinqiao Hospital of Army Medical University and 78 from Daping Hospital of Army Medical University) were stratified and randomly divided into training and test cohorts in a 7:3 ratio. The tumor consistency was classified as soft or firm. Clinicoradiological predictors were examined utilizing univariate and multivariate regression analyses. Radiomics features were selected employing the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms. Logistic regression (LR) and random forest (RF) classifiers were applied to construct the models. Receiver operating characteristic (ROC) curves and decision curve analyses (DCA) were performed to compare and validate the predictive capacities of the models. A comparative study of the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) was performed. The Shapley additive explanation (SHAP) was applied to investigate the optimal model's interpretability.</p><p><strong>Results: </strong>The combined model predicted the PMAs' consistency more effectively than the clinicoradiological and radiomics models. Specifically, the LR-combined model displayed optimal prediction performance (test cohort: AUC = 0.913; ACC = 0.840). The SHAP-based explanation of the LR-combined model suggests that the wavelet-transformed and Laplacian of Gaussian (LoG) filter features extracted from T₂WI and CE-T₁WI occupy a dominant position. Meanwhile, the skewness of the original first-order features extracted from T₂WI (T₂WI_original_first-order_Skewness) demonstrated the most substantial contribution.</p><p><strong>Conclusion: </strong>An interpretable machine learning model incorporating clinicoradiological predictors and multiparametric MRI (mpMRI)-based radiomics features may predict PMAs consistency, enabling tailored and precise therapies for patients with PMA.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144591866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isolated restricted diffusion in glioblastoma: incidence, progression, and survival impact '.","authors":"Jai Shankar, Nikunj Patil, Marco Ayroso, Roman Marin, Marc Del Bigio, Marshall Pitz, Jason Beiko, Joseph Silvaggio, Marco Essig, Saranya Kakumanu, Namita Sinha","doi":"10.1007/s00234-025-03672-4","DOIUrl":"https://doi.org/10.1007/s00234-025-03672-4","url":null,"abstract":"<p><strong>Background: </strong>Glioblastoma (GB) is the most malignant primary brain tumor. Isolated restricted diffusion (IRD) is restricted diffusion outside the confines of enhancing tumor with no corresponding enhancement on post contrast study. The aim of our study was to prospectively assess the incidence of IRD in GB patients, determine how often these foci proceed to contrast enhancement on follow up, and analyze the survival pattern of patients with IRD.</p><p><strong>Methods: </strong>In a prospective pilot cohort study, consecutive adult patients (≥ 18 years old) suspected of having GB on initial MRI of brain, were included and screened for the presence of IRD. All images were independently analyzed by two experienced radiologists for inter-rater reliability. The survival pattern of patients with IRD was assessed with Cox-regression and Kaplan-Meier curve analysis.</p><p><strong>Results: </strong>Of the 52 patients (median age- 63 years; male-63.5%) included, 21% (11 of 52) exhibited foci of IRD. Inter-rater agreement on the diagnosis of IRD foci was fair (kappa = 0.29) between the two readers. Among the 11 patients with IRD, only 7 (64%) showed enhancement in the IRD focus on imaging at a median follow up time of 110 days. The Kaplan Meier analysis revealed a significant decrease (p = 0.035) in the survival among patients with IRD focus.</p><p><strong>Conclusion: </strong>In conclusion, IRD foci were seen in 21% of patients with GB, with 64% of these demonstrating enhancement at the IRD focus on follow up imaging. A shorter survival was associated with IRD foci.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}