Tomography最新文献

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Electron Density and Effective Atomic Number of Normal-Appearing Adult Brain Tissues: Age-Related Changes and Correlation with Myelin Content. 正常成人脑组织的电子密度和有效原子序数:年龄相关变化及其与髓磷脂含量的相关性。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-25 DOI: 10.3390/tomography11090095
Tomohito Hasegawa, Masanori Nakajo, Misaki Gohara, Kiyohisa Kamimura, Tsubasa Nakano, Junki Kamizono, Koji Takumi, Fumitaka Ejima, Gregor Pahn, Eran Langzam, Ryota Nakanosono, Ryoji Yamagishi, Fumiko Kanzaki, Takashi Yoshiura
{"title":"Electron Density and Effective Atomic Number of Normal-Appearing Adult Brain Tissues: Age-Related Changes and Correlation with Myelin Content.","authors":"Tomohito Hasegawa, Masanori Nakajo, Misaki Gohara, Kiyohisa Kamimura, Tsubasa Nakano, Junki Kamizono, Koji Takumi, Fumitaka Ejima, Gregor Pahn, Eran Langzam, Ryota Nakanosono, Ryoji Yamagishi, Fumiko Kanzaki, Takashi Yoshiura","doi":"10.3390/tomography11090095","DOIUrl":"10.3390/tomography11090095","url":null,"abstract":"<p><p><b>Objectives:</b> Few studies have reported in vivo measurements of electron density (ED) and effective atomic number (Z<sub>eff</sub>) in normal brain tissue. To address this gap, dual-energy computed tomography (DECT)-derived ED and Z<sub>eff</sub> maps were used to characterize normal-appearing adult brain tissues, evaluate age-related changes, and investigate correlations with myelin partial volume (V<sub>my</sub>) from synthetic magnetic resonance imaging (MRI). <b>Materials and Methods:</b> Thirty patients were retrospectively analyzed. The conventional computed tomography (CT) value (CT<sub>conv</sub>), ED, Z<sub>eff</sub>, and V<sub>my</sub> were measured in the normal-appearing gray matter (GM) and white matter (WM) regions of interest. V<sub>my</sub> and DECT-derived parameters were compared between WM and GM. Correlations between V<sub>my</sub> and DECT parameters and between age and DECT parameters were analyzed. <b>Results:</b> V<sub>my</sub> was significantly greater in WM than in GM, whereas CT<sub>conv</sub>, ED, and Z<sub>eff</sub> were significantly lower in WM than in GM (all <i>p</i> < 0.001). Z<sub>eff</sub> exhibited a stronger negative correlation with V<sub>my</sub> (ρ = -0.756) than CT<sub>conv</sub> (ρ = -0.705) or ED (ρ = -0.491). ED exhibited weak to moderate negative correlations with age in nine of the 14 regions. In contrast, Z<sub>eff</sub> exhibited weak to moderate positive correlations with age in nine of the 14 regions. CT<sub>conv</sub> exhibited negligible to insignificant correlations with age: <b>Conclusions:</b> This study revealed distinct GM-WM differences in ED and Z<sub>eff</sub> along with opposing age-related changes in these quantities. Therefore, myelin may have substantially contributed to the lower Z<sub>eff</sub> observed in WM, which underlies the GM-WM contrast observed on non-contrast-enhanced CT.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 9","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473793/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research. 利用多模态基础模型进行胆道肿瘤研究。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-25 DOI: 10.3390/tomography11090096
Yashbir Singh, Jesper B Andersen, Quincy A Hathaway, Diana V Vera-Garcia, Varekan Keishing, Sudhakar K Venkatesh, Sara Salehi, Davide Povero, Michael B Wallace, Gregory J Gores, Yujia Wei, Natally Horvat, Bradley J Erickson, Emilio Quaia
{"title":"Leveraging Multimodal Foundation Models in Biliary Tract Cancer Research.","authors":"Yashbir Singh, Jesper B Andersen, Quincy A Hathaway, Diana V Vera-Garcia, Varekan Keishing, Sudhakar K Venkatesh, Sara Salehi, Davide Povero, Michael B Wallace, Gregory J Gores, Yujia Wei, Natally Horvat, Bradley J Erickson, Emilio Quaia","doi":"10.3390/tomography11090096","DOIUrl":"10.3390/tomography11090096","url":null,"abstract":"<p><p>This review explores how multimodal foundation models (MFMs) are transforming biliary tract cancer (BTC) research. BTCs are aggressive malignancies with poor prognosis, presenting unique challenges due to difficult diagnostic methods, molecular complexity, and rarity. Importantly, intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA), and distal bile duct cholangiocarcinoma (dCCA) represent fundamentally distinct clinical entities, with iCCA presenting as mass-forming lesions amenable to biopsy and targeted therapies, while pCCA manifests as infiltrative bile duct lesions with challenging diagnosis and primarily palliative management approaches. MFMs offer potential to advance research by integrating radiological images, histopathology, multi-omics profiles, and clinical data into unified computational frameworks, with applications tailored to these distinct BTC subtypes. Key applications include enhanced biomarker discovery that identifies previously unrecognizable cross-modal patterns, potential for improving currently limited diagnostic accuracy-though validation in BTC-specific cohorts remains essential-accelerated drug repurposing, and advanced patient stratification for personalized treatment. Despite promising results, challenges such as data scarcity, high computational demands, and clinical workflow integration remain to be addressed. Future research should focus on standardized data protocols, architectural innovations, and prospective validation studies. The integration of artificial intelligence (AI)-based methodologies offers new solutions for these historically challenging malignancies. However, current evidence for BTC-specific applications remains largely theoretical, with most studies limited to proof-of-concept designs or related cancer types. Comprehensive clinical validation studies and prospective trials demonstrating patient benefit are essential prerequisites for clinical implementation. The timeline for evidence-based clinical adoption likely extends 7-10 years, contingent on successful completion of validation studies addressing current evidence gaps.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 9","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12473628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Contrast-Enhanced Mammography in Breast Lesion Assessment: Accuracy and Surgical Impact. 对比增强乳房x光检查在乳腺病变评估中的准确性和手术影响。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-20 DOI: 10.3390/tomography11080093
Graziella Di Grezia, Sara Mercogliano, Luca Marinelli, Antonio Nazzaro, Alessandro Galiano, Elisa Cisternino, Gianluca Gatta, Vincenzo Cuccurullo, Mariano Scaglione
{"title":"Contrast-Enhanced Mammography in Breast Lesion Assessment: Accuracy and Surgical Impact.","authors":"Graziella Di Grezia, Sara Mercogliano, Luca Marinelli, Antonio Nazzaro, Alessandro Galiano, Elisa Cisternino, Gianluca Gatta, Vincenzo Cuccurullo, Mariano Scaglione","doi":"10.3390/tomography11080093","DOIUrl":"https://doi.org/10.3390/tomography11080093","url":null,"abstract":"<p><strong>Background: </strong>Accurate preoperative tumor sizing is critical for optimal surgical planning in breast cancer. Contrast-enhanced mammography (CEM) has emerged as a promising modality, yet its accuracy relative to conventional imaging and pathology requires further validation.</p><p><strong>Objective: </strong>To prospectively evaluate the dimensional accuracy and reproducibility of CEM compared to mammography and ultrasound, using surgical pathology as the reference standard.</p><p><strong>Methods: </strong>A total of 205 patients with 267 breast lesions underwent preoperative CEM, mammography, and ultrasound. Tumor sizes were measured independently by two radiologists. Accuracy was assessed via mean absolute error (MAE), Pearson and Spearman correlations, and inter-reader agreement evaluated by intraclass correlation coefficient (ICC) and Gwet's AC1. Sensitivity analyses included bootstrap confidence intervals and log-transformed data. The surgical impact of additional lesions detected by CEM was also analyzed.</p><p><strong>Results: </strong>CEM showed superior accuracy with a mean absolute error of 0.46 mm (95% CI: 0.24-0.68) compared to mammography (4.06 mm) and ultrasound (3.52 mm) (<i>p</i> < 0.00001). Pearson's correlation between CEM and pathology was exceptionally high (r = 0.995; 95% CI: 0.994-0.996), with similar robustness after log transformation. Inter-reader agreement for CEM was excellent (ICC 0.93; Gwet's AC1 ~0.96, 95% CI: 0.93-0.98). CEM detected additional lesions in 13.1% of patients, leading to altered surgical management in 6.4%. Background parenchymal enhancement was independently associated with measurement error.</p><p><strong>Conclusions: </strong>CEM provides highly accurate and reproducible tumor size estimation superior to conventional imaging modalities, with potential clinical impact through detection of additional lesions. Its ability to detect additional lesions not seen on mammography or ultrasound has direct implications for surgical decision making, with the potential to reduce reoperations and improve oncologic and cosmetic outcomes. However, high correlation values and selective patient cohorts warrant cautious interpretation. Further multicenter studies are needed to confirm these findings and define CEM's role in clinical practice.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12389778/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach. 自编码器辅助堆叠集成学习用于淋巴瘤亚型分类:一种混合深度学习和机器学习方法。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-18 DOI: 10.3390/tomography11080091
Roseline Oluwaseun Ogundokun, Pius Adewale Owolawi, Chunling Tu, Etienne van Wyk
{"title":"Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.","authors":"Roseline Oluwaseun Ogundokun, Pius Adewale Owolawi, Chunling Tu, Etienne van Wyk","doi":"10.3390/tomography11080091","DOIUrl":"https://doi.org/10.3390/tomography11080091","url":null,"abstract":"<p><strong>Background: </strong>Accurate subtype identification of lymphoma cancer is crucial for effective diagnosis and treatment planning. Although standard deep learning algorithms have demonstrated robustness, they are still prone to overfitting and limited generalization, necessitating more reliable and robust methods.</p><p><strong>Objectives: </strong>This study presents an autoencoder-augmented stacked ensemble learning (SEL) framework integrating deep feature extraction (DFE) and ensembles of machine learning classifiers to improve lymphoma subtype identification.</p><p><strong>Methods: </strong>Convolutional autoencoder (CAE) was utilized to obtain high-level feature representations of histopathological images, followed by dimensionality reduction via Principal Component Analysis (PCA). Various models were utilized for classifying extracted features, i.e., Random Forest (RF), Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), AdaBoost, and Extra Trees classifiers. A Gradient Boosting Machine (GBM) meta-classifier was utilized in an SEL approach to further fine-tune final predictions.</p><p><strong>Results: </strong>All the models were tested using accuracy, area under the curve (AUC), and Average Precision (AP) metrics. The stacked ensemble classifier performed better than all the individual models with a 99.04% accuracy, 0.9998 AUC, and 0.9996 AP, far exceeding what regular deep learning (DL) methods would achieve. Of standalone classifiers, MLP (97.71% accuracy, 0.9986 AUC, 0.9973 AP) and Random Forest (96.71% accuracy, 0.9977 AUC, 0.9953 AP) provided the best prediction performance, while AdaBoost was the poorest performer (68.25% accuracy, 0.8194 AUC, 0.6424 AP). PCA and t-SNE plots confirmed that DFE effectively enhances class discrimination.</p><p><strong>Conclusion: </strong>This study demonstrates a highly accurate and reliable approach to lymphoma classification by using autoencoder-assisted ensemble learning, reducing the misclassification rate and significantly enhancing the accuracy of diagnosis. AI-based models are designed to assist pathologists by providing interpretable outputs such as class probabilities and visualizations (e.g., Grad-CAM), enabling them to understand and validate predictions in the diagnostic workflow. Future studies should enhance computational efficacy and conduct multi-centre validation studies to confirm the model's generalizability on extensive collections of histopathological datasets.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12389832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Differences in PI-RADS Classification of Prostate Cancer Based on mpMRI Scans Taken 6 Weeks Apart. 间隔6周mpMRI扫描在前列腺癌PI-RADS分类中的差异
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-18 DOI: 10.3390/tomography11080092
Justine Schoch, Viola Düring, Michael Wiedmann, Daniel Overhoff, Daniel Dillinger, Stephan Waldeck, Hans-Ulrich Schmelz, Tim Nestler
{"title":"Differences in PI-RADS Classification of Prostate Cancer Based on mpMRI Scans Taken 6 Weeks Apart.","authors":"Justine Schoch, Viola Düring, Michael Wiedmann, Daniel Overhoff, Daniel Dillinger, Stephan Waldeck, Hans-Ulrich Schmelz, Tim Nestler","doi":"10.3390/tomography11080092","DOIUrl":"https://doi.org/10.3390/tomography11080092","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to investigate the consistency of lesion identification by Prostate Imaging Reporting and Data System (PI-RADS) and the related clinical and histological characteristics in a high-volume tertiary care center.</p><p><strong>Materials and methods: </strong>The analysis used real-world data from 111 patients between 2018 and 2022. Each patient underwent two multiparametric magnetic resonance imaging (MRI) scans of the prostate at different institutions with a median interval of 42 days between the scans, followed by an MRI-fused biopsy conducted 7 days after the second MRI.</p><p><strong>Results: </strong>The PI-RADS classifications assigned to the index lesions in the in-house prostate MRI were as follows: PI-RADS V, 33.3% (n = 37); PI-RADS IV, 49.5% (n = 55); PI-RADS III, 12.6% (n = 14); and PI-RADS II, 4.5% (n = 5). Cancer detection rates for randomized and/or targeted biopsies were 91.9% (n = 34) for PI-RADS V, 65.5% (n = 36) for PI-RADS IV, 21.4% (n = 3) for PI-RADS III, and 20% (n = 1) for PI-RADS II. Overall, malignant histology was observed in 64.9% (n = 72) of the targeted lesions and 57.7% (n = 64) of the randomized biopsies. In the first performed, external MRI, 18% (n = 20) and 10.8% (n = 12) of the patients were classified in the higher and lower PI-RADS categories, respectively. The biopsy plan was adjusted for 57 patients (51.4%); nevertheless, any cancer could have possibly been identified regardless of the adjustments.</p><p><strong>Conclusion: </strong>The 6-week interval between the MRI scans did not affect the quality of the biopsy results significantly.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Machine Learning and Feature Selection in Pediatric Appendicitis. 小儿阑尾炎的机器学习与特征选择。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-13 DOI: 10.3390/tomography11080090
John Kendall, Gabriel Gaspar, Derek Berger, Jacob Levman
{"title":"Machine Learning and Feature Selection in Pediatric Appendicitis.","authors":"John Kendall, Gabriel Gaspar, Derek Berger, Jacob Levman","doi":"10.3390/tomography11080090","DOIUrl":"https://doi.org/10.3390/tomography11080090","url":null,"abstract":"<p><strong>Background/objectives: </strong>Accurate prediction of pediatric appendicitis diagnosis, management, and severity is critical for clinical decision-making. We aimed to evaluate the predictive performance of a wide range of machine learning models, combined with various feature selection techniques, on a pediatric appendicitis dataset. A particular focus was placed on the role of ultrasound (US) image-descriptive features in model performance and explainability.</p><p><strong>Methods: </strong>We conducted a retrospective cohort study on a dataset of 781 pediatric patients aged 0-18 presenting to Children's Hospital St. Hedwig in Regensburg, Germany, between January 2016 and February 2023. We developed and validated predictive models; machine learning algorithms included the random forest, logistic regression, stochastic gradient descent, and the light gradient boosting machine (LGBM). These were paired exhaustively with feature selection methods spanning filter-based (association and prediction), embedded (LGBM and linear), and a novel redundancy-aware step-up wrapper approach. We employed a machine learning benchmarking study design where AI models were trained to predict diagnosis, management, and severity outcomes, both with and without US image-descriptive features, and evaluated on held-out testing samples. Model performance was assessed using overall accuracy and area under the receiver operating characteristic curve (AUROC). A deep learner optimized for tabular data, GANDALF, was also evaluated in these applications.</p><p><strong>Results: </strong>US features significantly improved diagnostic accuracy, supporting their use in reducing model bias. However, they were not essential for maximizing accuracy in predicting management or severity. In summary, our best-performing models were, for diagnosis, the random forest with embedded LGBM feature selection (98.1% accuracy, AUROC: 0.993), for management, the random forest without feature selection (93.9% accuracy, AUROC: 0.980), and for severity, the LGBM with filter-based association feature selection (90.1% accuracy, AUROC: 0.931).</p><p><strong>Conclusions: </strong>Our results demonstrate that high-performing, interpretable machine learning models can predict key clinical outcomes in pediatric appendicitis. US image features improve diagnostic accuracy but are not critical for predicting management or severity.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390108/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Feasibility of Sodium and Amide Proton Transfer-Weighted Magnetic Resonance Imaging Methods in Mild Steatotic Liver Disease. 钠和酰胺质子转移加权磁共振成像方法在轻度脂肪变性肝病中的可行性。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-06 DOI: 10.3390/tomography11080089
Diana M Lindquist, Mary Kate Manhard, Joel Levoy, Jonathan R Dillman
{"title":"Feasibility of Sodium and Amide Proton Transfer-Weighted Magnetic Resonance Imaging Methods in Mild Steatotic Liver Disease.","authors":"Diana M Lindquist, Mary Kate Manhard, Joel Levoy, Jonathan R Dillman","doi":"10.3390/tomography11080089","DOIUrl":"https://doi.org/10.3390/tomography11080089","url":null,"abstract":"<p><p><b>Background/Objectives</b>: Fat and inflammation confound current magnetic resonance imaging (MRI) methods for assessing fibrosis in liver disease. Sodium or amide proton transfer-weighted MRI methods may be more specific for assessing liver fibrosis. The purpose of this study was to determine the feasibility of sodium and amide proton transfer-weighted MRI in individuals with liver disease and to determine if either method correlated with clinical markers of fibrosis. <b>Methods</b>: T<sub>1</sub> and T<sub>2</sub> relaxation maps, proton density fat fraction maps, liver shear stiffness maps, amide proton transfer-weighted (APTw) images, and sodium images were acquired at 3T. Image data were extracted from regions of interest placed in the liver. ANOVA tests were run with disease status, age, and body mass index as independent factors; significance was set to <i>p</i> < 0.05. Post-hoc t-tests were run when the ANOVA showed significance. <b>Results</b>: A total of 36 participants were enrolled, 34 of whom were included in the final APTw analysis and 24 in the sodium analysis. Estimated liver tissue sodium concentration differentiated participants with liver disease from those without, whereas amide proton transfer-weighted MRI did not. Estimated liver tissue sodium concentration negatively correlated with the Fibrosis-4 score, but amide proton transfer-weighted MRI did not correlate with any clinical marker of disease. <b>Conclusions</b>: Amide proton-weighted imaging was not different between groups. Estimated liver tissue sodium concentrations did differ between groups but did not provide additional information over conventional methods.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12389949/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos. 基于零点自监督学习的高分辨率人类胚胎MRI压缩感知重构。
IF 2.2 4区 医学
Tomography Pub Date : 2025-08-02 DOI: 10.3390/tomography11080088
Kazuma Iwazaki, Naoto Fujita, Shigehito Yamada, Yasuhiko Terada
{"title":"Compressed Sensing Reconstruction with Zero-Shot Self-Supervised Learning for High-Resolution MRI of Human Embryos.","authors":"Kazuma Iwazaki, Naoto Fujita, Shigehito Yamada, Yasuhiko Terada","doi":"10.3390/tomography11080088","DOIUrl":"https://doi.org/10.3390/tomography11080088","url":null,"abstract":"<p><p><b>Objectives</b>: This study investigates whether scan time in the high-resolution magnetic resonance imaging (MRI) of human embryos can be reduced without compromising spatial resolution by applying zero-shot self-supervised learning (ZS-SSL), a deep-learning-based reconstruction method. <b>Methods</b>: Simulations using a numerical phantom were conducted to evaluate spatial resolution across various acceleration factors (AF = 2, 4, 6, and 8) and signal-to-noise ratio (SNR) levels. Resolution was quantified using a blur-based estimation method based on the Sparrow criterion. ZS-SSL was compared to conventional compressed sensing (CS). Experimental imaging of a human embryo at Carnegie stage 21 was performed at a spatial resolution of (30 μm)<sup>3</sup> using both retrospective and prospective undersampling at AF = 4 and 8. <b>Results</b>: ZS-SSL preserved spatial resolution more effectively than CS at low SNRs. At AF = 4, image quality was comparable to that of fully sampled data, while noticeable degradation occurred at AF = 8. Experimental validation confirmed these findings, with clear visualization of anatomical structures-such as the accessory nerve-at AF = 4; there was reduced structural clarity at AF = 8. <b>Conclusions</b>: ZS-SSL enables significant scan time reduction in high-resolution MRI of human embryos while maintaining spatial resolution at AF = 4, assuming an SNR above approximately 15. This trade-off between acceleration and image quality is particularly beneficial in studies with limited imaging time or specimen availability. The method facilitates the efficient acquisition of ultra-high-resolution data and supports future efforts to construct detailed developmental atlases.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images. 数字图像中可计算图像纹理特征的影响因素及鲁棒性评估。
IF 2.2 4区 医学
Tomography Pub Date : 2025-07-31 DOI: 10.3390/tomography11080087
Diego Andrade, Howard C Gifford, Mini Das
{"title":"Assessment of Influencing Factors and Robustness of Computable Image Texture Features in Digital Images.","authors":"Diego Andrade, Howard C Gifford, Mini Das","doi":"10.3390/tomography11080087","DOIUrl":"https://doi.org/10.3390/tomography11080087","url":null,"abstract":"<p><p><b>Background/Objectives:</b> There is significant interest in using texture features to extract hidden image-based information. In medical imaging applications using radiomics, AI, or personalized medicine, the quest is to extract patient or disease specific information while being insensitive to other system or processing variables. While we use digital breast tomosynthesis (DBT) to show these effects, our results would be generally applicable to a wider range of other imaging modalities and applications. <b>Methods:</b> We examine factors in texture estimation methods, such as quantization, pixel distance offset, and region of interest (ROI) size, that influence the magnitudes of these readily computable and widely used image texture features (specifically Haralick's gray level co-occurrence matrix (GLCM) textural features). <b>Results:</b> Our results indicate that quantization is the most influential of these parameters, as it controls the size of the GLCM and range of values. We propose a new multi-resolution normalization (by either fixing ROI size or pixel offset) that can significantly reduce quantization magnitude disparities. We show reduction in mean differences in feature values by orders of magnitude; for example, reducing it to 7.34% between quantizations of 8-128, while preserving trends. <b>Conclusions:</b> When combining images from multiple vendors in a common analysis, large variations in texture magnitudes can arise due to differences in post-processing methods like filters. We show that significant changes in GLCM magnitude variations may arise simply due to the filter type or strength. These trends can also vary based on estimation variables (like offset distance or ROI) that can further complicate analysis and robustness. We show pathways to reduce sensitivity to such variations due to estimation methods while increasing the desired sensitivity to patient-specific information such as breast density. Finally, we show that our results obtained from simulated DBT images are consistent with what we see when applied to clinical DBT images.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12390353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Reliability of Automated Amyloid PET Quantification: Real-World Validation of Commercial Tools Against Centiloid Project Method. 淀粉样蛋白PET自动定量的可靠性:商业工具对Centiloid项目方法的实际验证。
IF 2.2 4区 医学
Tomography Pub Date : 2025-07-30 DOI: 10.3390/tomography11080086
Yeon-Koo Kang, Jae Won Min, Soo Jin Kwon, Seunggyun Ha
{"title":"Reliability of Automated Amyloid PET Quantification: Real-World Validation of Commercial Tools Against Centiloid Project Method.","authors":"Yeon-Koo Kang, Jae Won Min, Soo Jin Kwon, Seunggyun Ha","doi":"10.3390/tomography11080086","DOIUrl":"https://doi.org/10.3390/tomography11080086","url":null,"abstract":"<p><p><b>Background:</b> Despite the growing demand for amyloid PET quantification, practical challenges remain. As automated software platforms are increasingly adopted to address these limitations, we evaluated the reliability of commercial tools for Centiloid quantification against the original Centiloid Project method. <b>Methods:</b> This retrospective study included 332 amyloid PET scans (165 [<sup>18</sup>F]Florbetaben; 167 [<sup>18</sup>F]Flutemetamol) performed for suspected mild cognitive impairments or dementia, paired with T1-weighted MRI within one year. Centiloid values were calculated using three automated software platforms, BTXBrain, MIMneuro, and SCALE PET, and compared with the original Centiloid method. The agreement was assessed using Pearson's correlation coefficient, the intraclass correlation coefficient (ICC), a Passing-Bablok regression, and Bland-Altman plots. The concordance with the visual interpretation was evaluated using receiver operating characteristic (ROC) curves. <b>Results:</b> BTXBrain (R = 0.993; ICC = 0.986) and SCALE PET (R = 0.992; ICC = 0.991) demonstrated an excellent correlation with the reference, while MIMneuro showed a slightly lower agreement (R = 0.974; ICC = 0.966). BTXBrain exhibited a proportional underestimation (slope = 0.872 [0.860-0.885]), MIMneuro showed a significant overestimation (slope = 1.053 [1.026-1.081]), and SCALE PET demonstrated a minimal bias (slope = 1.014 [0.999-1.029]). The bias pattern was particularly noted for FMM. All platforms maintained their trends for correlations and biases when focusing on subthreshold-to-low-positive ranges (0-50 Centiloid units). However, all platforms showed an excellent agreement with the visual interpretation (areas under ROC curves > 0.996 for all). <b>Conclusions:</b> Three automated platforms demonstrated an acceptable reliability for Centiloid quantification, although software-specific biases were observed. These differences did not impair their feasibility in aiding the image interpretation, as supported by the concordance with visual readings. Nevertheless, users should recognize the platform-specific characteristics when applying diagnostic thresholds or interpreting longitudinal changes.</p>","PeriodicalId":51330,"journal":{"name":"Tomography","volume":"11 8","pages":""},"PeriodicalIF":2.2,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12389775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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