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Purpose: This study aimed to explore the ability of fusion images of non-echo planar diffusion-weighted magnetic resonance imaging (non-EPI-DWI MRI) and computed tomography (CT) to accurately locate cholesteatoma and plan the surgical approach.
Methods: In the first part, 41 patients were included. Their CT images and non-EPI DWMRI images were fused. The scope of cholesteatoma in the fusion image was compared with that in the surgical video to evaluate the capability to locate cholesteatoma. A total of 229 patients were included in the second part, and they were divided into 2 groups. We chose the surgical approach for the CT group and the fusion group, and compared the accuracy of surgical approaches in the CT group and the fusion group using the surgical records.
Results: The location of cholesteatoma shown in the fusion images was almost identical to that observed during the operation (kappa = .862). The overall specificity and sensitivity of the fusion images in locating cholesteatoma were 94.12% and 93.06%, respectively. The accuracy of surgical approach selection based on the fusion images (99.02%) was higher than that of surgical approach selection based on the CT images (85.83%).
Conclusion: It is recommended that the fusion images be used to locate the range of the cholesteatoma before operation.
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http://dx.doi.org/10.1177/00034894241241189 | DOI Listing |
Hum Brain Mapp
September 2025
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Investigating neuroimaging data to identify brain-based markers of mental illnesses has gained significant attention. Nevertheless, these endeavors encounter challenges arising from a reliance on symptoms and self-report assessments in making an initial diagnosis. The absence of biological data to delineate nosological categories hinders the provision of additional neurobiological insights into these disorders.
View Article and Find Full Text PDFJ Biomed Opt
September 2025
Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability.
Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach.
Rev Cardiovasc Med
August 2025
Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, LE3 9QP Leicester, UK.
Adult congenital heart disease (ACHD) constitutes a heterogeneous and expanding patient cohort with distinctive diagnostic and management challenges. Conventional detection methods are ineffective at reflecting lesion heterogeneity and the variability in risk profiles. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL) models, has revolutionized the potential for improving diagnosis, risk stratification, and personalized care across the ACHD spectrum.
View Article and Find Full Text PDFMed Phys
September 2025
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Background: Four-dimensional magnetic resonance imaging (4D-MRI) holds great promise for precise abdominal radiotherapy guidance. However, current 4D-MRI methods are limited by an inherent trade-off between spatial and temporal resolutions, resulting in compromised image quality characterized by low spatial resolution and significant motion artifacts, hindering clinical implementation. Despite recent advancements, existing methods inadequately exploit redundant frame information and struggle to restore structural details from highly undersampled acquisitions.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Key Laboratory of Intelligent Medical Imaging of Wenzhou, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Background: Tumor deposits (TDs) are an important prognostic factor in rectal cancer. However, integrated models combining clinical, habitat radiomics, and deep learning (DL) features for preoperative TDs detection remain unexplored.
Purpose: To investigate fusion models based on MRI for preoperative TDs identification and prognosis in rectal cancer.