Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: Dual-energy computed tomography (DECT) has been used to improve image quality of head and neck squamous cell carcinoma (SCC). This study aimed to assess image quality of laryngeal SCC using linear blending image (LBI), nonlinear blending image (NBI), and noise-optimized virtual monoenergetic image (VMI+) algorithms.
Methods: Thirty-four patients with laryngeal SCC were retrospectively enrolled between June 2019 and December 2020. DECT images were reconstructed using LBI (80 kV and M_0.6), NBI, and VMI+ (40 and 55 keV) algorithms. Contrast-to-noise ratio (CNR), tumor delineation, and overall image quality were assessed and compared.
Results: VMI+ (40 keV) had the highest CNR and provided better tumor delineation than VMI+ (55 keV), LBI, and NBI, while NBI provided better overall image quality than VMI+ and LBI (all corrected p < 0.05).
Conclusions: VMI+ (40 keV) and NBI improve image quality of laryngeal SCC and may be preferable in DECT examination.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1002/hed.26812 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Leveraging GPT-4o for Automated Extraction and Categorization of CAD-RADS Features From Free-Text Coronary CT Angiography Reports: Diagnostic Study.
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
Novel 3d-printed Coaxial Light Microscope Adapter for Ophthalmic Wet Lab.
J Cataract Refract Surg
September 2025
Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy.
Purpose: To compare the usability and training effectiveness of a 3D-printed coaxial illumination system mounted on an off-the-shelf stereo-microscope to a professional ophthalmic surgical microscope, in cataract surgery simulation.
Setting: Ophthalmology Lab, Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy.
Design: Prospective randomized crossover study.
Incidence, risk factors, and comorbidities of persistent postural-perceptual dizziness after stroke: A prospective study of 284 cases.
J Vestib Res
September 2025
Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
ObjectiveTo explore the incidence, risk factors, and comorbidities of persistent postural-perceptual dizziness (PPPD) after stroke.MethodsPatients with acute stroke and vestibular symptoms were enrolled prospectively and continuously. Baseline information, risk factors, imaging materials, and diagnosis were collected.
View Article and Find Full Text PDFUsability and quality evaluation of the World Health Organization SkinNTDs app among frontline health workers in Cameroon: A mixed methods study.
PLoS Negl Trop Dis
September 2025
Universitat Oberta de Catalunya, Barcelona, Spain.
Background: Originally adapted from a paper-based guide for skin-related neglected tropical diseases (NTDs), version 3.0.0 of the World Health Organization (WHO) SkinNTDs app aims to strengthen disease surveillance and frontline health worker capacity in NTD-endemic settings.
View Article and Find Full Text PDFMulti-scale diffusion model for underwater image restoration and enhancement.
PLoS One
September 2025
The Institute of Port Information Digitalization, China Liaoning Port Group Co. Ltd., Dalian, Liaoning, China.
Background: Underwater environments face challenges with image degradation due to light absorption and scattering, resulting in blurring, reduced contrast, and color distortion. This significantly impacts underwater exploration and environmental monitoring, necessitating advanced algorithms for effective enhancement.
Objectives: The study aims to develop an innovative underwater image enhancement algorithm that integrates physical models with deep learning to improve visual quality and surpass existing methods in performance metrics.