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Background: Patients with vertebral column deformations are exposed to high risks associated with ionising radiation exposure. Risks are further increased due to the serial X-ray images that are needed to measure and asses their spinal deformation using Cobb or superimposition methods. Therefore, optimising such X-ray practice, via reducing dose whilst maintaining image quality, is a necessity.
Objectives: With a specific focus on lateral thoraco-lumbar images for Cobb and superimposition measurements, this paper outlines a systematic procedure to the optimisation of X-ray practice.
Methods: Optimisation was conducted based on suitable image quality from minimal dose. Image quality was appraised using a visual-analogue-rating-scale, and Monte-Carlo modelling was used for dose estimation. The optimised X-ray practice was identified by imaging healthy normal-weight male adult living human volunteers.
Results: The optimised practice consisted of: anode towards the head, broad focus, no OID or grid, 80 kVp, 32 mAs and 130 cm SID.
Conclusion: Images of suitable quality for laterally assessing spinal conditions using Cobb or superimposition measurements were produced from an effective dose of 0.05 mSv, which is 83% less than the average effective dose used in the UK for lateral thoracic/lumbar exposures. This optimisation procedure can be adopted and use for optimisation of other radiographic techniques.
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http://dx.doi.org/10.3233/XST-140449 | DOI Listing |
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.
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.
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 PDFPLoS 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 PDFPLoS 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.