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- 摘要
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The effect of different image reconstruction techniques on pre-clinical quantitative imaging and dual-energy CT
摘要： To analyse the effect of different image reconstruction techniques on image quality and dual energy CT (DECT) imaging metrics. A software platform for pre-clinical cone beam CT X-ray image reconstruction was built using the open-source reconstruction toolkit. Pre-processed projections were reconstructed with filtered back-projection and iterative algorithms, namely Feldkamp, Davis, and Kress (FDK), Iterative FDK, simultaneous algebraic reconstruction technique (SART), simultaneous iterative reconstruction technique and conjugate gradient. Imaging metrics were quantitatively assessed, using a quality assurance phantom, and DECT analysis was performed to determine the influence of each reconstruction technique on the relative electron density (ρe) and effective atomic number (Zeff) values. Iterative reconstruction had favourable results for the DECT analysis: a significantly smaller spread for each material in the ρe-Zeff space and lower Zeff and ρe residuals (on average 24 and 25% lower, respectively). In terms of image quality assurance, the techniques FDK, Iterative FDK and SART provided acceptable results. The three reconstruction methods showed similar geometric accuracy, uniformity and CT number results. The technique SART had a contrast-to-noise ratio up to 76% higher for solid water and twice as high for Teflon, but resolution was up to 28% lower when compared to the other two techniques. Advanced image reconstruction can be beneficial, but the benefit is small, and calculation times may be unacceptable with current technology. The use of targeted and downscaled reconstruction grids, larger, yet practicable, pixel sizes and GPU are recommended. An iterative CBCT reconstruction platform was build using RTK.
关键词： image reconstruction,iterative reconstruction,quantitative imaging,dual-energy CT,pre-clinical imaging
更新于2025-09-11 14:15:04
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The eye in AI: artificial intelligence in ophthalmology
摘要： The convergence of major developments in artificial intelligence (AI) for image analysis with advances in clinical imaging technologies has major implications for the practice of medicine. Gains in AI system performance have been the product of improvements in computing hardware and progress in algorithm design, such that large volumes of data can now be processed with great accuracy at extraordinary speeds. As Hogarty et al. illustrate in this edition of the Journal, the discipline of ophthalmology is at the forefront of the AI revolution, with a growing body of research indicating that AI systems can be applied to a wide range of ophthalmic imaging methods across a broad range of disease categories with remarkable performance.
关键词： image analysis,clinical imaging,artificial intelligence,deep learning,ophthalmology
更新于2025-09-11 14:15:04