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Radiology

January 2025

From the Department of Diagnostic, Molecular, and Interventional Radiology, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 (Y.Z., D.F.Y., C.I.H.); and Department of Radiology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China (Y.Z.).

Lung cancer is the leading cause of cancer deaths globally. In various trials, the ability of low-dose CT screening to diagnose early lung cancers leads to high cure rates. It is widely accepted that the potential benefits of low-dose CT screening for lung cancer outweigh the harms.

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Diffusion transformer model with compact prior for low-dose PET reconstruction.

Phys Med Biol

January 2025

Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, No. 350 Shushan Lake Road, Hefei High-tech Zone, Hefei City, Anhui Province, China, Hefei, 230031, CHINA.

Positron emission tomography (PET) is an advanced medical imaging technique that plays a crucial role in non-invasive clinical diagnosis. However, while reducing radiation exposure through low-dose PET scans is beneficial for patient safety, it often results in insufficient statistical data. This scarcity of data poses significant challenges for accurately reconstructing high-quality images, which are essential for reliable diagnostic outcomes.

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Background: Performing hemodialysis without heparin is still challenging. The objective of the present work was to evaluate the impact on thrombogenicity of the hemodialysis circuit using synthetic membranes compared to the asymmetric cellulose triacetate (ATA) membrane.

Methods: Prospective, multicenter, randomized, crossover, open-label study.

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Objective: The study aims to systematically characterize the effect of CT parameter variations on images and lung radiomic and deep features, and to evaluate the ability of different image harmonization methods to mitigate the observed variations.

Approach: A retrospective in-house sinogram dataset of 100 low-dose chest CT scans was reconstructed by varying radiation dose (100%, 25%, 10%) and reconstruction kernels (smooth, medium, sharp). A set of image processing, convolutional neural network (CNNs), and generative adversarial network-based (GANs) methods were trained to harmonize all image conditions to a reference condition (100% dose, medium kernel).

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A low-dose CT reconstruction method using sub-pixel anisotropic diffusion.

Nan Fang Yi Ke Da Xue Xue Bao

January 2025

School of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China.

Objectives: We present a new low-dose CT reconstruction method using sub-pixel and anisotropic diffusion.

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