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Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography

Medical physics (Lancaster), 2019-02, Vol.46 (2), p.550-562

2019

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Image Comparative Assessment Using Iterative Reconstructions: Clinical Comparison of Low-Dose Abdominal/Pelvic Computed Tomography Between Adaptive Statistical, Model-Based Iterative Reconstructions and Traditional Filtered Back Projection in 65 Patients

Investigative radiology, 2014-04, Vol.49 (4), p.209-216

2014

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Opportunistic Screening Using Low‐Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study

Journal of bone and mineral research, 2021-03, Vol.36 (3), p.427-435

2021

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Low‐dose CT image and projection dataset

Medical physics (Lancaster), 2021-02, Vol.48 (2), p.902-911

2021

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Lung cancer mortality reduction by LDCT screening—Results from the randomized German LUSI trial

International journal of cancer, 2020-03, Vol.146 (6), p.1503-1513

2020

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Ultra-low-dose CT for extremities in an acute setting: initial experience with 203 subjects

Skeletal radiology, 2020-04, Vol.49 (4), p.531-539

2020

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Noise2Context: Context‐assisted learning 3D thin‐layer for low‐dose CT

Medical physics (Lancaster), 2021-10, Vol.48 (10), p.5794-5803

2021

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Low‐dose CT denoising via convolutional neural network with an observer loss function

Medical physics (Lancaster), 2021-10, Vol.48 (10), p.5727-5742

2021

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Ultralow‐parameter denoising: Trainable bilateral filter layers in computed tomography

Medical physics (Lancaster), 2022-08, Vol.49 (8), p.5107-5120

2022

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Integrated, Multidisciplinary Management of Pulmonary Nodules Can Streamline Care and Improve Adherence to Recommendations

The oncologist (Dayton, Ohio), 2020-05, Vol.25 (5), p.431-437

2020

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Low‐dose CT denoising with a high‐level feature refinement and dynamic convolution network

Medical physics (Lancaster), 2023-06, Vol.50 (6), p.3597-3611

2023

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A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images

Medical physics (Lancaster), 2019-09, Vol.46 (9), p.3906-3923

2019

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Incorporation of residual attention modules into two neural networks for low‐dose CT denoising

Medical physics (Lancaster), 2021-06, Vol.48 (6), p.2973-2990

2021

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Detector shifting and deep learning based ring artifact correction method for low‐dose CT

Medical physics (Lancaster), 2023-07, Vol.50 (7), p.4308-4324

2023

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Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Physics in medicine & biology, 2024-04, Vol.69 (8), p.85013

2024

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Adapting low‐dose CT denoisers for texture preservation using zero‐shot local noise‐level matching

Medical physics (Lancaster), 2024-06, Vol.51 (6), p.4181-4200

2024

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Ultra‐low‐dose CT image denoising using modified BM3D scheme tailored to data statistics

Medical physics (Lancaster), 2019-01, Vol.46 (1), p.190-198

2019

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Direct estimation of the noise power spectrum from patient data to generate synthesized CT noise for denoising network training

Medical physics (Lancaster), 2024-03, Vol.51 (3), p.1637-1652

2024

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Utilization of an attentive map to preserve anatomical features for training convolutional neural‐network‐based low‐dose CT denoiser

Medical physics (Lancaster), 2023-05, Vol.50 (5), p.2787-2804

2023

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STEDNet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose CT

Medical physics (Lancaster), 2023-07, Vol.50 (7), p.4443-4458

2023

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Transfer learning framework for low‐dose CT reconstruction based on marginal distribution adaptation in multiscale

Medical physics (Lancaster), 2023-03, Vol.50 (3), p.1450-1465

2023

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Multilayer residual sparsifying transform (MARS) model for low‐dose CT image reconstruction

Medical physics (Lancaster), 2021-10, Vol.48 (10), p.6388-6400

2021

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Unpaired low‐dose computed tomography image denoising using a progressive cyclical convolutional neural network

Medical physics (Lancaster), 2024-02, Vol.51 (2), p.1289-1312

2024

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Effectiveness of a single low‐dose computed tomography screening for lung cancer: A population‐based perspective cohort study in China

International journal of cancer, 2024-02, Vol.154 (4), p.659-669

2024

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Low‐dose CT reconstruction using spatially encoded nonlocal penalty

Medical physics (Lancaster), 2017-10, Vol.44 (10), p.e376-e390

2017

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Cycle‐consistent adversarial denoising network for multiphase coronary CT angiography

Image Comparative Assessment Using Iterative Reconstructions: Clinical Comparison of Low-Dose Abdominal/Pelvic Computed Tomography Between Adaptive Statistical, Model-Based Iterative Reconstructions and Traditional Filtered Back Projection in 65 Patients

Opportunistic Screening Using Low‐Dose CT and the Prevalence of Osteoporosis in China: A Nationwide, Multicenter Study

Low‐dose CT image and projection dataset

Lung cancer mortality reduction by LDCT screening—Results from the randomized German LUSI trial

Ultra-low-dose CT for extremities in an acute setting: initial experience with 203 subjects

Noise2Context: Context‐assisted learning 3D thin‐layer for low‐dose CT

Low‐dose CT denoising via convolutional neural network with an observer loss function

Ultralow‐parameter denoising: Trainable bilateral filter layers in computed tomography

Integrated, Multidisciplinary Management of Pulmonary Nodules Can Streamline Care and Improve Adherence to Recommendations

Low‐dose CT denoising with a high‐level feature refinement and dynamic convolution network

A performance comparison of convolutional neural network‐based image denoising methods: The effect of loss functions on low‐dose CT images

Incorporation of residual attention modules into two neural networks for low‐dose CT denoising

Detector shifting and deep learning based ring artifact correction method for low‐dose CT

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery

Adapting low‐dose CT denoisers for texture preservation using zero‐shot local noise‐level matching

Ultra‐low‐dose CT image denoising using modified BM3D scheme tailored to data statistics

Direct estimation of the noise power spectrum from patient data to generate synthesized CT noise for denoising network training

Utilization of an attentive map to preserve anatomical features for training convolutional neural‐network‐based low‐dose CT denoiser

STEDNet: Swin transformer‐based encoder–decoder network for noise reduction in low‐dose CT

Transfer learning framework for low‐dose CT reconstruction based on marginal distribution adaptation in multiscale

Multilayer residual sparsifying transform (MARS) model for low‐dose CT image reconstruction

Unpaired low‐dose computed tomography image denoising using a progressive cyclical convolutional neural network

Effectiveness of a single low‐dose computed tomography screening for lung cancer: A population‐based perspective cohort study in China

Low‐dose CT reconstruction using spatially encoded nonlocal penalty

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