Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss

Marcos, Luella; Alirezaie, Javad; Babyn, Paul

doi:10.1109/embc46164.2021.9630790

Cited by 15 publications

(8 citation statements)

References 8 publications

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“…The predominant DL models for CT denoising are GANs and CNNs. As shown in Figure 2a, out of 99 publications examined, 61 studies use the models based on CNN, 59–119 while 30 studies are based on GAN 120–149 . Additionally, two studies adopt Transformer‐based approaches 150,151 .…”

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

“…Instead, they use techniques such as GANs, VAEs, or self‐supervised learning to estimate the underlying distribution of clean images from the noisy images. Twenty studies 85 , 87 , 120 , 121 , 122 , 127 , 128 , 129 , 130 , 131 , 133 , 135 , 136 , 141 , 143 , 145 , 146 , 148 , 149 , 154 , 158 apply different unsupervised training approaches. Unsupervised DL‐based methods rely on the assumption that the noisy image can be modeled as a combination of a clean image and additive noise, and aim to estimate the clean image from the noisy input.…”

Section: Training Validation and Evaluationmentioning

confidence: 99%

“…K‐fold cross‐validation is the most popular validation method typically used for supervised learning tasks where there are labeled data and a target variable to predict, including single fold, 68 , 71 , 72 5‐fold, 97 , 98 , 99 , 100 , 102 and 10‐fold cross‐validation. 128 , 129 , 130 , 131 In general, the performance results from higher iterations can be averaged to obtain a more reliable estimation of the model's performance.…”

Section: Training Validation and Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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With the ever‐increasing use of computed tomography (CT), concerns about its radiation dose have become a significant public issue. To address the need for radiation dose reduction, CT denoising methods have been widely investigated and applied in low‐dose CT images. Numerous noise reduction algorithms have emerged, such as iterative reconstruction and most recently, deep learning (DL)‐based approaches. Given the rapid advancements in Artificial Intelligence techniques, we recognize the need for a comprehensive review that emphasizes the most recently developed methods. Hence, we have performed a thorough analysis of existing literature to provide such a review. Beyond directly comparing the performance, we focus on pivotal aspects, including model training, validation, testing, generalizability, vulnerability, and evaluation methods. This review is expected to raise awareness of the various facets involved in CT image denoising and the specific challenges in developing DL‐based models.

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Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

Section: Training Validation and Evaluationmentioning

confidence: 99%

Section: Training Validation and Evaluationmentioning

confidence: 99%

See 1 more Smart Citation

CT image denoising methods for image quality improvement and radiation dose reduction

Sadia,

Chen,

Zhang

2024

J Applied Clin Med Phys

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“…In order to improve the accuracy of the pest identification model, Wang et al [20] improved the attention CBAM module, a new mixed attention module I CBAM. Marcos et al [17] proposed a universal adversarial network composed of accelerated fusion of spatial and channel attention modules to solve the limitations of GAN-based denoising model. Karthik et al [12] replaced the standard Squeeze-and-Excite [9] block in the Efficient-NetV2 [19] model with an Efficient Channel Attention [21] block, and the total number of training parameters dropped significantly.…”

Section: Attention Mechanismmentioning

confidence: 99%

EMC-Net：A Jujube Fruit Defects Identification Network

Zhao,

Xu,

Zhao

et al. 2024

Preprint

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Jujube defects typically occur during the growth, harvesting, packaging and transportation of jujube fruits. Reliable and rapid identification of jujube fruits defects and control measures are crucial for defect identification. However, traditional jujube fruits defects identification mostly relies on expert experience. It requires lots of labor and is subjective. In our study, we improved the ECA attention module and designed the Efficient Channel Attention2 (ECAA) attention module. Then we added ECAA to the MobileNet V2 and ConvNeXt base modules. Finally, for the output of each sub-model, we used weighted sum to achieve sub-models fusion to construct the ECAA MobileNet ConvNeXt(EMCNet) model. We also established a jujube fruits defects identification dataset that contained 4203 images of healthy and defected jujube fruits and their category labels. For EMC-Net model, we tested the performance of EMC-Net on the jujube fruits defects identification dataset. Experimental results show that the proposed model achieve an accuracy of 98.2% on the jujube fruits dataset. These results suggest that the proposed EMC-Net network can effectively identify jujube fruits defects.

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“…The PatchGAN (Pan et al 2021) structure is used by the discriminator. Furthermore, we incorporated frequency domain (Akçakaya et al 2019) and perceptual loss functions (Marcos et al 2021) to guide the SR process and improve the quality of the reconstructed images. The proposed model outperforms on bladder, abdomen, and brain datasets, which demonstrates the strong generalization and robustness of our proposed model.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction

Xiao¹,

Chen²,

Wang³

et al. 2023

Phys. Med. Biol.

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Objective: Computed tomography (CT) and magnetic resonance imaging (MRI) are widely used in medical imaging modalities, and provide valuable information for clinical diagnosis and treatment. However, due to hardware limitations and radiation safety concerns, the acquired images are often limited in resolution. Super-resolution reconstruction (SR) techniques have been developed to enhance resolution of CT and MRI slices, which can potentially improve diagnostic accuracy. To capture more useful feature information and reconstruct higher quality super-resolution images, we proposed a novel hybrid framework SR model based on generative adversarial networks (GANs). Approach: The proposed SR model combines frequency domain and perceptual loss functions, which can work in both frequency domain and image domain. The proposed method consists of 4 parts: i) the discrete Fourier transform (DFT) operation transforms the image from the image domain to frequency domain; ii) a complex residual U-net performs SR in the frequency domain; iii) the inverse discrete Fourier transform (iDFT) operation based on data fusion transforms the image from the frequency domain to image domain; iv) an enhanced residual U-net network is used for SR of image domain. Main results: Experimental results on bladder MRI slices, abdomen CT slices, and brain MRI slices show that the proposed SR model outperforms state-of-the-art SR methods in terms of visual quality and objective quality metric such as the structural similarity (SSIM) and the peak signal-to-noise ratio (PSNR), which proves that the proposed model has better generalization and robustness. (Bladder dataset: upscaling factor of 2: SSIM=0.913, PSNR=31.203; upscaling factor of 4: SSIM=0.821, PSNR=28.604. Abdomen dataset: upscaling factor of 2: SSIM=0.929, PSNR=32.594; upscaling factor of 4: SSIM=0.834, PSNR=27.050. Brain dataset: SSIM=0.861, PSNR=26.945).Significance: Our proposed SR model is capable of super-resolution reconstruction for CT and MRI slices. The SR results provide a reliable and effective foundation for clinical diagnosis and treatment.

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Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss

Cited by 15 publications

References 8 publications

CT image denoising methods for image quality improvement and radiation dose reduction

CT image denoising methods for image quality improvement and radiation dose reduction

EMC-Net：A Jujube Fruit Defects Identification Network

A novel hybrid generative adversarial network for CT and MRI super-resolution reconstruction

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