High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Yang, Linlin; Shangguan, Hong; Zhang, Xiong; Wang, Anhong; Han, Zefang

doi:10.1109/access.2019.2961983

Cited by 23 publications

(22 citation statements)

References 37 publications

(48 reference statements)

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“…Apart from the noise, the LDCT images are degraded by blurring [ 13 , 60 , 73 ] and streaking artifacts [ 28 , 34 , 50 , 71 , 75 , 81 , 91 ]. Lack of X-ray photons during the CT scanning and patient motion cause blurring.…”

Section: Overview Of Ldct Restorationmentioning

confidence: 99%

“…However, after publishing the Pix-to-Pix GAN by Isola et al [ 31 ], there were several LDCT restoration applications published based on it. The main reason for that is the generator of the Pix-to-Pix GAN followed the U-net architecture, and it accepts an image as the input instead of the noise distribution in the latent space [ 75 , 79 ]. The deeper U-net published in [ 79 ] permits to retain of the small details of the processed LDCT images.…”

Section: Architecturesmentioning

confidence: 99%

“…This basic structure of GAN is known as vanilla GAN. Moreover, GAN is flexible to implement different generator models based on various CNN architectures, such as the encoder–decoder [ 58 , 67 ], U-Net [ 6 , 53 , 75 , 79 ], and ResNet [ 12 , 28 , 46 ]. Also, the discriminator mostly acts as a binary classifier to distinguish the synthetic and NDCT images apart.…”

Section: Architecturesmentioning

confidence: 99%

“…4 encoder and 4 deconvolution layers Three conveying paths between the corresponding encoder and decoder layers No CNN with 6 convolutional layers L5, L8 2D-to-3D transfer learning method is efficient and stable [ 72 ] Loss of texture and tissue details [ 21 ] SMGAN, You et al [ 83 ] W-GAN Voxels of size 80 × 80 × 11 3D CNN with 8 convolution layers No No CNN with 6 convolutional layers L4, L6, L7 It successfully preserves the critical features to be diagnosed Edge blurring and structural dissimilarities appeared in some cases Kang et al [ 35 ] Cycle GAN Patches of size 56 × 56 CNN with 20 convolution layers 6 bypass connections in 6 convolution blocks Yes Patch-GAN structure of [ 31 ] (Patch size is 70 × 70) L5, L10, L11 Resistance to mode collapse and robust to cardiac motion and contrast change Lack of texture preservation due to the low stability of the DL model [ 75 ]. Altering the local image details [ 80 ] m-WGAN, Hu et al [ 28 ] W-GAN Patches of size 64 × 64 ResNet with 16 Residual blocks (38 Convolution layers) 16 Bypass connection in 16 residual blocks Yes CNN with 8 convolution layers L3, L6 Minimizing the over-smoothing The noise and artifacts remain in the restored images Park et al [ 53 ] Vanilla GAN Patches of size 128 × 128 U-net like deep convolutional framelet …”

Section: Architecturesmentioning

confidence: 99%

“…However, repeated CT scans may reveal that the patient may be exposed to radiation enormously. Overexpose to the radiation would cause the development of metabolic abnormalities, radiation-induced cancer, and other genetic disorders that fall the patients’ quality of life rapidly [ 75 ]. Therefore, low-dose CT (LDCT) scanning protocols have been proposed to minimize patients’ exposure to radiation while maintaining adequate diagnostic accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake

Abdullah

Sabri

et al. 2021

Complex Intell. Syst.

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Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.

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Section: Overview Of Ldct Restorationmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

Section: Architecturesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A review on Deep Learning approaches for low-dose Computed Tomography restoration

Kulathilake

Abdullah

Sabri

et al. 2021

Complex Intell. Syst.

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

Ko,

Song,

Baek

et al. 2024

Medical Physics

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BackgroundOn enhancing the image quality of low‐dose computed tomography (LDCT), various denoising methods have achieved meaningful improvements. However, they commonly produce over‐smoothed results; the denoised images tend to be more blurred than the normal‐dose targets (NDCTs). Furthermore, many recent denoising methods employ deep learning(DL)‐based models, which require a vast amount of CT images (or image pairs).PurposeOur goal is to address the problem of over‐smoothed results and design an algorithm that works regardless of the need for a large amount of training dataset to achieve plausible denoising results. Over‐smoothed images negatively affect the diagnosis and treatment since radiologists had developed clinical experiences with NDCT. Besides, a large‐scale training dataset is often not available in clinical situations. To overcome these limitations, we propose locally‐adaptive noise‐level matching (LANCH), emphasizing the output should retain the same noise‐level and characteristics to that of the NDCT without additional training.MethodsWe represent the NDCT image as the pixel‐wisely weighted sum of an over‐smoothed output from off‐the‐shelf denoiser (OSD) and the difference between the LDCT image and the OSD output. Herein, LANCH determines a 2D ratio map (i.e., pixel‐wise weight matrix) by locally matching the noise‐level of output and NDCT, where the LDCT‐to‐NDCT device flux (mAs) ratio reveals the NDCT noise‐level. Thereby, LANCH can preserve important details in LDCT, and enhance the sharpness of the noise‐free regions. Note that LANCH can enhance any LDCT denoisers without additional training data (i.e., zero‐shot).ResultsThe proposed method is applicable to any OSD denoisers, reporting significant texture plausibility development over the baseline denoisers in quantitative and qualitative manners. It is surprising that the denoising accuracy achieved by our method with zero‐shot denoiser was comparable or superior to that of the best training‐based denoisers; our result showed 1% and 33% gains in terms of SSIM and DISTS, respectively. Reader study with experienced radiologists shows significant image quality improvements, a gain of + 1.18 on a five‐point mean opinion score scale.ConclusionsIn this paper, we propose a technique to enhance any low‐dose CT denoiser by leveraging the fundamental physical relationship between the x‐ray flux and noise variance. Our method is capable of operating in a zero‐shot condition, which means that only a single low‐dose CT image is required for the enhancement process. We demonstrate that our approach is comparable or even superior to supervised DL‐based denoisers that are trained using numerous CT images. Extensive experiments illustrate that our method consistently improves the performance of all tested LDCT denoisers.

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Reconstructing Medical Images Using Generative Adversarial Networks: A Study

Buch

Thakkar

2022

Futuristic Trends in Networks and Computing Technologies

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High-Frequency Sensitive Generative Adversarial Network for Low-Dose CT Image Denoising

Cited by 23 publications

References 37 publications

A review on Deep Learning approaches for low-dose Computed Tomography restoration

A review on Deep Learning approaches for low-dose Computed Tomography restoration

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

Reconstructing Medical Images Using Generative Adversarial Networks: A Study

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