A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network

Han, Zefang; Shangguan, Hong; Zhang, Xiong; Peng-cheng, Zhang; Cui, Xueying; Ren, Huiying

doi:10.1109/jbhi.2022.3155788

Cited by 29 publications

(12 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…The majority of research focusing on the objective image quality evaluations of DL algorithms has consistently demonstrated remarkable noise reduction compared to FBP and IR at equivalent or lower radiation dose levels. 74,77,79,82,90,92,93,95,103,104,113,114,147 Additionally, the implementation of DL for metal artifact reduction demonstrates superior results when compared to IR. 62,86,119,121 CT image denoising approaches show promising potential, but are not widely accepted in routine clinical practice.…”

Section: Applicationmentioning

confidence: 99%

“…Supervised DL‐based methods have been shown to produce high‐quality denoised CT images, and they are effective at removing various types of noise patterns. Out of 99 studies reviewed, 64 studies 42,59–68,71,72,74,76,77,79,80,83,84,86,88–93,97–104 , 106–109,111–119,123–126,132,134,137–139,142,147,153,159,161–163 apply supervised training method. CNNs are a common type of supervised DL‐based model used for CT image denoising.…”

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

View full text Add to dashboard Cite

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.

show abstract

Section: Dl‐based Noise Reduction Methodsmentioning

confidence: 99%

Section: Applicationmentioning

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

View full text Add to dashboard Cite

show abstract

“…The other is included in the gradient domain to enhance the edge information and remove the streak artefacts. A denoising‐CGAN introduced by Han et al 18 contains a generator with dual‐encoder‐single‐decoder architecture. The first encoder is a pyramid non‐local attention module based on self‐similarity feature maps.…”

Section: Related Workmentioning

confidence: 99%

Cone beam computed tomography enhancement using feature‐embedded variational autoencoder with a perceptual loss function

Joseph

Singh

Pournami

et al. 2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Image‐Guided Radiation Therapy (IGRT) is a cancer treatment method. IGRT usually follows a fragmented treatment track. The weight loss of a patient causes the first treatment plan to be flawed. Medical experts can avert retaking Fan Beam Computed Tomography (FBCT) images before each session of the therapy if the Cone Beam Computed Tomography (CBCT), which is mounted on a Medical Linear Accelerator (LINAC), gives high‐quality images. Rather than improvising the CBCT machine, deep‐learning techniques can be used to enhance the CBCT images. This paper proposes a Feature‐Embedded Variational AutoEncoder (FE‐VAE) for CBCT image enhancement. A feature embedding is incorporated using a MultiResUNet‐based feature extractor to preserve structural information in the input image. A perceptual loss function, which is the product of Mean Absolute Error (MAE) and inverse Structural Similarity Index Measure (SSIM), is proposed in this work. The model is trained using Head‐and‐Neck (H&N) CBCT‐FBCT pairs of 59 cancer patients. Our proposed architecture successfully generates images much closer to the ground truth FBCT images. The model gives an average peak signal‐to‐noise ratio of 32.89 dB, mean squared error of 297.36, MAE of 32.37 HU, and SSIM of 0.99. The trained model gave the least deviated SSIM and MAE, implying that the model is well‐optimised. The experimentation with different losses proves the prominence of the proposed loss function. The visual quality of the results obtained is also comparable with the ground truth. The results indicate that the model produces enhanced CBCT images. Hence, the proposed FE‐VAE is useful in fractionated radiotherapy to minimise the number of scanning sessions for replanning the dosimetry.

show abstract

“…Currently, convolutional neural network (CNN) has attracted great attention in medical imaging [15,[21][22][23][24], including 4D CBCT [25]. Unlike the mentioned model-based methods, CNN could automatically learn a regularization term from big data and bring promising results over traditional methods [26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT

Zhang

Liu

et al. 2022

IEEE J. Biomed. Health Inform.

View full text Add to dashboard Cite

4D cone-beam computed tomography (CBCT) is an important imaging modality in image-guided radiation therapy to address the motion-induced artifacts caused by organ movements during the respiratory process. However, due to the extremely sparse projection data for each temporal phase, 4D CBCT reconstructions will suffer from severe streaking artifacts. Therefore, to tackle the streak artifacts and provide high-quality images, we proposed a framework termed Prior-Regularized Iterative Optimization Reconstruction (PRIOR) for 4D CBCT. The PRIOR framework combines the physics-based model and data-driven method simultaneously, with powerful feature extracting capacity, significantly promoting the image quality compared to single model-based or deep learning-based methods. Besides, we designed a specialized deep learning model named PRIOR-Net, which can effectively excavate the static information in the prior image reconstructed from the fully-sampled projections at the encoding stage to improve the reconstruction performance for individual phase-resolved images. Both the simulated and clinical 4D CBCT datasets were performed to evaluate the performance of the PRIOR-Net and the PRIOR framework. Compared with the advanced 4D CBCT reconstruction methods, the proposed methods achieve promising results quantitatively and qualitatively in streak artifact suppression, soft tissue restoration, and tiny detail preservation.

show abstract

A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network

Cited by 29 publications

References 29 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

Cone beam computed tomography enhancement using feature‐embedded variational autoencoder with a perceptual loss function

PRIOR: Prior-Regularized Iterative Optimization Reconstruction For 4D CBCT

Contact Info

Product

Resources

About