Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm

Shin, Yoo Jin; Chang, Won; Ye, Jong Chul; Kang, Eunjoo; Oh, Dong Yul; Lee, Yoon Jin; Park, Ji Hoon; Kim, Young Hoon

doi:10.3348/kjr.2019.0413

Cited by 64 publications

(56 citation statements)

References 24 publications

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“…The SNR and CNR values of high strength DLR images were higher than those of ASIR-V with 80 or 100% blending factor. Similar results were also reported in studies with different vendor systems and algorithms [10,14,15]. Both studies suggested that DLR has the potential to improve image quality and potentially reduce patient radiation dose.…”

Section: Discussionsupporting

confidence: 82%

“…DLR scored better on artifacts than 30% ASIR-V images in a previous study [12]. Another study reported no DLR related image artifacts [14]. A prior study has reported more frequent distortion artifacts with DLR [22].…”

Section: Discussionmentioning

confidence: 81%

“…Hybrid IR images that blend IR with FBP can be used to decrease this texture problem, although a trade-off between image noise and image texture occurs [9]. Recently, image denoising algorithms using arti cial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of IR while achieving good image quality [10][11][12][13][14][15]. However, there have been a limited number of studies evaluating this technique in a small number of children and the technique was only evaluated in abdomen CT images [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Yoon

Kim

Lim

et al. 2021

Preprint

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Background Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT image reconstruction to overcome the drawbacks of iterative reconstruction (IR). The purpose of our study was to compare objective and subjective image quality of DLR and IR on pediatric abdomen and chest CT images.Methods This retrospective study included pediatric body CT images from February 2020 to October 2020, performed on 51 patients (34 boys and 17 girls; age 1–18 years). Non-contrast chest CT (n = 16), contrast-enhanced chest CT (n = 12), and contrast-enhanced abdomen CT (n = 23) images were included. Standard 50% adaptive statistical iterative reconstruction V (ASIR-V) images were compared to images with 100% ASIR-V and DLR at medium and high strengths. Attenuation, noise, contrast to noise ratio (CNR), and signal to noise (SNR) measurements were performed. Overall image quality, artifacts, and noise were subjectively assessed by two radiologists using a four-point scale (superior, average, suboptimal, and unacceptable). Quantitative and qualitative parameters were compared using repeated measures analysis of variance (ANOVA) with Bonferroni correction and Wilcoxon signed-rank tests.Results DLR had better CNR and SNR than 50% ASIR-V in both pediatric chest and abdomen CT images. When compared with 50% ASIR-V, high strength DLR was associated with noise reduction in non-contrast chest CT (33.0%), contrast-enhanced chest CT (39.6%), and contrast-enhanced abdomen CT (38.7%) with increases in CNR at 149.1%, 105.8% and 53.1% respectively. The subjective assessment of overall image quality and noise was also better on DLR images (p < 0.001). However, there was no significant difference in artifacts between reconstruction methods.Conclusion Compared with 50% ASIR-V, DLR improved pediatric body CT images with significant noise reduction. However, artifacts were not improved by DLR, regardless of strength.

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Section: Discussionsupporting

confidence: 82%

Section: Discussionmentioning

confidence: 81%

Section: Introductionmentioning

confidence: 99%

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Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Yoon

Kim

Lim

et al. 2021

Preprint

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“…In another study, Kim et al [ 23 ] investigated the effect of different loss functions on convolutional neural network (CNN)–based image denoising performance using task-based image quality assessment for various signals and dose levels. Shin et al [ 24 ] compared the image quality of low-dose CT images obtained using a deep learning–based denoising algorithm with low-dose CT images reconstructed using filtered backprojection (FBP) and advanced modeled iterative reconstruction (ADMIRE). They reported that deep learning techniques achieved better noise properties compared with FBP and ADMIRE reconstructions of low-dose CT images.…”

Section: Introductionmentioning

confidence: 99%

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

et al. 2020

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Objectives The current study aimed to design an ultra-low-dose CT examination protocol using a deep learning approach suitable for clinical diagnosis of COVID-19 patients. Methods In this study, 800, 170, and 171 pairs of ultra-low-dose and full-dose CT images were used as input/output as training, test, and external validation set, respectively, to implement the full-dose prediction technique. A residual convolutional neural network was applied to generate full-dose from ultra-low-dose CT images. The quality of predicted CT images was assessed using root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR). Scores ranging from 1 to 5 were assigned reflecting subjective assessment of image quality and related COVID-19 features, including ground glass opacities (GGO), crazy paving (CP), consolidation (CS), nodular infiltrates (NI), bronchovascular thickening (BVT), and pleural effusion (PE). Results The radiation dose in terms of CT dose index (CTDI vol ) was reduced by up to 89%. The RMSE decreased from 0.16 ± 0.05 to 0.09 ± 0.02 and from 0.16 ± 0.06 to 0.08 ± 0.02 for the predicted compared with ultra-low-dose CT images in the test and external validation set, respectively. The overall scoring assigned by radiologists showed an acceptance rate of 4.72 ± 0.57 out of 5 for reference full-dose CT images, while ultra-low-dose CT images rated 2.78 ± 0.9. The predicted CT images using the deep learning algorithm achieved a score of 4.42 ± 0.8. Conclusions The results demonstrated that the deep learning algorithm is capable of predicting standard full-dose CT images with acceptable quality for the clinical diagnosis of COVID-19 positive patients with substantial radiation dose reduction. Key Points • Ultra-low-dose CT imaging of COVID-19 patients would result in the loss of critical information about lesion types, which could potentially affect clinical diagnosis. • Deep learning–based prediction of full-dose from ultra-low-dose CT images for the diagnosis of COVID-19 could reduce the radiation dose by up to 89%. • Deep learning algorithms failed to recover the correct lesion structure/density for a number of patients considered outliers, and as such, further research and development is warranted to address these limitations. Electronic supplementary material The online version of this article (10.1007/s00330-020-07225-6) contains supplementary material, which is available to authorized users.

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“…Recently, several deep learning networks for image-to-image translation have been reported, and they have enabled remarkable radiation dose reduction by image denoising [ 10 11 12 13 ]. Specifically, generative adversarial networks (GANs) have achieved state-of-the-art performance in image generation [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

Yan

Lin

et al. 2021

Korean J Radiol

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Objective To investigate the image quality of ultralow-dose CT (ULDCT) of the chest reconstructed using a cycle-consistent generative adversarial network (CycleGAN)-based deep learning method in the evaluation of pulmonary tuberculosis. Materials and Methods Between June 2019 and November 2019, 103 patients (mean age, 40.8 ± 13.6 years; 61 men and 42 women) with pulmonary tuberculosis were prospectively enrolled to undergo standard-dose CT (120 kVp with automated exposure control), followed immediately by ULDCT (80 kVp and 10 mAs). The images of the two successive scans were used to train the CycleGAN framework for image-to-image translation. The denoising efficacy of the CycleGAN algorithm was compared with that of hybrid and model-based iterative reconstruction. Repeated-measures analysis of variance and Wilcoxon signed-rank test were performed to compare the objective measurements and the subjective image quality scores, respectively. Results With the optimized CycleGAN denoising model, using the ULDCT images as input, the peak signal-to-noise ratio and structural similarity index improved by 2.0 dB and 0.21, respectively. The CycleGAN-generated denoised ULDCT images typically provided satisfactory image quality for optimal visibility of anatomic structures and pathological findings, with a lower level of image noise (mean ± standard deviation [SD], 19.5 ± 3.0 Hounsfield unit [HU]) than that of the hybrid (66.3 ± 10.5 HU, p < 0.001) and a similar noise level to model-based iterative reconstruction (19.6 ± 2.6 HU, p > 0.908). The CycleGAN-generated images showed the highest contrast-to-noise ratios for the pulmonary lesions, followed by the model-based and hybrid iterative reconstruction. The mean effective radiation dose of ULDCT was 0.12 mSv with a mean 93.9% reduction compared to standard-dose CT. Conclusion The optimized CycleGAN technique may allow the synthesis of diagnostically acceptable images from ULDCT of the chest for the evaluation of pulmonary tuberculosis.

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Low-Dose Abdominal CT Using a Deep Learning-Based Denoising Algorithm: A Comparison with CT Reconstructed with Filtered Back Projection or Iterative Reconstruction Algorithm

Cited by 64 publications

References 24 publications

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction

Ultra-low-dose chest CT imaging of COVID-19 patients using a deep residual neural network

Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis

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