Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

Hu, Chen; Zhang, Yi; Kalra, Mannudeep K.; Lin, Feng; Chen, Yang; Liao, Peixi; Zhou, Jiliu; Wang, Ge

doi:10.1109/tmi.2017.2715284

Cited by 1,361 publications

(1,082 citation statements)

References 43 publications

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“…This has already been applied to CT imaging and has been demonstrated to be of value on a dataset consisting of normal-dose and simulated low-dose CT. 15 Another approach uses paired MR images of the same anatomy, which are acquired at different field strengths. A study using 3T input data and 7T output data showed that a deep network can be trained to create simulated "7T-like" images from 3T data.…”

Section: -13mentioning

confidence: 99%

Deep Learning in Neuroradiology

Zaharchuk

Gong

Wintermark

et al. 2018

AJNR Am J Neuroradiol

253

169

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SUMMARY:Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method.ABBREVIATIONS: AD ϭ Alzheimer disease; ADNI ϭ Alzheimer's Disease Neuroimaging Initiative; ASL ϭ arterial spin-labeling; CNN ϭ convolutional neural network;

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Section: -13mentioning

confidence: 99%

Deep Learning in Neuroradiology

Zaharchuk

Gong

Wintermark

et al. 2018

AJNR Am J Neuroradiol

253

169

View full text Add to dashboard Cite

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“…In recent years, several convolutional neural network (CNN)‐based methods have been proposed for natural image denoising, and the application of three‐layer CNN for LDCT denoising has shown promising results. However, for certain imaging tasks, the three‐layer CNN introduces image blurring, thus a deeper CNN has been employed to increase the sharpness in LDCT denoising . In general, using a deeper CNN improves the image processing performance owing to its strong representational power.…”

Section: Introductionmentioning

confidence: 99%

“…In addition to the network designs and loss functions, reliable and objective image quality assessment is essential to derive a meaningful conclusion from a comparative study on different image denoising methods. The image quality metrics like the root‐mean‐squared error (RMSE) and the structural similarity index (SSIM) are widely used for the quantitative evaluation of LDCT denoising . However, regarding perceptual similarity, these simple metrics often do not match the qualitative evaluation .…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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Purpose Convolutional neural network (CNN)‐based image denoising techniques have shown promising results in low‐dose CT denoising. However, CNN often introduces blurring in denoised images when trained with a widely used pixel‐level loss function. Perceptual loss and adversarial loss have been proposed recently to further improve the image denoising performance. In this paper, we investigate the effect of different loss functions on image denoising performance using task‐based image quality assessment methods for various signals and dose levels. Methods We used a modified version of U‐net that was effective at reducing the correlated noise in CT images. The loss functions used for comparison were two pixel‐level losses (i.e., the mean‐squared error and the mean absolute error), Visual Geometry Group network‐based perceptual loss (VGG loss), adversarial loss used to train the Wasserstein generative adversarial network with gradient penalty (WGAN‐GP), and their weighted summation. Each image denoising method was applied to reconstructed images and sinogram images independently and validated using the extended cardiac‐torso (XCAT) simulation and Mayo Clinic datasets. In the XCAT simulation, we generated fan‐beam CT datasets with four different dose levels (25%, 50%, 75%, and 100% of a normal‐dose level) using 10 XCAT phantoms and inserted signals in a test set. The signals had two different shapes (spherical and spiculated), sizes (4 and 12 mm), and contrast levels (60 and 160 HU). To evaluate signal detectability, we used a detection task SNR (tSNR) calculated from a non‐prewhitening model observer with an eye filter. We also measured the noise power spectrum (NPS) and modulation transfer function (MTF) to compare the noise and signal transfer properties. Results Compared to CNNs without VGG loss, VGG‐loss‐based CNNs achieved a more similar tSNR to that of the normal‐dose CT for all signals at different dose levels except for a small signal at the 25% dose level. For a low‐contrast signal at 25% or 50% dose, adding other losses to the VGG loss showed more improved performance than only using VGG loss. The NPS shapes from VGG‐loss‐based CNN closely matched that of normal‐dose CT images while CNN without VGG loss overly reduced the mid‐high‐frequency noise power at all dose levels. MTF also showed VGG‐loss‐based CNN with better‐preserved high resolution for all dose and contrast levels. It is also observed that additional WGAN‐GP loss helps improve the noise and signal transfer properties of VGG‐loss‐based CNN. Conclusions The evaluation results using tSNR, NPS, and MTF indicate that VGG‐loss‐based CNNs are more effective than those without VGG loss for natural denoising of low‐dose images and WGAN‐GP loss improves the denoising performance of VGG‐loss‐based CNNs, which corresponds with the qualitative evaluation.

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“…Moreover, the introduction of such algorithms in clinical practice requires integration with preexisting workflows, along with an actual demonstration of their value in terms of cost reduction and outcome improvement. The possible applications of machine learning to assist the radiologist during routine clinical activity range from the automatic creation of study protocols to the hanging of study protocols, and to the improvement of computed tomography (CT) image quality; among the various advantages is also a reduction in the radiation dose . In addition, many recent articles have highlighted the ability to use deep convolutional neural networks (DCNNs) to assist radiologist interpretation of radiographic images …”

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confidence: 99%

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

Banzato

Causin

Puppa

et al. 2019

Magnetic Resonance Imaging

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Background Grading of meningiomas is important in the choice of the most effective treatment for each patient. Purpose To determine the diagnostic accuracy of a deep convolutional neural network (DCNN) in the differentiation of the histopathological grading of meningiomas from MR images. Study Type Retrospective. Population In all, 117 meningioma‐affected patients, 79 World Health Organization [WHO] Grade I, 32 WHO Grade II, and 6 WHO Grade III. Field Strength/Sequence 1.5 T, 3.0 T postcontrast enhanced T1 W (PCT1W), apparent diffusion coefficient (ADC) maps (b values of 0, 500, and 1000 s/mm2). Assessment WHO Grade II and WHO Grade III meningiomas were considered a single category. The diagnostic accuracy of the pretrained Inception‐V3 and AlexNet DCNNs was tested on ADC maps and PCT1W images separately. Receiver operating characteristic curves (ROC) and area under the curve (AUC) were used to asses DCNN performance. Statistical Test Leave‐one‐out cross‐validation. Results The application of the Inception‐V3 DCNN on ADC maps provided the best diagnostic accuracy results, with an AUC of 0.94 (95% confidence interval [CI], 0.88–0.98). Remarkably, only 1/38 WHO Grade II–III and 7/79 WHO Grade I lesions were misclassified by this model. The application of AlexNet on ADC maps had a low discriminating accuracy, with an AUC of 0.68 (95% CI, 0.59–0.76) and a high misclassification rate on both WHO Grade I and WHO Grade II–III cases. The discriminating accuracy of both DCNNs on postcontrast T1W images was low, with Inception‐V3 displaying an AUC of 0.68 (95% CI, 0.59–0.76) and AlexNet displaying an AUC of 0.55 (95% CI, 0.45–0.64). Data Conclusion DCNNs can accurately discriminate between benign and atypical/anaplastic meningiomas from ADC maps but not from PCT1W images. Level of evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1152–1159.

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Low-Dose CT With a Residual Encoder-Decoder Convolutional Neural Network

Cited by 1,361 publications

References 43 publications

Deep Learning in Neuroradiology

Deep Learning in Neuroradiology

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

Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: A preliminary study

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