Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images

Suzuki, Kenji; Liu, Junchi; Zarshenas, Amin; Higaki, Toru; Fukumoto, Wataru; Awai, Kazuo

doi:10.1007/978-3-319-67389-9_39

Cited by 10 publications

(10 citation statements)

References 22 publications

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“…Therefore, we reported both results for the sake of fair comparison. The work of Suzuki et al [59] reported a dose reduction of 90% from 1.1 mSv to 0.11 mSv with MTANN. Their network is patched based and need to train multiple networks corresponding to different anatomic regions.…”

Section: Discussionmentioning

confidence: 96%

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

2018

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Low-dose computed tomography (LDCT) has offered tremendous benefits in radiation-restricted applications, but the quantum noise as resulted by the insufficient number of photons could potentially harm the diagnostic performance. Current image-based denoising methods tend to produce a blur effect on the final reconstructed results especially in high noise levels. In this paper, a deep learning-based approach was proposed to mitigate this problem. An adversarially trained network and a sharpness detection network were trained to guide the training process. Experiments on both simulated and real dataset show that the results of the proposed method have very small resolution loss and achieves better performance relative to state-of-the-art methods both quantitatively and visually.

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Section: Discussionmentioning

confidence: 96%

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

2018

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“…Deep learning, a type of machine learning [7], has been recently proposed for CT dose reduction and has shown the potential to reduce noise artifacts [8][9][10]. Most of these approaches are based on learning the relationship between LDCT images and standard-dose CT (SDCT) images by using a pair of low-dose and high-dose CT images.…”

Section: Introductionmentioning

confidence: 99%

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

et al. 2022

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Objectives To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images. Materials and methods LDCT (80 kVp, 100 mAs, n = 83) and SDCT (120 kVp, 200 mAs, n = 42) images were divided into training (42 LDCT and 42 SDCT) and validation (41 LDCT) sets. A generative adversarial network framework was used to train unpaired datasets. The trained deep learning method generated virtual SDCT images (VIs) from the original LDCT images (OIs). To test the proposed method, LDCT images (80 kVp, 262 mAs, n = 33) were collected from another CT scanner using iterative reconstruction (IR). Image analyses were performed to evaluate the qualities of VIs in the validation set and to compare the performance of deep learning and IR in the test set. Results The noise of the VIs was the lowest in both validation and test sets (all p<0.001). The mean CT number of the VIs for the portal vein and liver was lower than that of OIs in both validation and test sets (all p<0.001) and was similar to those of SDCT. The contrast-to-noise ratio of portal vein and the signal-to-noise ratio (SNR) of portal vein and liver of VIs were higher than those of SDCT (all p<0.05). The SNR of VIs in test sets was the highest among three images. Conclusion The deep learning method trained by unpaired datasets could reduce noise of LDCT images and showed similar performance to SAFIRE. It can be applied to LDCT images of older CT scanners without IR.

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“…The input data are subject to the processing in multiple hidden layers, followed by linear transformation in the output layer. Under this terminology, we can construct a variety of early and recent DLIP models including fully convolutional networks. We applied NNC for radiation dose reduction in CT and breast imaging …”

Section: Methodsmentioning

confidence: 99%

“…R is a feed-forward neural network regression model, parameterized by h. The neural network receives pixel information from an image region (image patch) R in the input image f. The input data are subject to the processing in multiple hidden layers, followed by linear transformation in the output layer. Under this terminology, we can construct a variety of early 26,28 and recent 32,33 DLIP models including fully convolutional networks. We applied NNC for radiation dose reduction in CT and breast imaging.…”

Section: B Neural Network Convolutionmentioning

confidence: 99%

“…We applied NNC for radiation dose reduction in CT and breast imaging. 32,34 Given a set of input and desired output (or "teaching") images, one can extract regions R and the corresponding output pixels, from the input and desired output images, respectively. Having this set, the goal of the machine learning is to find the best parameter vector h of an NNC model so that the predictions are closest to the desired values.…”

Section: B Neural Network Convolutionmentioning

confidence: 99%

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Separation of bones from soft tissue in chest radiographs: Anatomy‐specific orientation‐frequency‐specific deep neural network convolution

et al. 2019

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Purpose Lung nodules that are missed by radiologists as well as by computer‐aided detection (CAD) systems mostly overlap with ribs and clavicles. Removing the bony structures would result in better visualization of undetectable lesions. Our purpose in this study was to develop a virtual dual‐energy imaging system to separate ribs and clavicles from soft tissue in chest radiographs. Methods We developed a mixture of anatomy‐specific, orientation‐frequency‐specific (ASOFS) deep neural network convolution (NNC) experts. Anatomy‐specific (AS) NNC was designed to separate the bony structures from soft tissue in different lung segments. While an AS design was proposed previously under our massive‐training artificial neural networks (MTANN) framework, in this work, we newly mathematically defined an AS experts model as well as its learning and inference strategies in a probabilistic deep‐learning framework. In addition, in combination with our AS experts design, we newly proposed the orientation‐frequency‐specific (OFS) NNC models to decompose bone and soft‐tissue structures into specific orientation‐frequency components of different scales using a multi‐resolution decomposition technique. We trained multiple NNC models, each of which is an expert for a specific orientation‐frequency component in a particular anatomic segment. Perfect reconstruction discrete wavelet transform was used for OFS decomposition/reconstruction, while we introduced a soft‐gating layer to merge the predictions of AS NNC experts. To train our model, we used the bone images obtained from a dual‐energy system as the target (or teaching) images while the standard chest radiographs were used as the input to our model. The training, validation, and test were performed in a nested two‐fold cross‐validation manner. Results We used a database of 118 chest radiographs with pulmonary nodules to evaluate our NNC scheme. In order to evaluate our scheme, we performed quantitative and qualitative evaluation of the predicted bone and soft‐tissue images from our model as well as the ones of a state‐of‐the‐art technique where the “gold‐standard” dual‐energy bone and soft‐tissue images were used as the reference images. Both quantitative and qualitative evaluations demonstrated that our ASOFS NNC was superior to the state‐of‐the‐art bone‐suppression technique. Particularly, our scheme was better able to maintain the conspicuity of nodules and lung vessels, comparing to the reference technique, while it separated ribs and clavicles from soft tissue. Comparing to a state‐of‐the‐art bone suppression technique, our bone images had substantially higher (t−test; P < 0.01) similarity, in terms of structural similarity index (SSIM) and peak signal‐to‐noise ratio (PSNR), to the “gold−standard” dual−energy bone images. Conclusions Our deep ASOFS NNC scheme can decompose chest radiographs into their bone and soft‐tissue images accurately, offering the improved conspicuity of lung nodules and vessels, and therefore would be useful for radiologists as well as CAD systems in d...

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Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images

Cited by 10 publications

References 22 publications

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network

Denoising of pediatric low dose abdominal CT using deep learning based algorithm

Separation of bones from soft tissue in chest radiographs: Anatomy‐specific orientation‐frequency‐specific deep neural network convolution

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