Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

Lei, Yang; Dong, Xue; Wang, Tonghe; Higgins, Kristin; Tian, Liang; Curran, Walter J.; Mao, Hui; Nye, Jonathon A.; Yang, Xiaofeng

doi:10.1088/1361-6560/ab4891

Cited by 89 publications

(63 citation statements)

References 36 publications

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“…Multiple variants of GAN include conditional GAN (cGan) [109], InfoGan [16], , CycleGAN [184], StarGan [19] and so on. In medical imaging, GAN has been used to perform image synthesis for inter-or intra-modality, such as MR to synthetic CT [84,89], CT to synthetic MR [27,83], CBCT to synthetic CT [58], non-attenuation correction (non-AC) PET to CT [26], low-dose PET to synthetic full-dose PET [88], non-AC PET to AC PET [28], low-dose CT to full-dose CT [159] and so on. In medical image registration, GAN is usually used to either provide additional regularization or translate multi-modal registration to unimodal registration.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

“…The field of medical image registration has been evolving rapidly with hundreds of papers published each year. Recently, DL-based methods have changed the landscape of medical image processing research and achieved the-state-of-art performances in many applications [25,27,45,58,84,85,86,88,89,97,98,156,157,158,160,161]. However, deep learning in medical image registration has not been extensively studied until the past three to four years.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning in medical image registration: a review

et al. 2020

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This paper presents a review of deep learning (DL) based medical image registration methods. We summarized the latest developments and applications of DL-based registration methods in the medical field. These methods were classified into seven categories according to their methods, functions and popularity. A detailed review of each category was presented, highlighting important contributions and identifying specific challenges. A short assessment was presented following the detailed review of each category to summarize its achievements and future potentials. We provided a comprehensive comparison among DL-based methods for lung and brain registration using benchmark datasets. Lastly, we analyzed the statistics of all the cited works from various aspects, revealing the popularity and future trend of DL-based medical image registration. 1 arXiv:1912.12318v1 [eess.IV] 27 Dec 2019 1. Summarize the latest developments in DL-based medical image registration. 2. Highlight contributions, identify challenges and outline future trends. 3. Provide detailed statistics on recent publications from different perspectives. 2 Deep Learning 2 Convolutional Neural NetworkConvolutional neural network (CNN) is a class of deep neural networks with regularized multilayer perceptron. CNN uses convolution operation in place of general matrix multiplication in simple neural networks. The convolutional filters and operations in CNN make it suitable for visual imagery signal processing. Because of its excellent feature extraction ability, CNN is one of the most successful models for image analysis. Since the breakthrough of AlexNet [79], many variants of CNN have been proposed and have achieved the-state-of-art performances in various image processing tasks. A typical CNN usually consists of multiple convolutional layers, max pooling layers, batch normalization layers, dropout layers, a sigmoid or softmax layer. In each convolutional layer, multiple channels of feature maps were extracted by sliding trainable convolutional kernels across the input feature maps. Hierarchical features with high-level abstraction are extracted using multiple convolutional layers. These feature maps usually go through multiple fully connected layer before reaching the final decision layer. Max pooling layers are often used to reduce the image sizes and to promote spatial invariance of the network. Batch normalization is used to reduce internal covariate shift among the training samples. Weight regularization and dropout layers are used to alleviate data overfitting. The loss function is defined as the difference between the predicted and the target output. CNN is usually trained by minimizing the loss via gradient back propagation using optimization methods. Many different types of network architectures have been proposed to improve the performance of CNN [93]. U-Net proposed by Ronneberger et al. is among one of the most used network architectures [120]. U-Net was originally used to perform neuronal structures segmentation. U-Net adopts symmetrical contractiv...

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning in medical image registration: a review

et al. 2020

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“…An input bypasses these hidden layers via the residual connection, thus the hidden layers enforces minimization of a residual image between the source and ground truth target images, thereby minimizing noise and artifacts. [25][26][27][28][29] In contrast, dense blocks concatenate outputs from previous layers rather than using feed-forward summation as in a standard AE block, capturing multifrequency (high and low frequency) information to better represent the mapping from the source image modality to the target image modality. Dense blocks are therefore commonly used in inter-modality image synthesis such as MR-to-CT and PET-to-CT. 12,[30][31][32][33][34] Within GANs, the AEs and its variants are commonly used for both generative and discriminative networks.…”

Section: B | U-netmentioning

confidence: 99%

A review on medical imaging synthesis using deep learning and its clinical applications

Wang

Lei

et al. 2020

J Applied Clin Med Phys

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This paper reviewed the deep learning‐based studies for medical imaging synthesis and its clinical application. Specifically, we summarized the recent developments of deep learning‐based methods in inter‐ and intra‐modality image synthesis by listing and highlighting the proposed methods, study designs, and reported performances with related clinical applications on representative studies. The challenges among the reviewed studies were then summarized with discussion.

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“…Consequently, our proposed S-CycleGAN model actually takes the count variation into account in the training and can be used for a relatively widespread dose levels in complicated clinical situations. The recently published paper [48] uses a very similar method, the CycleGAN, for LDPET denoising, but they still didn't investigate the metrics of SUV max and robustness to different count levels.…”

Section: Discussionmentioning

confidence: 99%

Study of low-dose PET image recovery using supervised learning with CycleGAN

Zhao¹,

Zhou²,

Gao³

et al. 2020

PLoS ONE

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PET is a popular medical imaging modality for various clinical applications, including diagnosis and image-guided radiation therapy. The low-dose PET (LDPET) at a minimized radiation dosage is highly desirable in clinic since PET imaging involves ionizing radiation, and raises concerns about the risk of radiation exposure. However, the reduced dose of radioactive tracers could impact the image quality and clinical diagnosis. In this paper, a supervised deep learning approach with a generative adversarial network (GAN) and the cycle-consistency loss, Wasserstein distance loss, and an additional supervised learning loss, named as S-CycleGAN, is proposed to establish a non-linear end-to-end mapping model, and used to recover LDPET brain images. The proposed model, and two recently-published deep learning methods (RED-CNN and 3D-cGAN) were applied to 10% and 30% dose of 10 testing datasets, and a series of simulation datasets embedded lesions with different activities, sizes, and shapes. Besides vision comparisons, six measures including the NRMSE, SSIM, PSNR, LPIPS, SUV max and SUV mean were evaluated for 10 testing datasets and 45 simulated datasets. Our S-CycleGAN approach had comparable SSIM and PSNR, slightly higher noise but a better perception score and preserving image details, much better SUV mean and SUV max , as compared to RED-CNN and 3D-cGAN. Quantitative and qualitative evaluations indicate the proposed approach is accurate, efficient and robust as compared to other stateof-the-art deep learning methods.

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Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks

Cited by 89 publications

References 36 publications

Deep learning in medical image registration: a review

Deep learning in medical image registration: a review

A review on medical imaging synthesis using deep learning and its clinical applications

Study of low-dose PET image recovery using supervised learning with CycleGAN

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