Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Johnson, Patricia M.; Drangova, Maria

doi:10.1002/mrm.27772

Cited by 88 publications

(86 citation statements)

References 37 publications

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“…This was achieved by allowing the gradient calculated from the FCN classification loss to propagate back to the generator to implicitly convey the disease-related information to the generator. As a result, the classification loss that was propagated provided a momentum for the generator to generate images that contributed to 16 [6,20] 16 [6,20] 16 [4,20] 16 [7,20] 16 [6,20] 14 [7,20] [24,30] 23.5 [18,28] 30 [26,30] 26 [24,30] 23 [20,27] 29 [20,30] 22 [0,30] 29 [25,30] 18 [6,22] Three independent datasets including (a) the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, (b) the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL), and (c) the National Alzheimer's Coordinating Center (NACC) were used for this study lower cross-entropy loss and thus facilitated better image classification. The generator of the GAN model consists of three 3D convolutional blocks in which each convolutional operation was followed by batch normalization and rectified linear unit (ReLu) activation.…”

Section: Deep Learning Frameworkmentioning

confidence: 99%

“…Since its introduction, there has been a surge of interest in the application of GAN frameworks related to the brain. Some of the applications include image generation with improved properties such as achieving super resolution or better quality [6][7][8][9][10][11], data augmentation [12][13][14], segmentation [9,[13][14][15][16], image reconstruction [17][18][19][20], image-to-image translation [21][22][23][24], and motion correction [25,26]. While these important studies have demonstrated the exciting prospect of using GAN architectures, there is a limited amount of work that has focused on utilizing the generated images for subsequent tasks such as disease classification [27].…”

Section: Introductionmentioning

confidence: 99%

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Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

et al. 2021

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Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from magnetic resonance imaging (MRI) scans of multiple magnetic field strengths to enhance Alzheimer’s disease (AD) classification performance. Methods T1-weighted brain MRI scans from 151 participants of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), who underwent both 1.5-Tesla (1.5-T) and 3-Tesla imaging at the same time were selected to construct a GAN model. This model was trained along with a three-dimensional fully convolutional network (FCN) using the generated images (3T*) as inputs to predict AD status. Quality of the generated images was evaluated using signal to noise ratio (SNR), Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Natural Image Quality Evaluator (NIQE). Cases from the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL, n = 107) and the National Alzheimer’s Coordinating Center (NACC, n = 565) were used for model validation. Results The 3T*-based FCN classifier performed better than the FCN model trained using the 1.5-T scans. Specifically, the mean area under curve increased from 0.907 to 0.932, from 0.934 to 0.940, and from 0.870 to 0.907 on the ADNI test, AIBL, and NACC datasets, respectively. Additionally, we found that the mean quality of the generated (3T*) images was consistently higher than the 1.5-T images, as measured using SNR, BRISQUE, and NIQE on the validation datasets. Conclusion This study demonstrates a proof of principle that GAN frameworks can be constructed to augment AD classification performance and improve image quality.

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Section: Deep Learning Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

et al. 2021

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“…GANs have been used to synthesize strikingly realistic pictures of faces and inanimate objects (11,12). With respect to medical image data, GANs have shown promise across a wide range of applications (13), including simulated modality transformations (14)(15)(16)(17)(18), artifact reduction (19,20), and synthetic-image generation for supervised machine learning, thereby obviating patient-privacy protection of training data (21)(22)(23).…”

Section: Generative Adversarial Network (Gans)mentioning

confidence: 99%

Artificial intelligence in molecular imaging

Herskovits¹

2021

Ann Transl Med

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AI has, to varying degrees, affected all aspects of molecular imaging, from image acquisition to diagnosis. During the last decade, the advent of deep learning in particular has transformed medical image analysis. Although the majority of recent advances have resulted from neural-network models applied to image segmentation, a broad range of techniques has shown promise for image reconstruction, image synthesis, differential-diagnosis generation, and treatment guidance. Applications of AI for drug design indicate the way forward for using AI to facilitate molecular-probe design, which is still in its early stages.Deep-learning models have demonstrated increased efficiency and image quality for PET reconstruction from sinogram data. Generative adversarial networks (GANs), which are paired neural networks that are jointly trained to generate and classify images, have found applications in modality transformation, artifact reduction, and synthetic-PET-image generation. Some AI applications, based either partly or completely on neural-network approaches, have demonstrated superior differential-diagnosis generation relative to radiologists. However, AI models have a history of brittleness, and physicians and patients may not trust AI applications that cannot explain their reasoning. To date, the majority of molecular-imaging applications of AI have been confined to research projects, and are only beginning to find their ways into routine clinical workflows via commercialization and, in some cases, integration into scanner hardware. Evaluation of actual clinical products will yield more realistic assessments of AI's utility in molecular imaging.

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“…Image generation can be conditional on a specific, which allows the cGAN model to encode particular patterns in the training process, so that the D network can generate output images with desired [11]. The cGAN architecture has been successfully applied to medical imaging tasks [12][13].…”

Section: A Deep Convolutional Generative Adversarial Networkmentioning

confidence: 99%

Enhancing Automated COVID-19 Chest X-ray Diagnosis by Image-to-Image GAN Translation

Liang

Huang

et al. 2020

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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The severe pneumonia induced by the infection of the SARS-CoV-2 virus causes massive death in the ongoing COVID-19 pandemic. The early detection of the SARS-CoV-2 induced pneumonia relies on the unique patterns of the chest X-Ray images. Deep learning is a data-greedy algorithm to achieve high performance when adequately trained. A common challenge for machine learning in the medical domain is the accessibility to properly annotated data. In this study, we apply a conditional adversarial network (cGAN) to perform image to image (Pix2Pix) translation from the non-COVID-19 chest X-Ray domain to the COVID-19 chest X-Ray domain. The objective is to learn a mapping from the normal chest X-Ray visual patterns to the COVID-19 pneumonia chest X-ray patterns. The original dataset has a typical imbalanced issue because it contains only 219 COVID-19 positive images but has 1,341 images for normal chest X-Ray and 1,345 images for viral pneumonia. A U-Net based architecture is applied for the image-to-image translation to generate synthesized COVID-19 X-Ray chest images from the normal chest X-ray images. A 50-convolutional-layer residual net (ResNet) architecture is applied for the final classification task. After training the GAN model for 100 epochs, we use the GAN generator to translate 1,100 COVID-19 images from the normal X-Ray to form a balanced training dataset (3,762 images) for the classification task. The ResNet based classifier trained by the enhanced dataset achieves the classification accuracy of 97.8% compared to 96.1% in the transfer learning mode. When trained with the original imbalanced dataset, the model achieves an accuracy of 96.1% compared to 95.6% in the training from trainby-scratch model. In addition, the classifier trained by the enhanced dataset has more stable measures in precision, recall, and F1 scores across different image classes. We conclude that the GAN-based data enhancement strategy is applicable to most medical image pattern recognition tasks, and it provides an effective way to solve the common expertise dependence issue in the medical domain.

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Conditional generative adversarial network for 3D rigid‐body motion correction in MRI

Cited by 88 publications

References 37 publications

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning

Artificial intelligence in molecular imaging

Enhancing Automated COVID-19 Chest X-ray Diagnosis by Image-to-Image GAN Translation

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