A Resource-Efficient Deep Learning Framework for Low-Dose Brain Pet Image Reconstruction and Analysis

Fu, Y.; Dong, Shunjie; Liu, Yi; Li, Xue; Xu, Yuanfan; Li, Feng; Yang, Qian; Yu, Tianbai; Tian, Ming; Zhuo, Cheng

doi:10.1109/isbi52829.2022.9761617

Cited by 6 publications

(30 citation statements)

References 18 publications

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“…All identified articles were published over the last five years and the publication number increased every year with the highest number in 2022 [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ]. This increasing trend was in line with the one in radiology [ 1 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ,…”

Section: Resultsmentioning

confidence: 99%

“…Around two-thirds of these ( n = 11) were determined as being of high quality [ 82 , 84 , 87 , 90 , 92 , 97 , 102 , 103 , 105 , 109 , 111 ]. All low-quality ones were conference papers ( n = 12) [ 79 , 80 , 81 , 83 , 85 , 86 , 91 , 93 , 94 , 95 , 104 , 108 ]. The GAN was commonly applied to MRI ( n = 18) [ 77 , 78 , 83 , 84 , 87 , 90 , 97 , 101 , 103 , 104 , 105 , 106 , 108 , 109 , 110 , 111 , 112 , 113 ] and X-ray ( n = 13) [ 79 , 80 , 89 , 91 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 102 ,…”

Section: Resultsmentioning

confidence: 99%

“…All low-quality ones were conference papers ( n = 12) [ 79 , 80 , 81 , 83 , 85 , 86 , 91 , 93 , 94 , 95 , 104 , 108 ]. The GAN was commonly applied to MRI ( n = 18) [ 77 , 78 , 83 , 84 , 87 , 90 , 97 , 101 , 103 , 104 , 105 , 106 , 108 , 109 , 110 , 111 , 112 , 113 ] and X-ray ( n = 13) [ 79 , 80 , 89 , 91 , 92 , 94 , 95 , 96 , 98 , 99 , 100 , 102 , 107 ], and the others included computed tomography (CT) ( n = 4) [ 82 , 86 , 93 , 97 ], ultrasound ( n = 2) [ 85 , 88 ] and positron emission tomography (PET) ( n = 1) [ 81 ]. Although the basic GAN architecture was still popular among the included studies ( n = 11) [ 77 …”

Section: Resultsmentioning

confidence: 99%

“…Both image synthesis and data augmentation ( n = 12) [ 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 ], and image translation ( n = 12) [ 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 ] were the commonest application areas of GAN in pediatric radiology. Other GAN application areas included image segmentation ( n = 5) [ 84 , 85 , 86 , 87 , 88 ], image reconstruction ( n = 4) [ 80 , 81 , 82 , 83 ], disease diagnosis ( n = 3) [ 77 , 78 , 79 ], and image quality assessment ( n = 1) [ 113 ]. However, none of the GAN models involved in these studies were commercially available [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 ,…”

Section: Resultsmentioning

confidence: 99%

“…Other GAN application areas included image segmentation ( n = 5) [ 84 , 85 , 86 , 87 , 88 ], image reconstruction ( n = 4) [ 80 , 81 , 82 , 83 ], disease diagnosis ( n = 3) [ 77 , 78 , 79 ], and image quality assessment ( n = 1) [ 113 ]. However, none of the GAN models involved in these studies were commercially available [ 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 ]. For the twenty-nine studies which compared their GAN model performances with those of other approaches, all of them outperformed the others by 0.1–158.6% [ …”

Section: Resultsmentioning

confidence: 99%

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Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

2023

Children

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Generative artificial intelligence, especially with regard to the generative adversarial network (GAN), is an important research area in radiology as evidenced by a number of literature reviews on the role of GAN in radiology published in the last few years. However, no review article about GAN in pediatric radiology has been published yet. The purpose of this paper is to systematically review applications of GAN in pediatric radiology, their performances, and methods for their performance evaluation. Electronic databases were used for a literature search on 6 April 2023. Thirty-seven papers met the selection criteria and were included. This review reveals that the GAN can be applied to magnetic resonance imaging, X-ray, computed tomography, ultrasound and positron emission tomography for image translation, segmentation, reconstruction, quality assessment, synthesis and data augmentation, and disease diagnosis. About 80% of the included studies compared their GAN model performances with those of other approaches and indicated that their GAN models outperformed the others by 0.1–158.6%. However, these study findings should be used with caution because of a number of methodological weaknesses. For future GAN studies, more robust methods will be essential for addressing these issues. Otherwise, this would affect the clinical adoption of the GAN-based applications in pediatric radiology and the potential advantages of GAN could not be realized widely.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

2023

Children

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Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging

Zhou,

Fu,

Dong

et al. 2024

Eur J Nucl Med Mol Imaging

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Transformers in health: a systematic review on architectures for longitudinal data analysis

Siebra,

Kurpicz-Briki,

Wac

2024

Artif Intell Rev

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Transformers are state-of-the-art technology to support diverse Natural Language Processing (NLP) tasks, such as language translation and word/sentence predictions. The main advantage of transformers is their ability to obtain high accuracies when processing long sequences since they avoid the vanishing gradient problem and use the attention mechanism to maintain the focus on the information that matters. These features are fostering the use of transformers in other domains beyond NLP. This paper employs a systematic protocol to identify and analyze studies that propose new transformers’ architectures for processing longitudinal health datasets, which are often dense, and specifically focused on physiological, symptoms, functioning, and other daily life data. Our analysis considered 21 of 456 initial papers, collecting evidence to characterize how recent studies modified or extended these architectures to handle longitudinal multifeatured health representations or provide better ways to generate outcomes. Our findings suggest, for example, that the main efforts are focused on methods to integrate multiple vocabularies, encode input data, and represent temporal notions among longitudinal dependencies. We comprehensively discuss these and other findings, addressing major issues that are still open to efficiently deploy transformers architectures for longitudinal multifeatured healthcare data analysis.

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A Resource-Efficient Deep Learning Framework for Low-Dose Brain Pet Image Reconstruction and Analysis

Cited by 6 publications

References 18 publications

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Generative Adversarial Network (Generative Artificial Intelligence) in Pediatric Radiology: A Systematic Review

Intelligent ultrafast total-body PET for sedation-free pediatric [18F]FDG imaging

Transformers in health: a systematic review on architectures for longitudinal data analysis

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