Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

Apostolopoulos, Ioannis D.; Παπαθανασίου, Νικόλαος; Apostolopoulos, Dimitris; Panayiotakis, George

doi:10.1007/s00259-022-05805-w

Cited by 16 publications

(17 citation statements)

References 113 publications

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“…It is worth noticing that other imaging improvements include motion correction and artefact removal. Several studies are addressing such issues in the PET modality [ 73 ]. In the current review, such studies were not found in the literature.…”

Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.

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Section: Resultsmentioning

confidence: 99%

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

et al. 2023

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“…It is challenging to store and pro-cess such enormous and complex datasets, which may be addressed by some automation forms us-ing more comprehensive approaches (e.g. segmen-tation) [81][82][83][84][85][86] or artificial intelligence [39,75,[87][88][89] Table 2 Characteristics and Challenges/Opportunities of total-body PET scanners.…”

Section: Opportunities and Challenges In Dynamic Total-body Pet Imagingmentioning

confidence: 99%

“…Another research interest in future work is the implementation of artificial intelligence (AI) for the totalbody PET imaging [87,150]. As a subcategory of AI, deep learning (DL) techniques, e.g., convolutional neural network (CNN) [151] and generative adversarial network (GAN) [89], have been extensively used in PET for solving a wide variety of problems involving image reconstruction [152][153][154], denoising [155,156], segmentation [157,158] as well as quantitation [159,160]. A few initial attempts have been made to extract the flux (K i ) from total-body PET studies by DL methods [61,148,161].…”

Section: Other Approachesmentioning

confidence: 99%

Quantitation of Dynamic Total-Body PET Imaging: Recent Developments and Future Perspectives

2023

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Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania) and Biograph Vision Quadra (Siemens Healthineers), further stimulate valuable inspiration to map kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. In this contribution, the published dynamic total-body PET datasets are outlined and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model) and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments and future perspectives of these methodologies for a diverse-tissue environment.

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“…The BiLSTM-CRF model was applied to the i2b2/UTHealth 2010 dataset, and an F1 value of 76.51 was achieved. Cross-Correlation Multi-Neural Networks (CCMNN) [ 6 ] and Generative Adversarial Networks (GAN) [ 24 , 25 , 26 ] have been widely applied to various image-processing tasks, such as image classification and generation. CCMNN improves the performance of picture classification tasks by utilizing cross-correlation across several neural networks.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

2023

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The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model’s transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.

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Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review

Cited by 16 publications

References 113 publications

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies

Quantitation of Dynamic Total-Body PET Imaging: Recent Developments and Future Perspectives

Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

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