Dielectric Breast Phantoms by Generative Adversarial Network

Shao, Wenbo; Zhou, Beibei

doi:10.1109/tap.2021.3121149

Cited by 4 publications

(2 citation statements)

References 34 publications

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“…Besides the U-Net, some generative adversarial networks (GANs), which are instinctively suitable for the imaging generation tasks, also prove to work well for electromagnetic pixelated imaging. In [48], GAN was trained to generate virtual dielectric anatomical breast phantoms that were similar to real human breasts, which realized data expansion for further researches. In the forward electromagnetic scattering problem, a framework of GAN, named pix2pix [49], was used to immediately predict the graphical induced currents when given permittivity contrast and incident field [50].…”

Section: Introductionmentioning

confidence: 99%

Towards Intelligent Electromagnetic Inverse Scattering Using Deep Learning Techniques and Information Metasurfaces

Zhang

Cui

2023

IEEE J. Microw.

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Electromagnetic inverse scattering (EMIS) is uniquely positioned among many inversion methods because it enables to image the scene in a contactless, quantitative and super-resolution way. Although many EMIS approaches have been proposed to date, they usually suffer from two important challenges, i.e., time-consuming data acquisition and computationally -prohibitive data post processing, especially for large-scale objects with high and even moderate contrasts. To tackle the challenges, we here propose a framework of intelligent EMIS with the aid of deep learning techniques and information metasurfaces, enabling to the efficient data acquisition and instant data processing in a smart way. Towards this goal, as illustrative examples, we considerably extend the canonical contrast source inversion (CSI) algorithm, a canonical EMIS method by updating the contrast via the generative adversarial network (GAN), an unsupervised deep learning approach, leading to a novel physics-informed unsupervised deep learning method for EMIS, referred to as CSI-GAN in short. Compared with existing deep learning solutions for EMIS, our method relies on the supervision of physical law instead of the labeled training dataset, beating the bottleneck arising from the collection of labeled training datasets. Furthermore, we propose a scheme of adaptive data acquisition with the use of information metasurface in a cost-efficiency way, remarkably reducing the number of measurements and thus speeding up the data acquisition but maintaining the reconstruction's quality. Illustrative examples are provided to demonstrate the performance gain in terms of reconstruction quality, showing the promising potentials for providing the intelligent scheme for the EMIS problems.INDEX TERMS MTT 70th Anniversary Special Issue, electromagnetic inverse scattering (EMIS), contrast source inversion, physics-informed neural network (PINN), unsupervised deep learning method, information metasurface.This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.

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Section: Introductionmentioning

confidence: 99%

Towards Intelligent Electromagnetic Inverse Scattering Using Deep Learning Techniques and Information Metasurfaces

Zhang

Cui

2023

IEEE J. Microw.

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“…The generated phantoms will contain activity distributions and attenuation maps to reflect anatomical and uptake variations that are commonly seen clinically in PD. Although GAN has been adopted to develop medical anatomical phantoms for MRI [ 29 ], CT [ 28 ] and in microwave medical imaging [ 30 , 31 ] research, to our best knowledge, the current paper is the first time that biomarker-distribution phantoms and attenuation maps have been developed by the GAN technique. With the generative network, unlimited number of phantoms with more variations can be produced, which will then be used to develop more robust AI-based PD SPECT imaging algorithms than former AI models [ 20 , 21 , 22 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging

Shao

Leung

et al. 2022

Diagnostics

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While machine learning (ML) methods may significantly improve image quality for SPECT imaging for the diagnosis and monitoring of Parkinson’s disease (PD), they require a large amount of data for training. It is often difficult to collect a large population of patient data to support the ML research, and the ground truth of lesion is also unknown. This paper leverages a generative adversarial network (GAN) to generate digital brain phantoms for training ML-based PD SPECT algorithms. A total of 594 PET 3D brain models from 155 patients (113 male and 42 female) were reviewed and 1597 2D slices containing the full or a portion of the striatum were selected. Corresponding attenuation maps were also generated based on these images. The data were then used to develop a GAN for generating 2D brain phantoms, where each phantom consisted of a radioactivity image and the corresponding attenuation map. Statistical methods including histogram, Fréchet distance, and structural similarity were used to evaluate the generator based on 10,000 generated phantoms. When the generated phantoms and training dataset were both passed to the discriminator, similar normal distributions were obtained, which indicated the discriminator was unable to distinguish the generated phantoms from the training datasets. The generated digital phantoms can be used for 2D SPECT simulation and serve as the ground truth to develop ML-based reconstruction algorithms. The cumulated experience from this work also laid the foundation for building a 3D GAN for the same application.

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Near-Field Microwave Scattering Formulation by a Deep Learning Method

Shao

Zhou²

2022

IEEE Trans. Microwave Theory Techn.

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Dielectric Breast Phantoms by Generative Adversarial Network

Cited by 4 publications

References 34 publications

Towards Intelligent Electromagnetic Inverse Scattering Using Deep Learning Techniques and Information Metasurfaces

Towards Intelligent Electromagnetic Inverse Scattering Using Deep Learning Techniques and Information Metasurfaces

Generation of Digital Brain Phantom for Machine Learning Application of Dopamine Transporter Radionuclide Imaging

Near-Field Microwave Scattering Formulation by a Deep Learning Method

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