Quantitative Reconstruction of Dielectric Properties Based on Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography

Luo, Zhaoxu; Li, Chenzhe; Liu, Dantong; Wang, Baosheng; Zhang, Lejia; Ma, Yuexin; Xu, Kuiwen; Wang, Xiong

doi:10.1109/tmtt.2023.3234466

Cited by 7 publications

(1 citation statement)

References 52 publications

(71 reference statements)

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“…In recent years, data-driven deep learning (DL) approaches have been widely used to solve inverse problems, and have shown promising potential in EPs reconstruction speed and noise robustness. [36][37][38][39][40] However, the reconstruction accuracy, data generalization ability, and training data acquisition still limit their application, especially for the human head, which has complex tissues and structures. DL-based methods also began to be applied to T 1 reconstruction, [41][42][43] benefiting the speed of T 1 reconstruction and thus benefiting wEPT reconstruction.…”

Section: Introductionmentioning

confidence: 99%

MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

Zheng,

Lou,

Huang

et al. 2024

NMR in Biomedicine

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Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non‐invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high‐resolution EPs reconstruction based on water content maps by using a physics‐informed network (PIN‐wEPT). The proposed method utilizes standard clinical protocols and conventional multi‐channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN‐wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

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Section: Introductionmentioning

confidence: 99%

MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

Zheng,

Lou,

Huang

et al. 2024

NMR in Biomedicine

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HASR-TAI: Hybrid model-based interpretable network and super-resolution network for thermoacoustic imaging

Liu,

Wan,

Shang

2023

Applied Physics Letters

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Microwave induced thermoacoustic tomography has shown promise for noninvasive and non-ionizing early tumor detection. Nowadays, thermoacoustic reconstruction methods based on deep learning have achieved good and time-efficient results. However, both deep learning methods based on the initial thermoacoustic image and end-to-end methods lack interpretability due to the black-box property of neural networks. In this Letter, we propose an interpretable end-to-end network structure comprising an unrolling part and a super-resolution part. In the unrolling part, a deep unfolding network interprets the iterations of the model-based algorithm based on compressed sensing as layers of the network. Subsequently, a fast and efficient super-resolution neural network maps the low-resolution image into the super-resolution space. Two breast models with different sizes of tumor targets are used for validation. By comparing with the traditional method and the deep learning method, the proposed method demonstrates superior performance in image quality and imaging time. Moreover, the parameters in the network hold physical significance, offering the potential for the interpretable end-to-end network in thermoacoustic imaging.

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Performance Evaluation of Focused Microwave Brain Hyperthermia Guided by Microwave-Induced Thermoacoustic Tomography

Xi,

Wang,

et al. 2023

IEEE J. Electromagn. RF Microw. Med. Biol.

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Quantitative Reconstruction of Dielectric Properties Based on Deep-Learning-Enabled Microwave-Induced Thermoacoustic Tomography

Cited by 7 publications

References 52 publications

MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

MR‐based electrical property tomography using a physics‐informed network at 3 and 7 T

HASR-TAI: Hybrid model-based interpretable network and super-resolution network for thermoacoustic imaging

Performance Evaluation of Focused Microwave Brain Hyperthermia Guided by Microwave-Induced Thermoacoustic Tomography

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