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Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography

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Abstract

Ultrasound computed tomography (USCT) is an emerging technology that offers a noninvasive and radiation-free imaging approach with high sensitivity, making it promising for the early detection and diagnosis of breast cancer. The speed-of-sound (SOS) parameter plays a crucial role in distinguishing between benign masses and breast cancer. However, traditional SOS reconstruction methods face challenges in achieving a balance between resolution and computational efficiency, which hinders their clinical applications due to high computational complexity and long reconstruction times. In this paper, we propose a novel and efficient approach for direct SOS image reconstruction based on an improved conditional generative adversarial network. The generator directly reconstructs SOS images from time-of-flight information, eliminating the need for intermediate steps. Residual spatial-channel attention blocks are integrated into the generator to adaptively determine the relevance of arrival time from the transducer pair corresponding to each pixel in the SOS image. An ablation study verified the effectiveness of this module. Qualitative and quantitative evaluation results on breast phantom datasets demonstrate that this method is capable of rapidly reconstructing high-quality SOS images, achieving better generation results and image quality. Therefore, we believe that the proposed algorithm represents a new direction in the research area of USCT SOS reconstruction.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62122072, 12174368, and 61705216), the National Key R & D Program of China (Grant No. 2022YFA1404400), the Anhui Provincial Science and Technology Department (Grant Nos. 202203a07020020 and 18030801138), and the Research Fund of the University of Science and Technology of China (YD2090002015).

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Authors and Affiliations

  1. College of Engineering Science, University of Science and Technology of China, Hefei, 230026, Anhui, China

    Xiaoyun Long & Chao Tian

  2. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, Anhui, China

    Xiaoyun Long & Chao Tian

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  1. Xiaoyun Long
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Correspondence to Chao Tian.

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Long, X., Tian, C. Spatial and channel attention-based conditional Wasserstein GAN for direct and rapid image reconstruction in ultrasound computed tomography. Biomed. Eng. Lett. 14, 57–68 (2024). https://doi.org/10.1007/s13534-023-00310-x

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  • DOI: https://doi.org/10.1007/s13534-023-00310-x

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