揭示精准性：一种利用稀疏数据增强光声成像的数据驱动方法。
Unveiling precision: a data-driven approach to enhance photoacoustic imaging with sparse data.
作者信息
Huang Mengyuan, Liu Wu, Sun Guocheng, Shi Chaojing, Liu Xi, Han Kaitai, Liu Shitou, Wang Zijun, Xie Zhennian, Guo Qianjin
机构信息
Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing 102617, China.
Xiyuan Hospital, Chinese Academy of Traditional Chinese Medicine, China.
出版信息
Biomed Opt Express. 2023 Dec 4;15(1):28-43. doi: 10.1364/BOE.506334. eCollection 2024 Jan 1.
Abstract
This study presents the Fourier Decay Perception Generative Adversarial Network (FDP-GAN), an innovative approach dedicated to alleviating limitations in photoacoustic imaging stemming from restricted sensor availability and biological tissue heterogeneity. By integrating diverse photoacoustic data, FDP-GAN notably enhances image fidelity and reduces artifacts, particularly in scenarios of low sampling. Its demonstrated effectiveness highlights its potential for substantial contributions to clinical applications, marking a significant stride in addressing pertinent challenges within the realm of photoacoustic acquisition techniques.
摘要
本研究提出了傅里叶衰减感知生成对抗网络（FDP-GAN），这是一种创新方法，致力于缓解由于传感器可用性受限和生物组织异质性而导致的光声成像局限性。通过整合各种光声数据，FDP-GAN显著提高了图像保真度并减少了伪影，尤其是在低采样情况下。其已证明的有效性突出了它对临床应用做出重大贡献的潜力，标志着在解决光声采集技术领域的相关挑战方面迈出了重要一步。
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