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首页> 外文期刊>Journal of Biophotonics >LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets
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LV-GAN: A deep learning approach for limited-view optoacoustic imaging based on hybrid datasets

机译：LV-GaN：基于混合数据集的有限视图光声成像的深度学习方法

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摘要

The optoacoustic imaging (OAI) methods are rapidly evolving for resolving optical contrast in medical imaging applications. In practice, measurement strategies are commonly implemented under limited-view conditions due to oversized image objectives or system design limitations. Data acquired by limited-view detection may impart artifacts and distortions in reconstructed optoacoustic (OA) images. We propose a hybrid data-driven deep learning approach based on generative adversarial network (GAN), termed as LV-GAN, to efficiently recover high quality images from limited-view OA images. Trained on both simulation and experiment data, LV-GAN is found capable of achieving high recovery accuracy even under limited detection angles less than 60 degrees. The feasibility of LV-GAN for artifact removal in biological applications was validated by ex vivo experiments based on two different OAI systems, suggesting high potential of a ubiquitous use of LV-GAN to optimize image quality or system design for different scanners and application scenarios.
机译：在医学成像应用中，光声成像（OAI）方法在解决光学对比度方面正在迅速发展。在实践中，由于超大的图像目标或系统设计限制，测量策略通常在有限的视野条件下实施。通过有限视野检测获取的数据可能会在重建的光声（OA）图像中产生伪影和失真。我们提出了一种基于生成性对抗网络的混合数据驱动深度学习方法，称为LV-GAN，以有效地从有限视角的OA图像中恢复高质量的图像。通过模拟和实验数据的训练，发现LV-GAN即使在小于60度的有限探测角度下也能实现高恢复精度。基于两种不同OAI系统的离体实验验证了LV-GAN在生物应用中去除伪影的可行性，表明LV-GAN在优化不同扫描仪和应用场景的图像质量或系统设计方面的广泛应用潜力很大。

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