Deep learning optoacoustic tomography with sparse data
Deep learning optoacoustic tomography with sparse data
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DOI：
10.1038/s42256-019-0095-3
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发表时间：
2019-10-01
影响因子：
23.8
通讯作者：
Razansky, Daniel
中科院分区：
文献类型：
--
作者：
Davoudi, Neda;Dean-Ben, Xose Luis;Razansky, Daniel
光声（光声）成像和断层扫描领域的快速发展是由对分辨率、速度、灵敏度、深度和对比度方面更好的成像性能的不断需求驱动的。在实践中，数据采集策略通常涉及断层扫描数据的次优采样，导致不可避免的性能折衷和图像质量降低。我们提出了一个新的框架，用于基于深度卷积神经网络从稀疏光声数据中有效恢复图像质量，并展示了其在体内全身小鼠成像中的性能。为了生成用于最佳训练的精确的高分辨率参考图像，设计了能够从活小鼠获得上级横截面图像质量的全视图断层扫描仪。当提供从基本上欠采样的数据或有限视图扫描重建的图像时，经过训练的网络能够增强任意定向结构的可见性并恢复预期的图像质量。值得注意的是，该网络还消除了从密集采样数据渲染的参考图像中存在的一些重建伪影。当使用合成或体模数据进行训练时，没有实现可比的增益，这强调了使用全视图扫描仪获取的高质量体内图像进行训练的重要性。这种新方法可以通过减轻常见的图像伪影，增强解剖对比度和图像量化能力，加速数据采集和图像重建方法，同时促进实用和负担得起的成像系统的开发，使许多光声成像应用受益。所建议的方法仅对图像域数据进行操作，因此可以无缝地应用于用其他方式重建的伪影图像。光声成像可以实现高的空间和时间分辨率，但图像质量往往受到次优数据采集的影响。开发了一种采用深度学习从稀疏或有限视图光声扫描中恢复高质量图像的新方法，并在体内全身小鼠成像中进行了演示。
The rapidly evolving field of optoacoustic (photoacoustic) imaging and tomography is driven by a constant need for better imaging performance in terms of resolution, speed, sensitivity, depth and contrast. In practice, data acquisition strategies commonly involve sub-optimal sampling of the tomographic data, resulting in inevitable performance trade-offs and diminished image quality. We propose a new framework for efficient recovery of image quality from sparse optoacoustic data based on a deep convolutional neural network and demonstrate its performance with whole body mouse imaging in vivo. To generate accurate high-resolution reference images for optimal training, a full-view tomographic scanner capable of attaining superior cross-sectional image quality from living mice was devised. When provided with images reconstructed from substantially undersampled data or limited-view scans, the trained network was capable of enhancing the visibility of arbitrarily oriented structures and restoring the expected image quality. Notably, the network also eliminated some reconstruction artefacts present in reference images rendered from densely sampled data. No comparable gains were achieved when the training was performed with synthetic or phantom data, underlining the importance of training with high-quality in vivo images acquired by full-view scanners. The new method can benefit numerous optoacoustic imaging applications by mitigating common image artefacts, enhancing anatomical contrast and image quantification capacities, accelerating data acquisition and image reconstruction approaches, while also facilitating the development of practical and affordable imaging systems. The suggested approach operates solely on image-domain data and thus can be seamlessly applied to artefactual images reconstructed with other modalities. Optoacoustic imaging can achieve high spatial and temporal resolution but image quality is often compromised by suboptimal data acquisition. A new method employing deep learning to recover high-quality images from sparse or limited-view optoacoustic scans has been developed and demonstrated for whole-body mouse imaging in vivo.