Optimal self-calibration of tomographic reconstruction parameters in whole-body small animal optoacoustic imaging.
Optimal self-calibration of tomographic reconstruction parameters in whole-body small animal optoacoustic imaging.
复制标题
DOI：
10.1016/j.pacs.2014.09.002
复制
发表时间：
2014-09
期刊：
影响因子：
7.9
通讯作者：
Razansky D
中科院分区：
文献类型：
--
作者：
Mandal S;Nasonova E;Deán-Ben XL;Razansky D
在层析光声成像中，需要充分选择与介质的光和超声传播特性相关的多个参数，以便准确地恢复局部光学吸光度图。在成像物体和周围介质中的声速是一个关键参数，通常假设声速是均匀的。声音值的实际速度和预测速度之间的不匹配可能导致图像失真，但可以通过基于图像清晰度度量的手动或自动优化来减轻。尽管一些基于图像清晰度度量的简单方法可以很容易地减轻高对比度和锐利图像特征存在下的失真，但它们可能无法提供足够的性能来处理平滑信号变化，因为通常存在于来自小动物的真实全身光声图像中。因此，在这项工作中提出了三种新的混合方法，这些方法在小鼠体内实验中被证明优于成熟的自动聚焦算法。
In tomographic optoacoustic imaging, multiple parameters related to both light and ultrasound propagation characteristics of the medium need to be adequately selected in order to accurately recover maps of local optical absorbance. Speed of sound in the imaged object and surrounding medium is a key parameter conventionally assumed to be uniform. Mismatch between the actual and predicted speed of sound values may lead to image distortions but can be mitigated by manual or automatic optimization based on metrics of image sharpness. Although some simple approaches based on metrics of image sharpness may readily mitigate distortions in the presence of highly contrasting and sharp image features, they may not provide an adequate performance for smooth signal variations as commonly present in realistic whole-body optoacoustic images from small animals. Thus, three new hybrid methods are suggested in this work, which are shown to outperform well-established autofocusing algorithms in mouse experiments in vivo.