计算机科学
断层摄影术
生物医学中的光声成像
光声层析成像
声速
组分(热力学)
平面的
重建算法
同种类的
迭代重建
质量(理念)
算法
计算机视觉
人工智能
声学
光学
数学
物理
计算机图形学(图像)
组合数学
量子力学
热力学
作者
Jenni Tick,Ben Cox,Andreas Hauptmann
标识
DOI:10.1016/j.pacs.2024.100597
摘要
Real-time applications in three-dimensional photoacoustic tomography from planar sensors rely on fast reconstruction algorithms that assume the speed of sound (SoS) in the tissue is homogeneous. Moreover, the reconstruction quality depends on the correct choice for the constant SoS. In this study, we discuss the possibility of ameliorating the problem of unknown or heterogeneous SoS distributions by using learned reconstruction methods. This can be done by modeling the uncertainties in the training data. In addition, a correction term can be included in the learned reconstruction method. We investigate the influence of both and while a learned correction component can improve reconstruction quality further, we show that a careful choice of uncertainties in the training data is the primary factor to overcome unknown SoS. We support our findings with simulated and in vivo measurements in 3D.
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