Lutzweiler Christian, Tzoumas Stratis, Rosenthal Amir, Ntziachristos Vasilis, Razansky Daniel
IEEE Trans Med Imaging. 2016 Feb;35(2):674-84. doi: 10.1109/TMI.2015.2490799. Epub 2015 Oct 14.
The concept of sparsity is extensively exploited in the fields of data acquisition and image processing, contributing to better signal-to-noise and spatio-temporal performance of the various imaging methods. In the field of optoacoustic tomography, the image reconstruction problem is often characterized by computationally extensive inversion of very large datasets, for instance when acquiring volumetric multispectral data with high temporal resolution. In this article we seek to accelerate accurate model-based optoacoustic inversions by identifying various sources of sparsity in the forward and inverse models as well as in the single- and multi-frame representation of the projection data. These sources of sparsity are revealed through appropriate transformations in the signal, model and image domains and are subsequently exploited for expediting image reconstruction. The sparsity-based inversion scheme was tested with experimental data, offering reconstruction speed enhancement by a factor of 40 to 700 times as compared with the conventional iterative model-based inversions while preserving similar image quality. The demonstrated results pave the way for achieving real-time performance of model-based reconstruction in multi-dimensional optoacoustic imaging.
稀疏性概念在数据采集和图像处理领域得到了广泛应用，有助于提高各种成像方法的信噪比和时空性能。在光声断层扫描领域，图像重建问题通常表现为对非常大的数据集进行计算量巨大的反演，例如在获取具有高时间分辨率的体积多光谱数据时。在本文中，我们试图通过识别正模型和反模型以及投影数据的单帧和多帧表示中的各种稀疏性来源，来加速基于模型的光声反演。这些稀疏性来源通过信号、模型和图像域中的适当变换得以揭示，并随后用于加速图像重建。基于稀疏性的反演方案通过实验数据进行了测试，与传统的基于迭代模型的反演相比，重建速度提高了40到700倍，同时保持了相似的图像质量。所展示的结果为在多维光声成像中实现基于模型的重建的实时性能铺平了道路。
