IEEE Trans Med Imaging. 2017 Sep;36(9):1858-1867. doi: 10.1109/TMI.2017.2704019. Epub 2017 May 12.
Optimal optoacoustic tomographic sampling is often hindered by the frequency-dependent directivity of ultrasound sensors, which can only be accounted for with an accurate 3-D model. Herein, we introduce a 3-D model-based reconstruction method applicable to optoacoustic imaging systems employing detection elements with arbitrary size and shape. The computational complexity and memory requirements are mitigated by introducing an efficient graphic processing unit (GPU)-based implementation of the iterative inversion. On-the-fly calculation of the entries of the model-matrix via a small look-up table avoids otherwise unfeasible storage of matrices typically occupying more than 300GB of memory. Superior imaging performance of the suggested method with respect to standard optoacoustic image reconstruction methods is first validated quantitatively using tissue-mimicking phantoms. Significant improvements in the spatial resolution, contrast to noise ratio and overall 3-D image quality are also reported in real tissues by imaging the finger of a healthy volunteer with a hand-held volumetric optoacoustic imaging system.
优化的光声层析采样通常受到超声传感器频率相关指向性的阻碍,这只能通过精确的 3D 模型来解释。在这里,我们引入了一种基于 3D 模型的重建方法,适用于采用任意大小和形状的探测元件的光声成像系统。通过引入基于图形处理单元(GPU)的迭代反演的有效实现,降低了计算复杂度和内存要求。通过小型查找表实时计算模型矩阵的条目,避免了通常占用超过 300GB 内存的矩阵的不可行存储。使用组织模拟体模对所提出的方法相对于标准光声图像重建方法的成像性能进行了定量验证。通过使用手持式容积光声成像系统对健康志愿者的手指进行成像,在真实组织中也报告了空间分辨率、对比噪声比和整体 3D 图像质量的显著改善。