Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
Phys Med Biol. 2012 Sep 7;57(17):5399-423. doi: 10.1088/0031-9155/57/17/5399. Epub 2012 Aug 3.
Iterative image reconstruction algorithms for optoacoustic tomography (OAT), also known as photoacoustic tomography, have the ability to improve image quality over analytic algorithms due to their ability to incorporate accurate models of the imaging physics, instrument response and measurement noise. However, to date, there have been few reported attempts to employ advanced iterative image reconstruction algorithms for improving image quality in three-dimensional (3D) OAT. In this work, we implement and investigate two iterative image reconstruction methods for use with a 3D OAT small animal imager: namely a penalized least-squares (PLS) method employing a quadratic smoothness penalty and a PLS method employing a total variation norm penalty. The reconstruction algorithms employ accurate models of the ultrasonic transducer impulse responses. Experimental data sets are employed to compare the performances of the iterative reconstruction algorithms to that of a 3D filtered backprojection (FBP) algorithm. By the use of quantitative measures of image quality, we demonstrate that the iterative reconstruction algorithms can mitigate image artifacts and preserve spatial resolution more effectively than FBP algorithms. These features suggest that the use of advanced image reconstruction algorithms can improve the effectiveness of 3D OAT while reducing the amount of data required for biomedical applications.
迭代重建算法的光声断层扫描（OAT），也称为光声断层扫描，具有改善图像质量的能力，超过分析算法，由于它们能够结合准确的成像物理模型，仪器的响应和测量噪声。然而，到目前为止，很少有报道试图采用先进的迭代图像重建算法，以提高三维（3D）OAT 的图像质量。在这项工作中，我们实现和调查两种迭代图像重建方法用于 3D OAT 小动物成像仪：即惩罚最小二乘（PLS）方法采用二次平滑惩罚和 PLS 方法采用全变差范数惩罚。重建算法采用超声换能器脉冲响应的精确模型。实验数据集被用来比较迭代重建算法的性能，以一个三维滤波反投影（FBP）算法。通过使用图像质量的定量措施，我们证明了迭代重建算法可以减轻图像伪影，并更有效地保留空间分辨率比 FBP 算法。这些特征表明，先进的图像重建算法的使用可以提高三维 OAT 的有效性，同时减少生物医学应用所需的数据量。
