论文标题
质子CT图像重建的迭代最小二乘法
An Iterative Least Squares Method for Proton CT Image Reconstruction
论文作者
论文摘要
临床上有用的质子计算机断层扫描图像将依靠算法来找到最佳拟合测得的质子数据的三维质子停止功率分布。我们提出了具有许多功能的最小二乘迭代方法，可以将质子成像放入更定量的框架中。 These include the definition of a unique solution that optimally fits the protons, the definition of an iteration vector that takes into account proton measurement uncertainties, the definition of an optimal step size for each iteration individually, the ability to simultaneously optimize the step sizes of many iterations, the ability to divide the proton data into arbitrary numbers of blocks for parallel processing and use of graphical processing units, and the definition of stopping criteria to determine when to stop迭代。我们发现，对于要成像的任何对象，有可能保证图像可以量化接近最佳解决方案，并且阶跃尺寸的优化减少了收敛所需的迭代总数。我们证明了这些算法在实际数据上的使用。
Clinically useful proton Computed Tomography images will rely on algorithms to find the three-dimensional proton stopping power distribution that optimally fits the measured proton data. We present a least squares iterative method with many features to put proton imaging into a more quantitative framework. These include the definition of a unique solution that optimally fits the protons, the definition of an iteration vector that takes into account proton measurement uncertainties, the definition of an optimal step size for each iteration individually, the ability to simultaneously optimize the step sizes of many iterations, the ability to divide the proton data into arbitrary numbers of blocks for parallel processing and use of graphical processing units, and the definition of stopping criteria to determine when to stop iterating. We find that it is possible, for any object being imaged, to provide assurance that the image is quantifiably close to an optimal solution, and the optimization of step sizes reduces the total number of iterations required for convergence. We demonstrate the use of these algorithms on real data.
