Lebedev Sergej, Sawall Stefan, Knaup Michael, Kachelrieß Marc
German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Z Med Phys. 2017 Sep;27(3):180-192. doi: 10.1016/j.zemedi.2017.04.004. Epub 2017 May 16.
Optimization of the AIR-algorithm for improved convergence and performance.
The AIR method is an iterative algorithm for CT image reconstruction. As a result of its linearity with respect to the basis images, the AIR algorithm possesses well defined, regular image quality metrics, e.g. point spread function (PSF) or modulation transfer function (MTF), unlike other iterative reconstruction algorithms. The AIR algorithm computes weighting images α to blend between a set of basis images that preferably have mutually exclusive properties, e.g. high spatial resolution or low noise. The optimized algorithm uses an approach that alternates between the optimization of rawdata fidelity using an OSSART like update and regularization using gradient descent, as opposed to the initially proposed AIR using a straightforward gradient descent implementation. A regularization strength for a given task is chosen by formulating a requirement for the noise reduction and checking whether it is fulfilled for different regularization strengths, while monitoring the spatial resolution using the voxel-wise defined modulation transfer function for the AIR image.
The optimized algorithm computes similar images in a shorter time compared to the initial gradient descent implementation of AIR. The result can be influenced by multiple parameters that can be narrowed down to a relatively simple framework to compute high quality images. The AIR images, for instance, can have at least a 50% lower noise level compared to the sharpest basis image, while the spatial resolution is mostly maintained.
The optimization improves performance by a factor of 6, while maintaining image quality. Furthermore, it was demonstrated that the spatial resolution for AIR can be determined using regular image quality metrics, given smooth weighting images. This is not possible for other iterative reconstructions as a result of their non linearity. A simple set of parameters for the algorithm is discussed that provides the mentioned results.
优化AIR算法以提高收敛性和性能。
AIR方法是一种用于CT图像重建的迭代算法。由于其相对于基图像的线性特性，与其他迭代重建算法不同，AIR算法具有定义明确、规则的图像质量指标，例如点扩散函数（PSF）或调制传递函数（MTF）。AIR算法计算加权图像α，以在一组优选具有互斥特性（例如高空间分辨率或低噪声）的基图像之间进行融合。优化后的算法使用一种方法，该方法在使用类似OSSART更新的原始数据保真度优化和使用梯度下降的正则化之间交替进行，这与最初提出的使用直接梯度下降实现的AIR不同。通过制定降噪要求并检查不同正则化强度下是否满足该要求，同时使用针对AIR图像的体素定义调制传递函数监测空间分辨率，来选择给定任务的正则化强度。
与AIR的初始梯度下降实现相比，优化后的算法在更短的时间内计算出相似的图像。结果可能受到多个参数的影响，这些参数可以缩小到一个相对简单的框架来计算高质量图像。例如，与最清晰的基图像相比，AIR图像的噪声水平可降低至少50%，同时空间分辨率大多得以保持。
优化将性能提高了6倍，同时保持了图像质量。此外，结果表明，在加权图像平滑的情况下，可使用常规图像质量指标确定AIR的空间分辨率。由于其他迭代重建的非线性，这对它们来说是不可能的。讨论了一组能提供上述结果的简单算法参数。
