Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.
Accelerating ordered subsets image reconstruction for X-ray CT using spatially nonuniform optimization transfer.
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DOI：
10.1109/tmi.2013.2266898
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发表时间：
2013-11
影响因子：
10.6
通讯作者：
Fessler JA
中科院分区：
文献类型：
--
作者：
Kim D;Pal D;Thibault JB;Fessler JA
X射线CT中的统计图像重建算法为降低的剂量水平提供了改进的图像质量，但需要大量的计算时间。迭代算法，收敛在几个迭代和服从大规模并行化是有利的多处理器实现。可分离二次代理（SQS）算法是可取的，因为它是简单的，同时更新所有体素。然而，标准SQS算法需要多次迭代才能收敛。本文提出了一种扩展的SQS算法，导致空间非均匀更新。非均匀（NU）SQS鼓励用于体素的较大步长，所述体素预期在当前图像与最终图像之间改变更多，从而加速收敛，而NU-SQS的推导保证单调下降。有序子集（OS）算法也可以加速SQS，提供合适的“子集平衡”条件。由于轴向感兴趣区域（ROI）之外的不完整采样，这些条件可能在3D螺旋锥束CT中失败。本文提出了一种改进的OS算法，它在螺旋CT的ROI外更稳定。我们使用CT扫描证明，建议的NU-OS-SQS算法处理的螺旋几何形状比传统的OS方法和“收敛”在不到一半的普通OS-SQS的时间。
Statistical image reconstruction algorithms in X-ray CT provide improved image quality for reduced dose levels but require substantial computation time. Iterative algorithms that converge in few iterations and that are amenable to massive parallelization are favorable in multiprocessor implementations. The separable quadratic surrogate (SQS) algorithm is desirable as it is simple and updates all voxels simultaneously. However, the standard SQS algorithm requires many iterations to converge. This paper proposes an extension of the SQS algorithm that leads to spatially non-uniform updates. The non-uniform (NU) SQS encourages larger step sizes for the voxels that are expected to change more between the current and the final image, accelerating convergence, while the derivation of NU-SQS guarantees monotonic descent. Ordered subsets (OS) algorithms can also accelerate SQS, provided suitable “subset balance” conditions hold. These conditions can fail in 3D helical cone-beam CT due to incomplete sampling outside the axial region-of-interest (ROI). This paper proposes a modified OS algorithm that is more stable outside the ROI in helical CT. We use CT scans to demonstrate that the proposed NU-OS-SQS algorithm handles the helical geometry better than the conventional OS methods and “converges” in less than half the time of ordinary OS-SQS.