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Statistical modeling challenges in model-based reconstruction for X-ray CT

机译:基于模型的X射线CT重建中的统计建模挑战

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Model-based iterative reconstruction (MBIR) is increasingly widely applied as an improvement over conventional, deterministic methods of image reconstruction in X-ray CT. A primary advantage of MBIR is potentially drastically reduced dosage without diagnostic quality loss. Early success of the method has naturally led to growing numbers of scans at very low dose, presenting data which does not match well the simple statistical models heretofore considered adequate. This paper addresses several issues arising in limiting cases which call for refinement of standard data models. The emergence of electronic noise as a significant contributor to uncertainty, and bias of sinogram values in photon-starved measurements are demonstrated to be important modeling problems in this new environment. We present also possible ameliorations to several of these low-dosage estimation issues.
机译:基于模型的迭代重建(MBIR)作为对X射线CT中传统的确定性图像重建方法的改进而得到越来越广泛的应用。 MBIR的主要优点是可以大幅度减少剂量,而不会降低诊断质量。该方法的早期成功自然导致以非常低的剂量进行扫描的次数越来越多,所提供的数据与迄今认为足够的简单统计模型无法很好地匹配。本文讨论了在限制情况下出现的一些问题,这些问题要求完善标准数据模型。电子噪声的出现是导致不确定性的重要因素,而光子饥饿测量中正弦图值的偏差被证明是在这种新环境中的重要建模问题。我们还提出了对这些低剂量估算问题中的几种的可能的改进措施。

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