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Slab-by-slab blurring model for geometric point response correction and attenuation correction using iterative reconstruction algorithms

机译:使用迭代重建算法的逐块模糊模型,用于几何点响应校正和衰减校正

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The distance-dependent geometric point response of a single photon emission computed tomographic (SPECT) system and the attenuation effect of photons passing through the object are modeled in an iterative OS-EM reconstruction algorithm to improve both the resolution and quantitative accuracy of the reconstructed images. In this paper, an efficient incremental slab-by-slab blurring model was introduced to speed up the reconstruction. A specified number of neighboring vertical slices are grouped into a slab. Slab-by-slab blurring, rather than slice-by-slice blurring, is performed to reduce the convolution times for both projector and backprojector which model the geometric point response of the system. The key advantage of this slab-by-slab blurring model over the slice-by-slice model is that the computational time can be significantly reduced while still maintaining the spatial resolution and quantitative accuracy of the reconstructed images. The application of this incremental slab-by-slab blurring model, incorporated with a model for attenuation, to the image reconstruction of patient SPECT data shows improved resolution and contrast over the images reconstructed without the corrections; it also shows much less computational time for the reconstruction than the slice-by-slice implementation.
机译:在迭代OS-EM重建算法中对单个光子发射计算机断层摄影(SPECT)系统与距离有关的几何点响应以及通过对象的光子的衰减效应进行建模,以提高重建图像的分辨率和定量精度。本文介绍了一种有效的逐块增量模糊模型,以加快重建速度。将指定数量的相邻垂直切片分组为一个平板。进行逐块模糊而不是逐块模糊,以减少对系统的几何点响应建模的投影仪和背投影仪的卷积时间。这种逐个片段的模糊模型比逐个片段的模型的主要优势在于,可以显着减少计算时间,同时仍保持重构图像的空间分辨率和定量精度。将这种逐块递增的模糊模型与衰减模型相结合,将其应用于患者SPECT数据的图像重建,与未经校正的重建图像相比,分辨率和对比度得到了改善。与逐个切片的实现相比,它还显示出更少的重建时间。

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