2.5-D simultaneous multislice reconstruction by series expansion methods from Fourier-rebinned PET data.
2.5-D simultaneous multislice reconstruction by series expansion methods from Fourier-rebinned PET data.
复制标题
通过傅立叶重组 PET 数据的级数展开方法进行 2.5 维同时多层重建。
DOI：
10.1109/42.870257
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Herman,GT
中科院分区：
文献类型：
--
作者：
Obi,T;Matej,S;Lewitt,RM;Herman,GT
正电子发射断层扫描（PET）中的全三维（3-D）数据的真实三维（3-D）体积重建由于其巨大的计算负担而仅具有有限的临床用途。将全3D数据傅立叶重组（Fourier rebinning）为一组2D正弦图数据将3D重建过程分解为解耦的2D图像切片的多个2D重建，从而即使在通过迭代重建算法执行2D重建的情况下也基本上减少了计算负担。另一方面，与图像切片的解耦相结合的重组中涉及的近似导致图像质量的一定降低，特别是当数据的信噪比低时。作者提出了一种基于级数展开原理的2.5维同时多层重建方法，其中体积由3维球对称钟形基函数的叠加表示。它利用了由于使用的三维（2-D）数据，而不是原来的完全3-D数据的时间减少，但在同一时间使用的3-D迭代重建方法与3-D基函数。相同的一般方法可以应用于属于使用跨越多个切片的3-D基函数的系列扩展方法（迭代或非迭代）的类的任何重建算法，并且可以用于任何多层正弦图或列表模式数据，无论是通过特殊的重新分组方案获得的还是通过PET扫描仪在2-D模式中使用隔片直接采集的。作者的研究证实，与标准2-D重建方法相比，所提出的2.5-D方法在重建质量方面提供了相当大的改善，而重建时间与2-D方法的数量级相同，并且即使在通用计算机上也具有临床实用性。
True three dimensional (3-D) volume reconstruction from fully 3-D data in positron emission tomography (PET) has only a limited clinical use because of its large computational burden. Fourier rebinning (FORE) of the fully 3-D data into a set of 2-D sinogram data decomposes the 3-D reconstruction process into multiple 2-D reconstructions of decoupled 2-D image slices, thus substantially decreasing the computational burden even in the case when the 2-D reconstructions are performed by an iterative reconstruction algorithm. On the other hand, the approximations involved in the rebinning combined with the decoupling of the image slices cause a certain reduction of image quality, especially when the signal-to-noise ratio of the data is low. The authors propose a 2.5-D Simultaneous Multislice Reconstruction approach, based on the series expansion principle, where the volume is represented by the superposition of 3-D spherically symmetric bell-shaped basis functions. It takes advantage of the time reduction due to the use of the FORE (2-D) data, instead of the original fully 3-D data, but at the same time uses a 3-D iterative reconstruction approach with 3-D basis functions. The same general approach can be applied to any reconstruction algorithm belonging to the class of series expansion methods (iterative or noniterative) using 3-D basis functions that span multiple slices, and can be used for any multislice sinogram or list mode data whether obtained by a special rebinning scheme or acquired directly by a PET scanner in the 2-D mode using septa. The authors' studies confirm that the proposed 2.5-D approach provides a considerable improvement in reconstruction quality, as compared to the standard 2-D reconstruction approach, while the reconstruction time is of the same order as that of the 2-D approach and is clinically practical even on a general-purpose computer.