An exact Fourier rebinning algorithm for 3D PET imaging using panel detectors.
An exact Fourier rebinning algorithm for 3D PET imaging using panel detectors.
复制标题
使用面板探测器进行 3D PET 成像的精确傅立叶重组算法。
DOI：
10.1088/0031-9155/49/11/020
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发表时间：
2004
影响因子：
3.5
通讯作者：
Chen,Chin-Tu
中科院分区：
文献类型：
--
作者：
Kao,Chien-Min;Pan,Xiaochuan;Chen,Chin-Tu
我们提出了一种精确的基于傅立叶的算法，用于重建由固定双面板PET系统生成的三维数据，以获得直接切片，以便随后逐片重建。该算法计算效率高，可以大大降低重构任务的问题维数和计算复杂度。通过不考虑散射、随机、探测器响应函数和衰减校正影响的计算机模拟研究，我们证明了所提出算法生成的直接切片在定量上是准确的，并且表现出比原始直接切片更好的噪声特性。然而，图像伪影，发生在轴向强度不连续和靠近轴的区域，可以观察到。有了初步的支持证据，我们规定这些伪影是由于三维数据偏导数的离散实现中的错误。虽然一般加权可以用于所提出的重建算法，但在本工作中，我们只研究了均匀加权的使用，所得到的直接切片表现出不均匀的噪声分布，中心切片的噪声较小。在未来的研究中，通过使用一般权重来减少在大斜角处获得的数据的贡献，我们期望可以实现更均匀的噪声分布，并且可以以整体图像方差为代价减少视差误差引起的轴向模糊。
We present an exact Fourier-based algorithm for rebinning 3D data generated by a stationary dual-panel PET system to obtain direct slices for subsequent slice-by-slice reconstruction. The algorithm is computationally efficient and can greatly reduce the problem dimensionality and computation complexity of the reconstruction task. By conducting computer simulation studies in which the effects of scatter, randoms, detector response functions and attenuation correction are not considered, we demonstrate that direct slices generated by the proposed algorithm are quantitatively accurate and exhibit substantially better noise characteristics than the original direct slices. However, image artefacts, occurring at axial intensity discontinuities and in regions close to the axial axis, can be observed. With preliminary supporting evidence, we stipulate that these artefacts are due to errors in discrete implementations of partial derivatives of the 3D data. Although general weightings can be used in the proposed rebinning algorithm, in this work we only study the use of uniform weightings and the resulting direct slices exhibit non-uniform noise distributions, with the central slices being less noisy. In future studies, by using general weightings to reduce contributions of the data that are acquired at large oblique angles, we expect that more uniform noise distributions can be achieved, and axial blurring due to parallax errors can be reduced, at the cost of overall image variance.