公开(公告)号：US20100074500A1

公开(公告)日：2010-03-25

申请号：US12564358

申请日：2009-09-22

申请人： Michel Defrise ,  Vladimir Y. Panin

发明人： Michel Defrise ,  Vladimir Y. Panin

IPC分类号： G06K9/00

CPC分类号： G06T11/006 ,  G06T2211/424

摘要： Methods and systems for reconstructing a nuclear medical image from time-of-flight (TOF) positron emission tomography (PET) imaging data are disclosed. Measured three-dimensional (3D) TOF-PET data, including direct two-dimensional (2D) projections and oblique 3D projection data, are acquired from a PET scanner. A model 3D image is preset, a modeled 2D TOF sinogram is generated from the model 3D image, and a modeled 3D TOF sinogram is generated from the 2D TOF sinogram based on an exact inverse rebinning relation in Fourier space. The model 3D image is corrected based on the 3D TOF sinogram and is provided as the reconstructed nuclear medical image. Techniques disclosed herein are useful for facilitating efficient medical imaging, e.g., for diagnosis of various bodily conditions.

摘要翻译： 公开了从飞行时间（TOF）正电子发射断层摄影（PET）成像数据重建核医学图像的方法和系统。 从PET扫描仪获取测量的三维（3D）TOF-PET数据，包括直接二维（2D）投影和倾斜3D投影数据。 模型3D图像被预置，从模型3D图像生成建模的2D TOF正弦图，并且基于傅里叶空间中的精确逆重组关系从2D TOF正弦图生成建模的3D TOF正弦图。 基于3D TOF正弦图校正模型3D图像，并且被提供为重建的核医学图像。 本文公开的技术可用于促进有效的医学成像，例如用于诊断各种身体状况。

公开(公告)号：US20100057819A1

公开(公告)日：2010-03-04

申请号：US12463946

申请日：2009-05-11

申请人： Vladimir Y. Panin

发明人： Vladimir Y. Panin

IPC分类号： G06F7/00

CPC分类号： G06F7/588 ,  G01T1/00

摘要： The use of the ordinary Poisson iterative reconstruction algorithm in PET requires the estimation of expected random coincidences. In a clinical environment, random coincidences are often acquired with a delayed coincidence technique, and expected randoms are estimated through variance reduction (VR) of measured delayed coincidences. In this paper we present iterative VR algorithms for random compressed sinograms, when previously known methods are not applicable. Iterative methods have the advantage of easy adaptation to any acquisition geometry and of allowing the estimation of singles rates at the crystal level when the number of crystals is relatively small. Two types of sinogram compression are considered: axial (span) rebinning and transaxial mashing. A monotonic sequential coordinate descent algorithm, which optimizes the Least Squares objective function, is investigated. A simultaneous update algorithm, which possesses the advantage of easy parallelization, is also derived for both cases of the Least Squares and Poisson Likelihood objective function.

摘要翻译： 在PET中使用普通泊松迭代重建算法需要估计预期的随机重合。 在临床环境中，通常采用延迟符合技术来获得随机符合度，并且通过测量的延迟符合度的方差减小（VR）估计预期随机数。 在本文中，我们提出了用于随机压缩正弦图的迭代VR算法，如果以前已知的方法不适用。 迭代方法具有容易适应于任何采集几何的优点，并且当晶体数目相对较小时允许在晶体级别估计单数率。 考虑两种类型的正弦图压缩：轴向（跨度）重新定位和轴向糖化。 研究了优化最小二乘方目标函数的单调连续坐标下降算法。 对于最小二乘法和泊松似然目标函数的两种情况，也推导出具有易并行化优势的同步更新算法。

公开(公告)号：US06539103B1

公开(公告)日：2003-03-25

申请号：US09189424

申请日：1998-11-10

申请人： Vladimir Y. Panin ,  Gengsheng Lawrence Zeng ,  Grant T. Gullberg

发明人： Vladimir Y. Panin ,  Gengsheng Lawrence Zeng ,  Grant T. Gullberg

IPC分类号： G06K900

CPC分类号： G06T11/005 ,  G06T2211/432 ,  Y10S128/922

摘要： A method of constructing a non-uniform attenuation map (460) of a subject for use in image reconstruction of SPECT data is provided. It includes collecting a population of a priori transmission images and storing them in an a priori image memory (400). The transmission images not of the subject. Next, a cross-correlation matrix (410) is generated from the population of transmission images. The eigenvectors (420) of the cross-correlation matrix (410) are calculated. A set of orthonormal basis vectors (430) is generated from the eigenvectors (420). A linear combination of the basis vectors (420) is constructed (440), and coefficients for the basis vectors are determined (450) such that the linear combination thereof defines the non-uniform attenuation map (460).

摘要翻译： 提供了构建用于SPECT数据的图像重建中的被摄体的不均匀衰减图（460）的方法。 它包括收集先验传输图像的群体并将其存储在先验图像存储器（400）中。 不是主题的传输图像。 接下来，从发送图像的群体生成互相关矩阵（410）。 计算互相关矩阵（410）的特征向量（420）。 从特征向量（420）生成一组正交基矢量（430）。 构建基矢量（420）的线性组合（440），并确定用于基矢量的系数（450），使得其线性组合限定非均匀衰减图（460）。