NUFFT for Multi-Modality In Vivo Imaging

描述(由申请人提供)： 目前的MRI、CT和PET图像重建算法都受到傅立叶空间非均匀数据的较大内插误差的限制。这正成为大规模3D成像的计算瓶颈，数据量随着数据量的增加和对高分辨率的要求而迅速增长。建议的研究将开发多维非均匀快速傅立叶变换(NUFFT)算法。这种算法由于具有高精度和高效率的内插，将大大提高图像重建的分辨率和速度。NUFFT将应用于磁共振成像(MRI)、正电子发射断层扫描(PET)和X射线计算机断层扫描(CT)，并进行评估。还将为磁共振成像/正电子发射计算机断层成像组合和CT/正电子发射计算机断层成像组合开发一个联合反演框架，以改进通过个别模式获得的信息。 在这项研究的R21第一阶段，我们将(A)开发新的2D非均匀快速傅立叶变换(NUFFT)算法；(B)使用来自Duke Center for In Vivo显微镜的合成和真实的MRI和CT数据来评估NUFFT算法。 在这项研究的R33第二阶段，我们将(A)开发用于MRT、CT和PET成像模式的3D NUFFT算法，(B)开发基于NUFFT的用于MRI/PET和CT/PET组合的多模式成像的联合反演框架，以及(C)使用合成和真实的MRI/PET和CT/PET数据来评估3D NUFFT和联合反演框架。 这样的框架可以被认为是MRI、PET、CT以及潜在的许多其他模式中图像重建计算范式的转变。对于快速发展的3D成像应用，新的NUFFT算法将比传统方法提高分辨率和计算速度几个数量级。联合反演法将改善单个模式所获得的信息。

DESCRIPTION (provided by applicant): Current MRI, CT, and PET image reconstruction algorithms are limited by the large interpolation errors of the nonuniform data in the Fourier space. This is becoming a computational bottleneck in large-scale 3D imaging where the size of data grows rapidly with the size of volume and the increasing demand on high resolution. The proposed research would develop multidimensional nonuniform fast Fourier transform (NUFFT) algorithms. Such algorithms will significantly improve both the resolution and the speed of image reconstruction because of the highly accurate and efficient interpolation. The NUFFT will be applied to and evaluated by magnetic resonance imaging (MRI), positron emission tomography (PET), and X-ray computerized tomography (CT). A joint inversion framework will also be developed for the MRI/PET combination and for the CT/PET combination to improve the information obtained by individual modalities. In the R21 Phase I of this research, we will (a) develop new 2D nonuniform fast Fourier transform (NUFFT) algorithms; (b) evaluate the NUFFT algorithms with synthetic and real MRI and CT data from the Duke Center for In Vivo Microscopy. In the R33 Phase II of this research, we will (a) develop 3D NUFFT algorithms for MRT, CT and PET imaging modalities, (b) develop a joint inversion framework based on NUFFT for multi-modality imaging in the MRI/PET and CT/PET combinations, and (c) evaluate the 3D NUFFT and joint inversion framework with synthetic and real MRI/PET and CT/PET data. Such a framework can be considered as a shift in computational paradigm for image reconstruction in MRI, PET, CT, and potentially many other modalities. For rapidly growing 3D imaging applications, the new NUFFT algorithms would improve the resolution and computation speed over the conventional methods by orders of magnitude. The joint inversion method will improve the information obtained by the individual modalities.