NUFFT for Multi-Modality In Vivo Imaging

描述（由申请人提供）： 当前的MRI、CT和PET图像重建算法受到傅立叶空间中的非均匀数据的大的插值误差的限制。这正在成为大规模3D成像中的计算瓶颈，其中数据的大小随着体积的大小和对高分辨率的需求的增加而快速增长。本研究将发展多维非均匀快速傅立叶变换（NUFFT）演算法。由于插值的高精度和高效率，这些算法将显著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）并进行评价。还将为MRI/PET组合和CT/PET组合开发一个联合反演框架，以改善通过单个模态获得的信息。 在本研究的R21阶段I中，我们将（a）开发新的2D非均匀快速傅立叶变换（NUFFT）算法;（B）使用来自杜克体内显微镜中心的合成和真实的MRI和CT数据评估NUFFT算法。 在本研究的R33阶段II中，我们将（a）开发用于MRT、CT和PET成像模态的3D NUFFT算法，（B）开发基于NUFFT的联合反演框架，用于MRI/PET和CT/PET组合中的多模态成像，以及（c）使用合成和真实的MRI/PET和CT/PET数据评估3D NUFFT和联合反演框架。 这样的框架可以被认为是在MRI、PET、CT和潜在的许多其他模态中用于图像重建的计算范例的转变。对于快速增长的三维成像应用，新的NUFFT算法将提高分辨率和计算速度比传统的方法由数量级。联合反演方法将改善由各个模态获得的信息。

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.