Unified Joint Statistical Reconstruction of PET & MR

摘要 同时PET/MR可以被认为是一种综合的成像方式，只有当两者的信息 医疗模式被整合在一起。在当前常规的PET/MR应用中，进行PET和MR扫描 分开，并且图像也分别重建。该信息仅在 应用程序级别。在这里，我们提出了联合PET/MR图像重建的统一方法，这是一个范例 转变整合PET和MR信息的新方法，以显著最大化PET/MR的结果 PET和MR扫描仪确实测量了不同的生理或生理信号，但仍然存在冗余 使用这两种模式获得的图像之间的信息(例如，肿瘤边界和互信息) 可用于在潜在的关节重建中建立PET和MR图像之间的连接。此外，如果 考虑到房室模型，由PET和MR估计的生理参数可以 重叠，因此从一种形态估计的参数图像(体素方向的运动参数)可以是 直接用于帮助估计其他通道的参数图像。因此，存在相互间的 这两种模式之间的联系，我们可以用来开发出优雅的关节重建方法。 我们将首先利用PET/MR的同时采集来开发静态图像重建 从MR图像导出的解剖先验，并开发使用 从MR图像计算的运动场。我们相信，在这两种情况下，PET图像的质量将显著提高 与传统方法相比有所改进。对于PET/MR，有许多新的方法来联合建模动态 宠物和MR图像。因此，我们将开发一种交替方向乘子法(ADMM)来直接估计 从原始数据中得到动态PET和动态MR的体素方向动力学参数。这将实现 动态PET和MR参数图像的最大信噪比我们还将研究新的 非平稳运动模型的参数成像方法，其中不仅估计图像而且 还有参数图像估计的不确定性。在以下情况下，了解不确定性是很重要的 关于疾病的进展/退化、信号检测等做出决定。我们将使用一种方法 由我们的实验室开发，其中原始PET数据中的噪声将使用 原点集成算法。

Abstract Simultaneous PET/MR can be considered as an integrated imaging modality only if the information of both modalities is integrated together. In current routine PET/MR applications, the PET and MR scans are performed separately, and the images are reconstructed separately as well. The information is integrated only at the application level. Here we propose unified methodologies of joint PET/MR image reconstruction, a paradigm shifting new way to integrate information of PET and MR to significantly maximize the outcome of PET/MR. The PET and MR scanners indeed measure different physical or physiological signals, but there are still redundant information (e.g. tumor boundary and mutual information) between the images obtained with the two modalities that can be utilized to build connection between PET and MR images in a potential joint reconstruction. In addition, if the compartmental model is taken into account, the physiological parameters estimated from PET and MR can have overlaps, and therefore the parametric image (voxel-wise kinetic parameters) estimated from one modality could be directly used to help the estimation of the parametric image of the other modality. Therefore, there are inter- connections between these two modalities that we can use to develop elegant methods of joint reconstruction. We will first take advantage of the simultaneous acquisition of PET/MR to develop a static image reconstruction with anatomic prior derived from MR images, and to develop methods to jointly reconstruct gated PET images using a motion field computed from MR images. We believe in both cases, the quality of PET images will be significantly improved compared to traditional approaches. For PET/MR, there are many novel ways to jointly model the dynamic PET and MR images. We will thus develop an alternating direction method of multipliers (ADMM) to directly estimate the voxel-wise kinetic parameters of dynamic PET and dynamic MR together from raw data. This will achieve the maximum signal noise ratio of parametric images for both dynamic PET and MR. We will also investigate novel approaches to parametric imaging of non-stationary kinetic modeling in which not only the images are estimated but also the uncertainty on those estimates of the parametric images. The knowledge of uncertainty is important when making decisions about progression/regression of the disease, signal detection, etc. We will use a method developed in our laboratory in which the noise in raw PET data will be "transferred" to parameter images using origin ensemble algorithm.