Unified Joint Statistical Reconstruction of PET & MR

PET统一联合统计重建

基本信息

批准号：
10263164
负责人：
Quanzheng Li
金额：
$ 24.82万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2022-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10263164
关键词：
Address Algorithmic Software Algorithms Anatomy Bayesian Method Computer software Data Decision Making Detection Disease Progression Disease regression Future Image Imaging problem Imaging technology Joint repair Joints Kinetics Knowledge Laboratories Magnetic Resonance Imaging Measurement Measures Methodology Methods Modality Modeling Morphology Motion Noise Outcome Pathology Patients Performance Physicians Physiological Positron-Emission Tomography Probability Process Resolution Scanning Signal Transduction System Technology Testing Tracer Translations Uncertainty attenuation base clinical application computerized data processing data acquisition design experimental study image reconstruction imaging modality improved kinetic model molecular imaging novel novel strategies nuclear imaging parametric imaging physical model physiologic model quantitative imaging reconstruction tool tumor uptake

项目摘要

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.

抽象的仅当同时 PET/MR 的信息都包含在内时，才可以将同步 PET/MR 视为一种集成成像方式。方式被整合在一起。在当前的常规 PET/MR 应用中，执行 PET 和 MR 扫描单独地，并且图像也单独地重建。该信息仅在应用级别。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 信息的新方式，以显着最大化 PET/MR 的结果。这 PET和MR扫描仪确实测量不同的物理或生理信号，但仍然存在冗余通过两种方式获得的图像之间的信息（例如肿瘤边界和相互信息）可用于在潜在的联合重建中建立 PET 和 MR 图像之间的连接。此外，如果考虑到房室模型，从 PET 和 MR 估计的生理参数可以有重叠，因此从一种模态估计的参数图像（体素动力学参数）可以是直接用于帮助估计其他模态的参数图像。因此，有间这两种模式之间的联系我们可以用来开发优雅的联合重建方法。我们将首先利用 PET/MR 的同步采集来开发静态图像重建从 MR 图像导出解剖先验，并开发使用联合重建门控 PET 图像的方法根据 MR 图像计算的运动场。我们相信在这两种情况下，PET 图像的质量都会显着提高与传统方法相比有所改进。对于 PET/MR，有许多新颖的方法来联合建模动态 PET 和 MR 图像。因此，我们将开发一种交替方向乘数法（ADMM）来直接估计来自原始数据的动态 PET 和动态 MR 的体素动力学参数。这将实现动态 PET 和 MR 参数图像的最大信噪比。我们也会调查小说非平稳动力学建模的参数成像方法，其中不仅估计图像，而且还有参数图像估计的不确定性。当以下情况时，对不确定性的了解很重要：做出有关疾病进展/消退、信号检测等的决定。我们将使用一种方法我们实验室开发的，其中原始 PET 数据中的噪声将使用“转移”到参数图像原始集成算法。