Unified Joint Statistical Reconstruction of PET & MR

PET统一联合统计重建

• 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
• 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

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.

摘要 同步PET/MR可以被认为是一个集成的成像模式，只有当两者的信息， 模式是结合在一起的。在当前常规PET/MR应用中，执行PET和MR扫描 并且图像也被单独地重建。信息仅在 应用层。在这里，我们提出了联合PET/MR图像重建的统一方法， 转移新的方法来整合PET和MR的信息，以显着最大限度地提高PET/MR的结果。 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。 使用两种模态获得的图像之间的信息（例如，肿瘤边界和互信息）， 可以用于在潜在的关节重建中建立PET和MR图像之间的连接。另外如果 考虑房室模型，从PET和MR估计的生理参数可以具有 重叠，并且因此从一种模态估计的参数图像（逐体素的动力学参数）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 根据MR图像计算的运动场。我们相信，在这两种情况下，PET图像的质量将显着 与传统方法相比有所改善。对于PET/MR，有许多新颖的方法来联合建模动态成像。 PET和MR图像。因此，我们将开发一个交替方向的乘数法（ADMM）直接估计 动态PET和动态MR的逐体素动力学参数一起来自原始数据。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 非平稳动力学建模的参数成像方法，其中不仅估计图像， 还有参数图像的那些估计的不确定性。不确定性的知识很重要， 做出关于疾病的进展/消退、信号检测等的决定。我们将使用一种方法 在我们的实验室中开发的，其中原始PET数据中的噪声将被"转移"到参数图像， 原点集成算法