Optimization of PET Imaging

 描述(申请人提供)：该项目的目标是通过开发新的图像重建方法和数据分析工具来提高PET成像的效率。PET是一种分子成像手段，通过用正电子发射体标记感兴趣的生物分子，能够直接对人和动物的生理生化过程进行成像。它在临床诊断和生物学研究中有着广泛的应用，包括肿瘤学、心脏病学、神经科学以及利用动物模型研究各种人类疾病。带有[18F]氟脱氧葡萄糖(FDG)的PET/CT越来越多地用于不同类型肿瘤的癌症患者的分期、再分期和治疗监测。然而，目前的FDG-PET在检测微转移和小肿瘤浸润性淋巴结方面的敏感性较低。因此，提高PET图像的质量将对现代医学产生深远的影响。这一建议建立在我们之前关于患者自适应惩罚最大似然图像重建和动态PET成像的工作的基础上。这项建议的目的是评估我们开发的有前景的图像重建方法，使用具有组织学验证的地面真实数据的患者数据，并通过特定任务的优化和添加运动补偿功能来增强这些方法的健壮性和性能。这四个具体目标是：(I)使用经组织学证实的基本事实的乳腺癌患者评估患者自适应PML重建，(Ii)开发患者自适应动态PET图像重建，(Iii)开发运动补偿动态PET图像重建方法，以及(Iv)使用经活检证实的肺癌患者数据验证运动补偿重建。本研究的成功将对PET成像的临床应用产生重大而积极的影响。

 DESCRIPTION (provided by applicant): The goal of this project is to improve the efficacy of PET imaging through the development of novel image reconstruction methods and data analysis tools. PET is a molecular imaging modality that is capable of imaging physiological and biochemical processes directly in humans and animals by labeling biomolecules of interests with positron emitters. It has wide applications in clinical diagnosis and biological research, includin oncology, cardiology, neuroscience, and studies of various human diseases using animal models. PET/CT with [18F]fluorodeoxyglucose (FDG) is increasingly being used for staging, restaging and treatment monitoring for cancer patients with different types of tumors. However, current FDG-PET provides a low sensitivity to detect micrometastases and small tumor infiltrated lymph nodes. Therefore, improving the quality of PET images will have a profound impact on modern medicine. This proposal builds upon our previous work on patient-adaptive penalized maximum likelihood image reconstruction and dynamic PET imaging. The objective of this proposal is to evaluate the promising image reconstruction methods that we have developed using patient data with histology-verified ground truth and to enhance the robustness and performance of these methods through task- specific optimization and adding motion compensation ability. The four specific aims are (i) Evaluation of patient-adaptive PML reconstruction using breast cancer patients with histologically verified ground truth, (ii) Development of patient-adaptive dynamic PET image reconstruction, (iii) Development of a motion-compensated dynamic PET image reconstruction method, and (iv) Validation of the motion-compensated reconstruction using biopsy-proven lung cancer patient data. The success of this research will have a significant and positive impact on the clinical application of PET imaging.