Optimization of PET Imaging

 描述（由申请人提供）：本项目的目标是通过开发新型图像重建方法和数据分析工具来提高PET成像的有效性。PET是一种分子成像模式，其能够通过用正电子发射体标记感兴趣的生物分子来直接对人类和动物中的生理和生化过程进行成像。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、神经科学以及使用动物模型研究各种人类疾病。使用[18F]氟脱氧葡萄糖（FDG）的PET/CT越来越多地用于患有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检测微转移和小肿瘤浸润淋巴结的灵敏度较低。因此，提高PET图像质量将对现代医学产生深远的影响。这个建议建立在我们以前的工作，患者自适应惩罚最大似然图像重建和动态PET成像。本提案的目的是评估我们使用具有组织学验证的基础事实的患者数据开发的有前途的图像重建方法，并通过任务特定优化和添加运动补偿能力来增强这些方法的鲁棒性和性能。四个具体目标是：（i）使用经组织学验证的基础真实值的乳腺癌患者评价患者自适应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。这项研究的成功将对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.