Data-driven Head Motion Correction in PET Imaging Using Deep Learning

使用深度学习在 PET 成像中进行数据驱动的头部运动校正

• 批准号: 10376855
• 负责人: John Aaron Onofrey
• 金额: $ 20.94万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10376855
• 关键词: Address; Algorithms; Alzheimer' s Disease; Anatomy; Anxiety Disorders; Behavior; Binding; Biomedical Research; Brain; Brain imaging; Brain scan; Cigarette; Clinical; Clinical Research; Computer software; Custom; Data; Data Analyses; Development; Devices; Effect Modifiers (Epidemiology); Evaluation; Event; Funding; Gold; Head; Head Movements; Hour; Human; Human body; Image; Individual; Learning; Measurement; Measures; Methodology; Methods; Modeling; Morphologic artifacts; Motion; Movement; Neurodegenerative Disorders; Parkinson Disease; Pathologic Processes; Patients; Performance; Phase; Physiological Processes; Positron-Emission Tomography; Research; Research Personnel; Scanning; Smoking; Synapses; System; Testing; Time; Tracer; Training; Variant; Work; base; clinical practice; clinical translation; deep learning; deep neural network; density; design; effectiveness evaluation; fluorodeoxyglucose; image reconstruction; imaging modality; improved; innovation; interest; neural network; novel; psychologic; reconstruction; research facility; simulation; statistics; tool

Project Summary Positron-emission tomography (PET) is an imaging modality that allows clinicians and researchers to study the physiological or pathological processes of the human body, and in particular the brain via the use of specific tracers. For brain PET imaging, patient head movement during scanning presents a challenge for accurate PET image reconstruction and subsequent quantitative analysis. Problems due to head motion are exacerbated by the long duration of the scans, with scan times commonly over one hour. Furthermore, some PET studies specifically involve subjects that either have trouble staying still due to psychological variations, e.g. patients with neurodegenerative disorders such as Alzheimer's disease and Parkinson's disease, or psychological variations, e.g. subjects with anxiety disorders, or are required to participate in tasks that involve movement, e.g. smoking cigarettes while scanning. In brain scans, the average head motion can vary from 7 mm in clinical scans to triple this amount for longer research scans. Quantitatively, a 5 mm head motion can produce biases of up to ~35% in regional intensities and ∼15% in volume of distribution estimates, which could much larger than the difference observed in regional intensities or binding potential that distinguish different demographic groups being studied. The ability to track and correct head motion, therefore, would be of high utility in both clinical and research PET studies. In the past, many motion correction methods have been proposed. However, except for hardware-based approaches, there has been no method that can track frequent head motion on-the-fly during the PET acquisition. Hardware-based approaches are not readily available for clinical translation or used by other research facilities due to highly-customized software/hardware setup. To address this challenge, we propose to develop a data-driven methodology using deep learning to track and estimate rigid head motion using PET raw data, and incorporate both tracer type and time as conditional variables into this deep neural network design in order to handle diverse PET tracer types and their dynamic behavior. Overall, these solutions will provide for a data-driven motion estimation methodology to improve the quality of PET imaging. Specifically, we will start with the development and testing of our methodology for rigid head motion estimation using single-tracer PET raw data. Then we will perform evaluation of our multi-tracer motion estimation methodology applied to real PET data with a diverse range of tracers. Finally, in the exploratory phase, we will integrate time-of-flight information into deep learning-based motion prediction. The significance of this proposal is that it will allow for improved quality of PET imaging in real time and potentially allow for its use in clinical PET systems that do not have special motion tracking hardware. This work will serve as a first step towards developing data-driven motion estimation algorithms for full body PET imaging. The innovation lies in the development of what is a data-driven solution to the problem of real time motion estimation.

项目摘要 正电子发射断层扫描（PET）是一种成像方式，使临床医生和研究人员能够研究 人体的物理或病理过程，尤其是通过使用特定的大脑 示踪剂。对于脑宠物成像，扫描过程中的患者头部移动提出了一个挑战，以便准确 PET图像重建和随后的定量分析。由于头部运动引起的问题是 由于扫描的长时间持续时间，扫描时间通常超过一个小时，这加剧了。此外，有些 宠物研究专门涉及受试者，这些受试者要么由于心理差异而静止不动， 例如神经退行性疾病的患者，例如阿尔茨海默氏病和帕金森氏病，或 心理变化，例如患有焦虑症的受试者，或要求参与涉及的任务 运动，例如扫描时吸烟。在脑部扫描中，平均头部运动可以从7个变化 临床扫描中的MM以进行更长的研究扫描，以进行三倍。定量，5毫米头运动可以 在区域强度中产生高达约35％的偏见，分配估计量〜15％，这可能 比区域强度或结合潜力所观察到的差异大得多 人口组正在研究。因此，跟踪和纠正头部运动的能力将很高 临床和研究宠物研究的实用程序。过去，许多运动校正方法已经 建议的。但是，除了基于硬件的方法外，没有方法可以跟踪频率 宠物收购期间的脸部运动。基于硬件的方法不容易获得 临床翻译或由于高度定制的软件/硬件设置而被其他研究设施使用。到 应对这一挑战，我们建议使用深度学习以跟踪和 使用PET原始数据估算刚性头运动，并将示踪剂类型和时间纳入条件 变量到这种深度神经网络设计中，以处理潜水员宠物示踪剂类型及其动态 行为。总体而言，这些解决方案将为数据驱动的运动估计方法提供改善 宠物成像的质量。具体来说，我们将从对刚性的方法的开发和测试开始 使用单个跟踪器PET原始数据进行头部运动估算。然后，我们将对我们的多追踪器进行评估 运动估计方法将用于潜水员示踪剂范围的真实PET数据应用。最后，在 探索性阶段，我们将将飞行时间信息整合到基于深度学习的运动预测中。这 该提案的意义是，它将允许实时提高宠物成像的质量 允许其在没有特殊运动跟踪硬件的临床宠物系统中使用。这项工作将服务 作为开发数据驱动的运动估计算法的第一步，用于全身PET成像。这 创新在于开发什么是数据驱动的实时运动问题的解决方案 估计。

项目成果

Supervised Deep Learning for Head Motion Correction in PET.

PET 中头部运动校正的监督深度学习。

• DOI: 10.1007/978-3-031-16440-8_19
• 发表时间: 2022
• 期刊: Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Zeng,Tianyi;Zhang,Jiazhen;Revilla,Enette;Lieffrig,EléonoreV;Fang,Xi;Lu,Yihuan;Onofrey,JohnA
• 通讯作者: Onofrey,JohnA

MULTI-TASK DEEP LEARNING AND UNCERTAINTY ESTIMATION FOR PET HEAD MOTION CORRECTION.

宠物头部运动校正的多任务深度学习和不确定性估计。

• DOI: 10.1109/isbi53787.2023.10230791
• 发表时间: 2023
• 期刊: Proceedings. IEEE International Symposium on Biomedical Imaging
• 作者: Lieffrig,EléonoreV;Zeng,Tianyi;Zhang,Jiazhen;Fontaine,Kathryn;Fang,Xi;Revilla,Enette;Lu,Yihuan;Onofrey,JohnA
• 通讯作者: Onofrey,JohnA