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.

项目概要 正电子发射断层扫描 (PET) 是一种成像方式，允许精英和研究人员研究 人体，特别是大脑的生理或病理过程，通过使用特定的 对于脑部 PET 成像来说，扫描过程中患者头部的运动对准确度提出了挑战。 PET 图像重建和随后的定量分析是由于头部运动引起的。 扫描持续时间较长，扫描时间通常超过一小时，而且有些扫描时间较长，从而加剧了这种情况。 PET 研究特别涉及由于心理变化而难以保持静止的受试者， 例如患有阿尔茨海默病和帕金森病等神经退行性疾病的患者，或 心理上的，例如患有焦虑症的受试者，或需要参与涉及的任务 运动，例如扫描时吸烟。在脑部扫描中，平均头部运动可能会有所不同。 毫米在临床扫描中为更长的研究扫描提供了三倍的数量，5 毫米的头部运动可以。 在区域强度方面产生高达约 35% 的偏差，在分布量估计方面产生约 15% 的偏差，这可能 远大于在区分不同区域的区域强度或结合电位中观察到的差异 因此，正在研究的人口群体跟踪和纠正头部运动的能力将很高。 在临床和研究 PET 研究中的实用性 过去，已经有许多运动矫正方法。 然而，除了基于硬件的方法之外，还没有可以跟踪频繁的方法。 基于硬件的方法不易用于 PET 采集期间的动态头部运动。 由于高度定制的软件/硬件设置，临床翻译或被其他研究机构使用。 为了应对这一挑战，我们建议开发一种数据驱动的方法，使用深度学习来跟踪和 使用 PET 原始数据估计刚性头部运动，并结合示踪剂类型和时间作为条件 将变量引入到这种深度神经网络设计中，以便处理不同的 PET 示踪剂类型及其动态 总体而言，这些解决方案将提供数据驱动的运动估计方法来改善行为。 具体来说，我们将从开发和测试我们的刚性方法开始。 使用单追踪器 PET 原始数据进行头部运动估计，然后我们将对多追踪器进行评估。 最后，将运动估计方法应用于具有多种示踪剂的真实 PET 数据。 在探索阶段，我们将把飞行时间信息集成到基于深度学习的运动预测中。 该提案的意义在于，它将提高 PET 成像的实时质量，并有可能提高 允许在没有特殊运动跟踪硬件的临床 PET 系统中使用这项工作。 这是开发用于全身 PET 成像的数据驱动运动估计算法的第一步。 创新在于开发数据驱动的实时运动问题解决方案 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