Artificial Intelligence Driven Platform for PET/MR Imaging

人工智能驱动的 PET/MR 成像平台

• 批准号: 10652112
• 负责人: Hui Mao
• 金额: $ 76.39万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-19 至 2024-09-18
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10652112
• 关键词: Abdomen; Acceleration; Artificial Intelligence; Benchmarking; Blinded; Brain; Brain imaging; Childhood; Clinical; Collection; Data; Development; Diagnosis; Diagnostic; Disease; Dose; Elderly; Functional Imaging; Goals; Heart Diseases; Hybrids; Image; Imaging Techniques; Imaging technology; Lead; MRI Scans; Magnetic Resonance Imaging; Maps; Measurement; Medical Imaging; Methods; Molecular; Monitor; Morphologic artifacts; Motion; Multimodal Imaging; Noise; Outcome; Patient Care; Patient imaging; Patients; Performance; Play; Positron-Emission Tomography; Process; Protocols documentation; Radiation exposure; Radioactive Tracers; Resolution; Role; Running; Sampling; Scanning; Scheme; Signal Transduction; Solid; Source; Staging; Testing; Time; Tissues; X-Ray Computed Tomography; attenuation; base; clinical care; clinical practice; clinical translation; cohort; convolutional neural network; cost; data acquisition; deep learning; deep learning algorithm; experience; follow-up; generative adversarial network; high resolution imaging; image processing; image reconstruction; imaging approach; imaging capabilities; imaging platform; imaging system; improved; innovation; innovative technologies; molecular imaging; multimodality; multiparametric imaging; nervous system disorder; neural network architecture; novel; parametric imaging; patient population; pediatric patients; precision medicine; quantitative imaging; radiotracer; specific biomarkers; standard of care; whole body imaging

PROJECT SUMMARY This project will develop and test a novel patient data-driven artificial intelligence (AI)-assisted image processing platform to enable dose-reduced multimodal parametric simultaneous positron emission tomography/magnetic resonance imaging (PET/MRI). Data driven multiparametric imaging is an important approach in precision medicine with the state-of-the-art whole-body hybrid PET/MRI technology playing increasing roles. However, the broader application and promising potential of the has yet to be realized. Radiation exposure from the PET radiotracers remains to be a concern when using PET is used repeatedly in follow-up examinations, especially for pediatric patients. Corrections for PET attenuation and scatter using non- CT images from MRI for PET image reconstruction and quantification needs to run additional non-diagnostic MRI sequences to derive PET attenuation correction (AC) maps, not only making the scan time longer but also containing image artifact, especially when used in whole-body imaging applications. Collecting quantitative and functional image measurements with additional MRI sequences for parametric diagnostic information can further extend the scan time, making PET/MRI scans often intolerable by pediatric, elderly and motion-prone patients. With the proposed AI frameworks, we will perform MRI-based attenuation correction (MbAC), not just limited in brain but whole body which has not been solved, using patient-specific diagnostic MRI data that are collected on the integrated PET/MRI system and to generate diagnostic-quality full-dose-equivalent PET images from low- dose data (e.g., a fraction of standard dose of the radioactive tracer). Furthermore, high-resolution and multi- orientation MR images can be “synthesized” from low-resolution and noisy images collected from fast MRI scans using the developed AI frameworks. Building on our recent development of deep learning algorithms for MbAC and synthesizing MRI, CT and PET images from various types of source images, we will: 1) develop and optimize AI frameworks to generate desired high-resolution MR images with different contrasts from low-resolution data collected by rapid MRI scans; 2) develop and refine AI-driven MbAC method using synthesized high-resolution MR images and MRI-aided synthesis of full-dose-equivalent PET images from low-dose data; and 3) determine and evaluate the performance of developed AI-driven low-dose and fast PET/MRI platform in a cohort of patients who have received standard of care PET/CT. We will use quantitative image quality metrics and expert-review to compare AI-generated full-dose-equivalent images with those of the “ground truth” full-dose PET/MRI and matching PET/CT exams. This innovative low-dose and fast PET/MRI imaging approach can be implemented in clinical settings with high efficiency, reduced cost and better patient experience, especially for those who cannot use the standard of care PET/CT or PET/MRI procedures, enabling the multimodal molecular imaging applications of PET/MRI in disease staging, therapy monitoring and follow-up of patients with oncological, cardiac and neurological diseases.

该项目将开发和测试一种新颖的患者数据驱动的人工智能 （AI）辅助的图像处理平台，以实现剂量降低的多模态参数同时正电子 发射断层扫描/磁共振成像（PET/MRI）。数据驱动的多参数成像是一种 采用最先进的全身混合PET/MRI技术的精准医疗的重要方法 扮演越来越重要的角色。然而，更广泛的应用和良好的潜力尚未实现。 当使用PET重复使用时，来自PET放射性示踪剂的辐射暴露仍然是一个问题， 随访检查，尤其是儿科患者。使用非线性校正PET衰减和散射 用于PET图像重建和量化的MRI CT图像需要运行额外的非诊断性 MRI序列来导出PET衰减校正（AC）图，不仅使扫描时间更长， 包含图像伪影，特别是当用于全身成像应用时。收集定量和 用于参数诊断信息的附加MRI序列的功能图像测量可以进一步 延长扫描时间，使PET/MRI扫描往往无法忍受的儿童，老年人和运动倾向的患者。 通过所提出的AI框架，我们将执行基于MRI的衰减校正（MbAC），而不仅仅限于 大脑，但全身尚未解决，使用收集的患者特异性诊断MRI数据， 在集成的PET/MRI系统上，并从低剂量产生诊断质量的全剂量等效PET图像， 剂量数据（例如，放射性示踪剂标准剂量的一部分）。此外，高分辨率和多 定向MR图像可以从快速MRI扫描收集的低分辨率和噪声图像“合成” 使用开发的AI框架。基于我们最近开发的MbAC深度学习算法 并从各种类型的源图像合成MRI、CT和PET图像，我们将：1）开发和优化 AI框架用于从低分辨率数据生成具有不同对比度的所需高分辨率MR图像 通过快速MRI扫描收集; 2）使用合成的高分辨率开发和改进AI驱动的MbAC方法 MR图像和来自低剂量数据的全剂量等效PET图像的MRI辅助合成;以及3）确定 并在一组患者中评估开发的AI驱动的低剂量和快速PET/MRI平台的性能 接受过标准PET/CT的患者。我们将使用定量图像质量指标和专家评审 将AI生成的全剂量等效图像与“地面实况”全剂量PET/MRI图像进行比较， 匹配PET/CT检查这种创新的低剂量和快速PET/MRI成像方法可以在 高效、低成本和更好的患者体验的临床环境，特别是对于那些无法 使用标准的PET/CT或PET/MRI程序，实现多模式分子成像 PET/MRI在肿瘤患者疾病分期、治疗监测和随访中的应用， 心脏和神经系统疾病。