Artificial Intelligence Driven Platform for PET/MR Imaging

项目摘要本项目将开发和测试一种新型的患者数据驱动的人工智能 (AI)辅助图像处理平台实现剂量降低的多峰参数同时正电子 发射断层扫描/磁共振成像(PET/MRI)。数据驱动多参数成像是一种 PET/MRI全身混合成像技术在精密医学中的重要应用 发挥着越来越大的作用。然而，它的更广泛的应用和广阔的潜力还有待于实现。 当重复使用PET时，来自PET放射性示踪剂的辐射暴露仍然是一个令人担忧的问题 后续检查，特别是对儿科患者。对PET衰减和散射的校正使用非 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，以获得PET衰减校正(AC)图，不仅使扫描时间更长，而且 包含图像伪影，尤其是用于全身成像应用时。收集定量和 具有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，使儿童、老年人和运动倾向患者经常无法忍受PET/MRI扫描。 使用建议的AI框架，我们将执行基于MRI的衰减校正(MbAC)，而不仅仅限于 大脑而是整个身体都没有得到解决，使用收集的患者特定的诊断MRI数据 在集成的PET/MRI系统上，并生成诊断质量的全剂量当量PET图像 剂量数据(例如，放射性示踪剂标准剂量的一小部分)。此外，高分辨率和多分辨率 定位磁共振图像可以从快速MRI扫描收集的低分辨率和有噪声的图像中合成 使用开发的人工智能框架。基于我们最近针对MbAC的深度学习算法的发展 从各种类型的源图像中合成MRI、CT和PET图像，我们将：1)开发和优化 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图像的AI框架 通过快速MRI扫描收集；2)开发和改进人工智能驱动的MbAC方法，使用合成的高分辨率 MR图像和MRI辅助从低剂量数据合成全剂量当量PET图像；以及3)确定 并在一组患者中评估开发的人工智能驱动的低剂量快速PET/MRI平台的性能 接受了标准的PET/CT治疗。我们将使用定量图像质量指标和专家评审 将人工智能生成的全剂量当量图像与“地面真相”全剂量PET/MRI图像和 匹配的PET/CT检查。这种创新的低剂量、快速的PET/MRI成像方法可以在 高效率、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CARE PET/CT或PET/MRI程序，实现多模式分子成像 PET/MRI在肿瘤分期、疗效监测及随访中的应用 心脏和神经疾病。

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.