Constrained Disentanglement (CODE) Network for CT Metal Artifact Reduction in Radiation Therapy

用于减少放射治疗中 CT 金属伪影的约束解缠结 (CODE) 网络

基本信息

批准号：
10184493
负责人：
Bruno De Man
金额：
$ 259.38万
依托单位：
RENSSELAER POLYTECHNIC INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2024-09-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10184493
关键词：
Affect Aging Artificial Intelligence Benchmarking Cancer Patient Cause of Death Cessation of life Clinic Clinical Communities Computer software Data Dimensions Estimation Techniques Evaluation General Hospitals Goals Human Image Implant Institutes Lead Learning Malignant Neoplasms Massachusetts Metals Methods Modeling Modification Morphologic artifacts Network-based Output Patient-Focused Outcomes Patients Performance Photons Positioning Attribute Protons Radiation therapy Recording of previous events Records Reporting Research Residual state Supervision Techniques Technology Technology Transfer Testing Therapeutic Studies Time Trace metal Translating Translations Treatment outcome World Health Organization X-Ray Computed Tomography base clinical application clinical imaging clinical translation computerized data processing deep learning dosimetry experimental study image reconstruction imaging Segmentation imaging modality imaging software improved industry partner innovation learning network learning strategy man meetings open source proton therapy reconstruction tomography treatment planning

项目摘要

 ABSTRACT The World Health Organization reported that cancer is the second leading cause of death globally and is re- sponsible for 9.6 million deaths in 2018. Approximately 50% of all cancer patients receive radiation therapy (RT). Many of them have metal implants, which induce image artifacts in the treatment planning CT images and compromise or preclude treatment in an estimated 15% of all radiation therapy patients. Despite extensive CT metal artifact reduction (MAR) research it remains one of the long-standing challenges in the CT field, without a clinically satisfactory solution. The overall goal of this project is to develop cutting-edge deep learning imaging methods and software solutions for commercial CT scanners to eliminate CT metal artifacts in general and improve RT in particular. We propose a three-pronged approach to systematically tackle this challenge in three specific aims: (1) adversarial learning techniques for estimation of sinogram missing data and metal traces; (2) constrained disentanglement (CODE) networks to remove CT image artifacts during image reconstruction, through post-processing, and in both data and image domains; and (3) systematic evaluation of our proposed CT MAR techniques and clinical translation into robust RT planning methods to maximize the RT treatment planning accuracy and thus improve patient outcomes. Our synergistic track records in CT MAR research, especially with deep imaging methods over the past three years, promises an unprecedented opportunity for a brand-new solution to CT MAR. For the first time we will integrate contemporary AI innovations in data preprocessing, image reconstruction, post-processing, observer studies and treatment planning synergistically in a unified data-driven framework, positioning this project uniquely to eliminate metal artifacts and their complications in radiation therapy. This project will be pursued through the long-term academic-industrial partnership among Dr. Ge Wang at Ren- sselaer Polytechnic Institute (RPI), Dr. Bruno De Man at GE Research Center (GRC), and Dr. Harald Paganetti at Massachusetts General Hospital (MGH). While our teams will collaborate closely through the whole project, GRC has a history of CT research and translation, including direct raw data processing, and will focus on Aim 1. RPI is a pioneering group in tomographic reconstruction, especially deep-learning-based CT imaging, and will lead Aim 2. The MGH team is at the forefront of radiation therapy research and will be responsible for Aim 3. Upon completion of this project, we will have redefined the state of the art of CT MAR, largely eliminating CT metal artifacts and substantially improving radiation therapy planning and delivery accuracy. With the above-proposed networks for CT MAR, metal artifacts will have been basically eliminated, targeting residual errors <10 HU for photon and proton therapy planning, with the goal of reducing the clinical diametric error to ±3% and the proton range error due to metal artifacts to <2mm. Since our approach is software-based and open-source, the path for technology transfer and clinical translation is clearly defined, as well tested before.

 抽象的世界卫生组织报告说，癌症是全球第二大死亡原因，并且正在重新进行 2018年960万人死亡的发起人。大约50％的癌症患者接受放射治疗（RT）。他们中的许多人都有金属histranivation骨，在治疗计划中诱导图像伪影CT图像和在所有放射治疗患者中，估计15％的折衷或排除治疗。尽管CT广泛金属伪像减少（MAR）研究仍然是CT领域的长期挑战之一，没有临床满足的解决方案。该项目的总体目标是开发最先进的深度学习成像方法和软件解决方案商业CT扫描仪通常会消除CT金属伪像，尤其是改善RT。我们建议在三个具体目标中，有系统地应对这一挑战的三管齐下的方法：（1）对抗性学习估算正弦图缺失数据和金属痕迹的技术；（2）限制的分解（代码）在图像重建过程中，通过后处理以及两个数据中删除CT图像伪像的网络和图像域；（3）我们提出的CT MAR技术和临床翻译的系统评估进入强大的RT计划方法，以最大化RT治疗计划的准确性，从而提高患者结果。我们在CT MAR研究中的协同记录记录，尤其是对有关的深层成像方法过去三年，向CT Mar提供了崭新的解决方案，这是前所未有的机会。首次我们将在数据预处理，图像重建，后处理中整合当代AI创新，观察者的研究和治疗计划在统一数据驱动的框架中协同定位独特的项目以消除金属伪像及其在放射疗法中的并发症。该项目将通过Ren- Ge Wang博士之间的长期学术工业合作伙伴关系来追求 Sselaer理工学院（RPI），GE研究中心（GRC）的Bruno de Man博士和Harald Paganetti博士在马萨诸塞州综合医院（MGH）。尽管我们的团队将在整个项目中密切合作，但 GRC具有CT研究和翻译的历史，包括直接的原始数据处理，并将专注于AIM 1。 RPI是层析成像重建方面的开创性小组，尤其是基于深度学习的CT成像，将会 LEAD AIM 2。MGH团队处于放射治疗研究的最前沿，并将负责AIM 3。该项目完成后，我们将重新定义CT Mar的艺术状况，在很大程度上消除了CT 金属伪像，并大大改善了放射治疗计划和交付精度。与 CT MAR的上述网络，金属伪像基本上将被消除，以靶向为目标用于光子和质子治疗计划的错误<10 HU，目的是将临床直径误差降低到 ±3％和由于金属伪像<2mm而导致的质子范围误差。由于我们的方法是基于软件的，并且开源，技术转移和临床翻译的路径和临床翻译的定义清楚，并且以前进行了测试。