Next-Generation Cardiovascular MRI powered by Artificial Intelligence

由人工智能驱动的下一代心血管 MRI

基本信息

批准号：
10385754
负责人：
AGGELOS K KATSAGGELOS
金额：
$ 23.04万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
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：
2021-04-15 至 2024-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10385754
关键词：
3-Dimensional 4D MRI Acceleration Address Adopted Adoption Adult Agreement Architecture Artificial Intelligence Cardiac Cardiovascular system Childhood Cine Magnetic Resonance Imaging Classification Clinical Community Hospitals Computer Vision Systems Computer software Data Databases Digital Imaging and Communications in Medicine Echocardiography Gadolinium Generations Goals Hour Human Image Image Analysis Image Enhancement Institution Intervention Learning Magnetic Resonance Imaging Patients Pattern Performance Perfusion Phase Physicians Process Prognosis Protocols documentation Reading Reporting Resources Sampling Scanning Series Speed Technology Testing Time Training United States National Institutes of Health base cardiovascular imaging clinical practice clinical translation computer science convolutional neural network cost data space deep learning empowered experimental study heart imaging high reward high risk image processing image reconstruction imaging Segmentation imaging facilities interest network architecture neural network neural network architecture next generation novel reconstruction response risk prediction routine practice signal processing single photon emission computed tomography success

项目摘要

Project Summary/Abstract: Despite the accuracy and versatility of cardiovascular MRI, its footprint is only 1% among cardiac imaging tests (SPECT, echocardiography, CT, MRI) in the US. While there are several factors such as referral patterns favoring SPECT and echocardiography among cardiologists that account for low utilization, the two addressable obstacles that preclude widespread adoption are lengthy scan time (imaging facility operational cost) and reading (physician cost). These obstacles must be addressed for community hospitals with limited resources to adopt cardiovascular MRI into clinical routine practice. While compressed sensing (CS), since its introduction into the MRI world in 2007, has led to highly-accelerated cardiovascular MRI acquisitions, the subsequent image reconstruction remains too slow (> 5 min for 2D time series, > 1 hour for 3D time series) for clinical translation (unmet need 1). Downstream, image analysis for cardiovascular MRI is notoriously labor intensive (e.g. 30- to 60-min) and limited (“circles” at two cardiac phases for cine MRI, whereas perfusion and late gadolinium-enhanced (LGE) images are evaluated visually), for what is essentially a basic computer vision task (unmet need 2). In direct response, we will address these two unmet needs and unlock the enormous potential of CMR using deep learning (DL). DL applications have exploded since advancements in optimization and GPU hardware. While several recent studies have applied neural networks such as convolutional neural networks (CNNs), U-Nets, and Generative Adversarial Nets (GANs) for reconstruction and segmentation, no study has implemented an inline end-to-end pipeline that receives raw k-space from the MRI scanner and delivers both reconstructed images and fully processed images automatically with high speed (< 1 min). The objectives of this study are: a) developing a network for image reconstruction with maximal acceleration (aim 1), (b) developing a network for image processing tasks (aim 2), and c) developing an integrated, end-to-end network that does both (aim 3). By developing an architecture that can simultaneously learn maximal acceleration, fine tune end-to-end performance, and perform reconstruction/inference using feed-forward networks, we anticipate a disruptive technology that will lead to a paradigm shift in cardiovascular MRI and increase its footprint in community hospitals. This 2-year study is doable because of the requisite database of raw k-space (not derived from DICOM) data (N = 617) and annotated cardiac MR images (N=3,021) from over 3,000 patients existing at our institution. Success of this proposal will deliver a disruptive technology that has potential to cause a paradigm shift in cardiovascular MRI and enable widespread adoption of cardiovascular MRI into clinical routine practice.

项目摘要/摘要：尽管心血管MRI的准确性和多功能性，但其足迹只是在美国，心脏成像测试（SPECT，超声心动图，CT，MRI）中有1％。虽然有几个在心脏病专家中有利于SPECT和超声心动图的转介模式等因素利用率较低，排除宽度采用的两个可寻址障碍是漫长的扫描时间（成像设施运营成本）和阅读（医师成本）。这些障碍必须解决资源有限的社区医院将心血管MRI采用到临床常规实践中。自2007年引入MRI世界以来，压缩感应（CS）导致了高度加速的心血管MRI获取，随后的图像重建仍然太慢（> 5分钟持续2次系列，3D时间序列> 1小时）用于临床翻译（未满足需要1）。下游，图像分析众所周知，心血管MRI是劳动密集型（例如30至60分钟）和有限的（两个心脏的“圆”） Cine MRI的阶段，而视觉上评估了许可和晚期增强（LGE）图像）对于本质上是一项基本的计算机视觉任务（未满足需要2）。在直接回复中，我们将解决这些问题两种未满足的需求，并使用深度学习（DL）解释了CMR的巨大潜力。自优化和GPU硬件的进步以来，已经探索了DL应用程序。而最近的几个研究应用了神经网络，例如卷积神经网络（CNN），U-NET和通用对抗网（gan）进行重建和细分，没有任何研究实施端到端从MRI扫描仪接收原始K空间的管道，并提供重建图像和完全高速（<1分钟）自动处理的图像。这项研究的目标是：a）开发具有最大加速度的图像重建网络（AIM 1），（b）为图像开发网络处理任务（AIM 2）和c）开发一个同时完成的集成的端到端网络（AIM 3）。经过开发一个可以轻松学习最大加速度，微调端到端的体系结构性能，并使用馈送网络执行重建/推理，我们预计会有破坏性将导致心血管MRI范式转移并增加社区的足迹的技术医院。这项为期两年的研究是可行的，因为原始K空间的必要数据库 DICOM（n = 617）和注释的心脏MR图像（n = 3,021），来自我们现有的3,000多名患者机构。该提案的成功将提供有可能引起范式的破坏性技术心血管MRI的转移，并使心血管MRI的宽度采用到临床常规实践中。