Low- and Zero-dose Contrast-enhanced MRI Using Deep Learning

使用深度学习的低剂量和零剂量对比增强 MRI

• 批准号: 10225646
• 负责人: Enhao Gong
• 金额: $ 75.57万
• 依托单位: SUBTLE MEDICAL, INC.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10225646
• 关键词: Address; Affect; Angiography; Animals; Artificial Intelligence; Brain; Cessation of life; Chronic Kidney Failure; Clinical; Clinical Research; Computer software; Contrast Media; Data Set; Deposition; Detection; Development; Diagnostic; Dose; Evaluation; Gadolinium; Goals; Health Professional; Hospitals; Human; Image; Image Enhancement; Infrastructure; Kidney Failure; Link; MRI Scans; Magnetic Resonance Imaging; Manufacturer Name; Medical Imaging; Methods; Modeling; Monitor; Motivation; Nephrogenic Systemic Fibrosis; Pain; Pathology; Patients; Performance; Phase; Relaxation; Renal function; Reporting; Research; Risk; Safety; Signal Transduction; Small Business Innovation Research Grant; Software Validation; System; Testing; Training; Work; base; clinical application; contrast enhanced; contrast imaging; convolutional neural network; deep learning; deep learning algorithm; disability; experience; image reconstruction; learning strategy; prevent; software development; software infrastructure; tumor

Project Summary Motivation: Gadolinium-based contrast agents (GBCAs) are used in approximately a third of all MRI scans. The unique relaxation parameters of GBCAs create indispensable image contrast for a wide range of clinical applications, such as angiography and tumor detection. However, the usage of GBCAs has been linked to the development of nephrogenic systemic fibrosis (NSF). NSF can be painful, cause severe disability, and even death. The risk of developing NSF prevents millions of patients with advanced chronic kidney disease (CKD) from receiving contrast-enhanced MRI exams. The recent identification of gadolinium deposition within the brain and body has raised additional safety concerns about the usage of GBCAs. Studies have demonstrated increased signal intensity on the unenhanced T1-weighted MR images that is correlated with previous GBCA exposure, and this gadolinium retention is independent of renal function. While initial reports focused on linear GBCAs, more recent reports show that gadolinium deposition occurs with macrocyclic GBCAs as well, albeit at lower levels. FDA has recently issued warnings about gadolinium retention following contrast-enhanced MRI, and required GBCA manufacturers to conduct human and animal studies to further assess the safety of these contrast agents. This project addresses these concerns by developing low-dose and zero-dose contrast-enhanced MRI using artificial intelligence (AI) and deep learning (DL). Approach: This fast-track project has two phases and three aims. Aim 1 (Phase I) is to develop a DL method that can synthesize full-dose contrast-enhanced MR images using pre-contrast images and contrast-enhanced images acquired with only 10% of standard GBCA dose. A software infrastructure will be constructed to seamlessly integrate the DL software between MR scanners and PACS. Aim 2 (Phase II) is to develop a DL method that can synthesize full-dose contrast-enhanced MR images using GBCA-free acquisitions with different image contrast. In Aim 3 (Phase II), we will clinically validate and evaluate both low-dose and zero-dose DL methods, including on patients with mild- to-moderate CKD. Non-inferiority tests and diagnostic performance of the synthesized full-dose images compared to the true full-dose images will be performed. Significance: This work will lead to safer contrast-enhanced MRI. The low-dose and zero-dose contrast-enhanced MRI method will benefit not only millions of patients with advanced CKD, who cannot currently undergo contrast-enhanced MRI, but many more patients with normal kidney function, who are at the risk of gadolinium retention after contrast-enhanced MRI.

项目摘要 动机：基于Gadolinium的对比剂（GBCA）大约使用 所有MRI扫描中的三分之一。 GBCA的独特放松参数创造了必不可少的 多种临床应用（例如血管造影和肿瘤）的图像对比度 检测。但是，GBCA的使用与发展有关 肾病全身纤维化（NSF）。 NSF可能很痛苦，会导致严重的残疾，并且 甚至死亡。发展NSF的风险可防止数百万患者 慢性肾脏疾病（CKD）接受对比增强的MRI检查。最近 识别大脑和身体内部沉积已增加了额外 对GBCA的使用的安全担忧。研究表明信号增加 与以前相关的未增强T1加权MR图像的强度 GBCA暴露，该gadolin保留率与肾功能无关。尽管 最初的报道重点是线性GBCAS，最近的报告表明Gadolinium 尽管在较低的水平上，但也与大环GBCA一起沉积。 FDA有 最近在对比增强MRI之后发出警告， 并要求GBCA制造商进行人类和动物研究以进一步 评估这些对比剂的安全性。该项目解决了这些问题 使用人工智能开发低剂量和零剂量对比增强的MRI （AI）和深度学习（DL）。 方法：这个快速轨道项目有两个阶段和三个目标。目标1（第一阶段）是 开发一种可以使用全剂量对比增强MR图像合成的DL方法 预先对比的图像和对比度增强图像仅以标准的10％获取 GBCA剂量。将构建软件基础架构以无缝整合DL MR扫描仪和PAC之间的软件。 AIM 2（II阶段）是开发DL方法 可以使用无GBCA合成全剂量对比增强的MR图像 具有不同图像对比度的采集。在AIM 3（第二阶段）中，我们将在临床上验证 并评估低剂量和零剂量DL方法，包括针对温和的患者 委员CKD。合成的非效率测试和诊断性能 将执行与真正的全剂量图像相比的全剂量图像。 意义：这项工作将导致更安全的对比度增强MRI。低剂量和 零剂量对比增强的MRI方法不仅使数百万患者受益 高级CKD，目前无法接受对比增强的MRI，但更多 肾功能正常的患者，在 对比增强的MRI。