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.

项目概要 动机：基于钆的造影剂（GBCA）大约用于 占所有 MRI 扫描的第三位。 GBCA 独特的松弛参数创造了不可或缺的 图像对比度适用于广泛的临床应用，例如血管造影和肿瘤 检测。然而，GBCA 的使用与 肾源性系统性纤维化（NSF）。 NSF 可能会带来痛苦，导致严重残疾，并且 甚至死亡。发生 NSF 的风险阻止了数百万晚期患者的健康 接受对比增强 MRI 检查导致的慢性肾脏病 (CKD)。最近的 大脑和身体内钆沉积的鉴定引起了额外的关注 关于使用 GBCA 的安全问题。研究表明信号增强 与之前相关的未增强 T1 加权 MR 图像上的强度 GBCA 暴露和钆滞留与肾功能无关。尽管 最初的报告重点关注线性 GBCA，最近的报告显示钆 大环 GBCA 也会发生沉积，尽管水平较低。 FDA 有 最近发布了关于对比增强 MRI 后钆滞留的警告， 并要求GBCA制造商进行人体和动物研究以进一步 评估这些造影剂的安全性。该项目通过以下方式解决了这些问题 使用人工智能开发低剂量和零剂量对比增强 MRI （人工智能）和深度学习（DL）。 方法：这个快速通道项目分为两个阶段和三个目标。目标 1（第一阶段）是 开发一种 DL 方法，可以使用以下方法合成全剂量对比增强 MR 图像 仅用标准值 10% 即可获取的预对比图像和对比增强图像 GBCA 剂量。将构建软件基础设施以无缝集成深度学习 MR 扫描仪和 PACS 之间的软件。目标 2（第二阶段）是开发 DL 方法 可以使用 GBCA-free 合成全剂量对比增强 MR 图像 具有不同图像对比度的采集。在目标 3（第二阶段）中，我们将进行临床验证 并评估低剂量和零剂量 DL 方法，包括对患有轻度 至中度 CKD。合成物的非劣效性检验和诊断性能 将执行与真实全剂量图像相比的全剂量图像。 意义：这项工作将带来更安全的对比增强 MRI。低剂量和 零剂量对比增强MRI方法不仅使数以百万计的癌症患者受益 晚期 CKD，目前无法接受对比增强 MRI，但还有更多 肾功能正常且术后有钆滞留风险的患者 对比增强 MRI。