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

项目摘要 动机：钆基造影剂（GBCA）的使用约为 三分之一的核磁共振扫描GBCA独特的弛豫参数为我们提供了 成像对比度用于广泛临床应用，例如血管造影和肿瘤 侦测然而，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 甚至死亡发展NSF的风险阻止了数百万晚期乳腺癌患者 慢性肾脏疾病（CKD），接受对比增强MRI检查。近期 脑和身体内钆沉积的鉴定已经提出了额外的 关于GBCA使用的安全问题。研究表明， 未增强的T1加权MR图像上的强度与先前的 GBCA暴露，这种钆保留是独立的肾功能。而 最初的报告集中在线性GBCA，最近的报告表明，钆 大环GBCA也会发生沉积，尽管水平较低。FDA已经 最近发布了关于对比增强MRI后钆滞留的警告， 并要求GBCA制造商进行人类和动物研究，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这些问题： 使用人工智能开发低剂量和零剂量对比增强MRI (AI)深度学习（DL） 方法：这一快速通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DL方法，可以使用 造影前图像和造影增强图像仅用10%的标准 GBCA剂量。将构建一个软件基础设施来无缝集成DL MR扫描仪和PACS之间的软件。目标2（第二阶段）是开发DL方法 可以使用无GBCA合成全剂量对比增强MR图像 不同图像对比度的采集。在目标3（第二阶段），我们将在临床上验证 并评估低剂量和零剂量DL方法，包括轻度- 至中度CKD。非劣效性检验和合成的诊断性能 将进行全剂量图像与真实全剂量图像的比较。 意义：这项工作将导致更安全的对比增强MRI。低剂量和 零剂量对比增强MRI方法不仅将使数百万患有 晚期CKD，目前不能接受对比增强MRI，但更多 肾功能正常的患者，他们在服用后有钆潴留的风险。 对比增强MRI。

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.