SCH:Artificial Intelligence for Contrast-Enhanced Imaging

SCH：用于对比度增强成像的人工智能

• 批准号: 2306545
• 负责人: Shuo Li
• 金额: $ 112.5万
• 依托单位: Case Western Reserve University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2306545&HistoricalAwards=false
• 关键词: SCH; Artificial; Intelligence; Contrast; Enhanced

The instances of liver tumors and its associated cost has been increasing steadily in the last few years. Diagnosing liver tumors currently requires injecting patients with a chemical contrast agent while doing magnetic resonance imaging (MRI). These chemical contrast agents are time consuming to administer, expensive, and have morbid side effects for many individuals. This Smart and Connected Health (SCH) award brings together a multidisciplinary team, comprising researchers from computer science, biomedical engineering, and clinical radiology to develop an Artificial Intelligence generated virtual contrast MRI thereby reducing the time, cost, and morbidity due to the chemical contrast agent. This project will provide a diverse group of students and clinical fellows with interdisciplinary training in machine learning, image processing, and medical imaging. Additionally, this project will engage students from middle/high school students (K-12 outreach) to doctoral students and postdoctoral fellows.This project proposes novelties in contextual adversarial learning, uncertainty, and reliability-based analysis to enable fundamental understanding and computer modelling of contrast enhanced imaging. The project proposes to (1) investigate “contrasomics”, a brand new category of contextual features; (2) develop novel cross-domain contextual models to detect, classify, and quantify lesions, and to synthesize virtual contrast images that have equivalent diagnostic value with real contrast enhanced imaging; (3) develop novel uncertainty and reliability analysis to gain the trust of end users; (4) validate the models with liver cancer/tumour classification using MRI. This project brings together computer science, biomedical engineering, and clinical radiology researchers and proposes an integrated multi-disciplinary education and outreach program to achieve the broadest possible dissemination of the knowledge gained from this work. The project also proposes potential future transfer to practice (industry and clinic) and wide dissemination of advances to broad communities beyond the image processing, machine learning, and medical data analytics domains.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在过去的几年中，肝肿瘤及其相关成本的实例一直在稳步增长。当前诊断肝肿瘤需要在进行磁共振成像（MRI）时注射化学对比剂的患者。这些化学对比代理很耗时，用于管理，昂贵，并为许多人带来病态的副作用。这项智能和联系的健康（SCH）奖将一个多学科的团队汇集在一起​​，完成了计算机科学，生物医学工程和临床放射学的研究人员，以开发人工智能产生的虚拟对比度MRI，从而减少由于化学对比剂而导致的时间，成本和发病率。该项目将在机器学习，图像处理和医学成像中为学生和临床研究员提供跨学科的培训。此外，该项目将与中学生（K-12外展）的学生联系到博士和博士后研究员。本项目的提案在上下文对抗性学习，不确定性和基于可靠性的基于可靠性的分析中进行新颖性，以启用对比造成造成造成造成造成造成造成造成的成像的基本理解和计算机建模。 （1）调查“矛盾”的项目建议，这是一个全新的上下文特征类别； （2）开发新型的跨域上下文模型来检测，分类和量化病变，并合成具有等效诊断值的虚拟对比度图像，并具有实际的对比度增强成像； （3）开发新颖的不确定性和可靠性分析以获得最终用户的信任； （4）使用MRI验证具有肝癌/肿瘤分类的模型。该项目汇集了计算机科学，生物医学工程以及临床放射学研究人员，并提出了一项综合的多学科教育和外展计划，以实现从这项工作中获得的知识的最广泛传播。该项目还提出了未来向实践（行业和诊所）的潜在转移，并将进步的广泛传播到除图像处理，机器学习和医疗数据分析领域之外的广泛社区。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来支持的。