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.

在过去的几年里，肝肿瘤的病例和相关的费用一直在稳步增加。目前诊断肝脏肿瘤需要在做磁共振成像(MRI)的同时给患者注射化学造影剂。这些化学造影剂给药费时、昂贵，而且对许多人都有病态的副作用。这一智能互联健康(SCH)奖汇集了一个多学科团队，其中包括来自计算机科学、生物医学工程和临床放射学的研究人员，以开发人工智能生成的虚拟对比MRI，从而减少由于化学造影剂而产生的时间、成本和发病率。该项目将为不同的学生和临床研究员提供机器学习、图像处理和医学成像方面的跨学科培训。此外，这个项目将吸引从初中生/高中生(K-12外展)到博士后学生和博士后研究员。该项目在上下文对抗性学习、不确定性和基于可靠性的分析方面提出了新的观点，以实现对比度增强成像的基本理解和计算机建模。该项目建议(1)研究一种全新的上下文特征--“对比组学”；(2)开发新的跨域上下文模型来检测、分类和量化病变，并合成与真实对比增强成像具有同等诊断价值的虚拟对比图像；(3)开发新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验证模型与肝癌/肿瘤分类。该项目汇集了计算机科学、生物医学工程和临床放射学研究人员，并提出了一个综合的多学科教育和推广计划，以实现从这项工作中获得的知识的尽可能广泛的传播。该项目还建议将未来的潜在成果转移到实践中(行业和临床)，并将进展广泛传播到图像处理、机器学习和医疗数据分析领域以外的广泛社区。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。