SCH:Artificial Intelligence for Contrast-Enhanced Imaging

在过去的几年里，肝肿瘤及其相关费用的情况一直在稳步增加。诊断肝脏肿瘤目前需要在进行磁共振成像（MRI）时向患者注射化学造影剂。这些化学造影剂给药耗时、昂贵，并且对许多个体具有病态的副作用。 这个智能互联健康（SCH）奖项汇集了一个多学科团队，包括计算机科学，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，从而减少时间，成本和由于化学造影剂的发病率。该项目将为不同群体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，该项目将吸引从初中/高中学生（K-12推广）到博士生和博士后研究员的学生。该项目提出了情境对抗学习，不确定性和基于可靠性的分析的新颖性，以实现对对比增强成像的基本理解和计算机建模。该项目提出：（1）研究“对比组学”，一种全新的背景特征类别;（2）开发新的跨域背景模型来检测、分类和量化病变，并合成与真实的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）利用MRI对模型进行肝癌/肿瘤分类验证。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最广泛传播。该项目还提出了潜在的未来转移到实践（行业和诊所）和广泛传播的进步，以广泛的社区超越图像处理，机器学习和医疗数据分析领域。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

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.