CDS&E: Collaborative Research: Scalable Deep Learning-Based Quantitative Ultrasound Tomography

超声计算机断层扫描（USCT）采用先进的成像方法，通过快速、低成本的扫描提供高度特异性的组织分化和诊断，无需使用镇静剂和x射线。然而，这些基于物理的成像方法目前需要较长的处理时间，并且有时会遇到重建错误，这阻碍了它们在时间敏感的USCT应用（例如脑和乳腺成像）中的广泛应用。为了解决这些差距，该项目将创建两种基于人工智能的方法，以显著提高最先进的USCT技术的重建速度和质量。更快、更强大的USCT系统将潜在地带来社会效益，如改善医疗结果、降低风险、提高患者满意度和降低医疗成本。合作研究团队将积极招募和指导具有不同背景的本科生和研究生参与这项研究，与K-12教育工作者和学生接触，以培养学生对数据科学、计算和STEM领域的兴趣，将研究结果纳入课堂模块，并将技术和研究结果传播给公众。该项目的目标是创建两个开源，高性能计算（HPC）和基于深度学习（DL）的框架，以显着提高基于全波形反演（FWI）的USCT的重建速度和质量。FWI技术最近使USCT能够重建详细的定量材料/组织参数，但它们的重建速度慢，计算成本高。为了应对这些挑战，其中一种方法将创新性地将伴随断层扫描理论（ATT）整合到生成对抗网络（GAN）中，通过为GAN提供强先验，可靠地加速基于fwi的USCT。使用这种方法可以实现快速的患者筛查。第二种深度学习方法在训练和应用中利用物理指导、循环一致性来提供非凡的、详细的重建。第二种方法减少了训练中对地面真值模型的依赖，减轻了对初始模型的依赖，并利用专门构建的计算硬件进行深度学习加速，因此可以a)降低假阳性率，同时减少不必要的额外测试/活检；b)减少假阴性，从而实现早期诊断/治疗。本文将为这两种方法推导理论基础。计算细节，如框架设计/调整和科学问题的研究（例如，初始模型的影响和正演模型误差）将被生成和传播。将创建一个网络基础设施，提供支持HPC/ gpu的成像数据生成和培训框架，以及两个适合基于dl的USCT研究的临床相关数据库，并向公众开放。该项目开发的框架构建理念和方法将展示一种高效、系统的方法，以可扩展和并行的方式应用基于深度学习的技术，从而加速超声成像、光声断层扫描、x射线计算机断层扫描、雷达技术、地球物理和磁共振成像等更广泛的dl相关主题。该项目由生物医学系统工程（EBMS）计划和促进竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Empowered by advanced imaging methods, ultrasound computed tomography (USCT) provides highly specific tissue differentiation and diagnosis through rapid, low-cost scanning without using sedatives and X-rays. However, these physics-based imaging methods currently require a long processing time and at times encounter reconstruction errors, hindering them from being widely used in time-sensitive USCT applications (e.g., brain and breast imaging). To address these gaps, this project will create two artificial intelligence-based approaches to significantly improve the reconstruction speed and the quality of the state-of-the-art USCT technologies. A faster and more powerful USCT system will potentially lead to societal benefits such as improved medical outcomes, reduced risks, higher patient satisfaction, and reduced healthcare costs. The collaborative team of investigators will actively recruit and mentor undergraduate and graduate students with diverse backgrounds to participate in this research, reach out to K-12 educators and students to create student interest in data science, computing, and STEM fields, incorporate the findings into class modules, and disseminate the technology and findings to the public.This goal of this project is to create two open-source, high-performance computing (HPC)-enabled, and deep learning (DL)-based frameworks to significantly improve the reconstruction speed and the quality of full waveform inversion (FWI)-based USCT. FWI techniques have recently enabled USCT in the reconstruction of detailed quantitative material/tissue parameters, but they have a slow reconstruction speed and a high-computational cost. To address these challenges, one of the approaches will innovatively incorporate the adjoint-tomography theory (ATT) into a generative adversarial network (GAN) to reliably accelerate FWI-based USCT by providing strong priors for GAN. Rapid patient screening can be achieved using this method. The second DL approach leverages physics-guided, cycle-consistence in both training and its application to provide extraordinary, detailed reconstruction. The second method reduces the reliance on ground truth models in training, alleviates the dependence of initial models, and utilizes the purposely-built computing hardware for DL acceleration and hence can a) lower the false-positive rates while reducing unnecessary extra tests/biopsies and b) decrease the false negatives to enable early diagnosis/treatment. The theoretical foundations will be derived for both approaches. Computational details such as framework designing/tuning and the studies of scientific problems (e.g., influences of initial models and forward modeling errors) will be generated and disseminated. A cyberinfrastructure that provides HPC/GPU-enabled imaging data generation and training frameworks and two clinically relevant databases suitable for DL-based USCT studies will be created and made available to the public. The framework building philosophy and approaches developed in this project will demonstrate an efficient, systematic approach to applying deep learning-based techniques in a scalable and parallel manner, thereby accelerating broader DL-related topics in ultrasound imaging, photoacoustic tomography, X-ray computed tomography, radar technologies, geophysics, and magnetic resonance imaging.This project is jointly funded by the Engineering of Biomedical Systems (EBMS) Program and the Established Program to Stimulate Competitive Research (EPSCoR).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.