CAREER: Developing a list-mode imaging paradigm

职业：开发列表模式成像范例

• 批准号: 2239707
• 负责人: Abhinav Kumar Jha
• 金额: $ 50万
• 依托单位: Washington University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2028-02-29
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239707&HistoricalAwards=false
• 关键词: CAREER; Developing; list; mode; imaging

Imaging sciences have revolutionized discoveries in a multitude of scientific applications. Multiple imaging systems, which include systems for imaging living systems and those for gamma-ray astronomy, have the unique ability to acquire data on a per-photon basis and store this data in list format. However, the full potential of these systems, referred to as list-mode systems, remains untapped since current methods to process data from these systems are sub-optimal and result in information loss. This information loss is even more concerning for list-mode systems, since typically these systems are photon-starved, and it is vital that maximal information be extracted from each detected photon. Towards addressing this important need, in this CAREER project, a new paradigm to process data from list-mode systems will be developed. The development of this paradigm will open new frontiers on imaging small-sized regions and low-count imaging. Direct impact will be demonstrated in quantifying physiological properties of small structures deep in the brain towards the goal of understanding the pathophysiology of Parkinson disease. The paradigm is also poised to impact many other sciences where such systems are used including astrophysics, biomedical research, geoscientific process monitoring, and nuclear security and safety. The highly integrated educational objective will be to train, educate, and motivate students at all levels about the fundamental aspects of imaging science, with the goal of grooming the next generation of imaging scientists who possess strong mathematical proficiency and the ability to translate this proficiency to develop methods to process data from imaging systems.The proposed program aims to push the fundamental limits of imaging by developing novel theoretical and computational methods to extract task-specific information from list-mode imaging systems. Conventional computational imaging methods are typically designed to operate with discrete data. Thus, when processing data from list-mode systems, these methods first bin that data, which, as has been shown in multiple studies, results in loss of task-specific information. To avoid this information loss, there is a crucial need for new methods that can process the list-mode data in this continuous format. The intellectual significance of this proposal stems from this need and lies in the development of a continuous-to-continuous operator-based paradigm and associated methods to process list-mode data. The research approach is to (a) develop new information-theory-based techniques to quantify limits on task performance with list-mode systems, (b) design and validate algorithms to extract task-specific information from list-mode systems, including new reconstruction algorithms that will estimate continuous representation of the underlying object and (c) develop algorithms to estimate mean regional uptake in small regions of the brain from single-photon emission computed tomography images. The proposed list-mode paradigm will strongly impact both the fundamental and applied aspects of imaging science by providing new rigorous mathematical formalisms to process list-mode data, thereby yielding the ability to retrieve previously unextracted information from these systems.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.

成像科学已经在众多科学应用中带来了革命性的发现。多种成像系统，包括生命系统成像系统和伽马射线天文学成像系统，具有以每个光子为基础获取数据并以列表格式存储这些数据的独特能力。然而，这些系统（称为列表模式系统）的全部潜力尚未得到充分开发，因为目前处理这些系统数据的方法不是最优的，并且会导致信息丢失。对于列表模式系统，这种信息损失更令人担忧，因为这些系统通常是光子匮乏的，从每个检测到的光子中提取最大的信息是至关重要的。为了解决这一重要需求，在这个CAREER项目中，将开发一种新的范例来处理来自列表模式系统的数据。这种模式的发展将为成像小尺寸区域和低计数成像开辟新的领域。直接影响将在量化大脑深处小结构的生理特性方面得到证明，以实现理解帕金森病的病理生理学目标。这一范式也将影响到许多其他科学领域，其中包括天体物理学、生物医学研究、地球科学过程监测以及核安保与安全。高度整合的教育目标将是培养、教育和激励各级学生了解成像科学的基本方面，目标是培养下一代的成像科学家，他们具有很强的数学能力，并有能力将这种能力转化为处理成像系统数据的方法。该计划旨在通过开发新的理论和计算方法来从列表模式成像系统中提取特定任务的信息，从而突破成像的基本限制。传统的计算成像方法通常被设计为处理离散数据。因此，在处理来自列表模式系统的数据时，这些方法首先将这些数据存储起来，正如多项研究所显示的那样，这会导致丢失特定于任务的信息。为了避免这种信息丢失，非常需要能够以这种连续格式处理列表模式数据的新方法。该建议的智力意义源于这一需求，并在于开发一种基于连续到连续运算符的范式和相关方法来处理列表模式数据。研究方法是(a)开发新的基于信息理论的技术来量化列表模式系统对任务性能的限制，(b)设计和验证从列表模式系统中提取任务特定信息的算法，包括新的重建算法，该算法将估计潜在对象的连续表示，以及(c)开发算法来估计从单光子发射计算机断层扫描图像中大脑小区域的平均区域摄取。提出的列表模式范式将通过提供新的严格的数学形式来处理列表模式数据，从而产生从这些系统中检索以前未提取的信息的能力，从而强烈地影响成像科学的基础和应用方面。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。