CIF: Small: Efficient Model-Based Iterative Reconstruction For High Resolution CT

CIF：小型：基于模型的高效迭代重建高分辨率 CT

基本信息

批准号：
2210866
负责人：
Alireza Entezari
金额：
$ 60万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2025-06-30
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210866&HistoricalAwards=false
关键词：
CIF Small Efficient Model Based

项目摘要

Enabling image reconstruction from low-dose X-ray data has been a major challenge in Computed Tomography (CT) imaging for several decades. Model-Based Iterative Reconstruction (MBIR) algorithms enable high-resolution image reconstruction from low-dose data by incorporating models for X-ray physics, the acquisition process and image priors into an optimization process for reconstruction. Despite promising dose-reduction results from these modern image reconstruction algorithms, the classical filtered back projection algorithm and its variants are widely employed in practical settings, especially when rapid imaging is necessary. The main impediment in translating these low-dose imaging technologies to practice has been their computational cost — long reconstruction time compared to classical methods.This project develops a novel algorithmic approach that leverages techniques from approximation theory together with performance optimization tools for tackling the computational challenges in MBIR. The project has a set of inter-connected goals that develop the proposed framework for 3-D optics, specializing to common X-ray and detector geometries. Moreover, signal processing tools are developed within this framework for increasing image resolution, in a computationally efficient way, in a broad set of CT inverse problems. The project also includes an extensive evaluation plan using established benchmarks as well as a repository of data maintained by the US National Library of Medicine specifically for validation of acceleration methods that seek to enable routine use of MBIR methods. These developments could play a game changing role in the applicability of low-dose imaging in practice and, ultimately, adoption in a wider range of applications.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.

从低剂量X射线数据启用图像重建已成为计算机断层扫描（CT）成像的主要挑战。基于模型的迭代重建（MBIR）算法通过将X射线物理学的模型，采集过程和图像先验转换为重建的优化过程，从低剂量数据进行了高分辨率图像重建。尽管这些现代图像重建算法的有望减少剂量减少，但经典的后退投影算法及其变体在实际设置中被广泛使用，尤其是在需要快速成像时。将这些低剂量成像技术转化为实践的主要障碍是它们的计算成本 - 与经典方法相比，长期重建时间。该项目开发了一种新型的算法方法，该方法利用近似理论的技术以及绩效优化工具来应对MBIR的计算挑战。该项目具有一组相互连接的目标，可以为3-D光学元件开发拟议的框架，专门针对常见的X射线和检测器几何形状。此外，在此框架内开发了信号处理工具，以在一系列CT反向问题中以计算有效的方式增加图像分辨率。该项目还包括一项广泛的评估计划，使用已建立的基准和美国国家医学图书馆维护的数据存储库，专门用于验证加速方法，该方法旨在实现常规使用MBIR方法。这些事态发展可能在实践中的低剂量成像的应用中起着改变游戏的作用，并最终在更广泛的应用程序中进行适应。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛的影响来审查标准，通过评估来评估。