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

• 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

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.

几十年来，实现从低剂量X射线数据重建图像一直是计算机断层扫描(CT)成像领域的一大挑战。基于模型的迭代重建(MBIR)算法通过将X射线物理模型、采集过程和图像先验信息合并到重建的优化过程中，实现了从低剂量数据进行高分辨率图像重建。尽管这些现代图像重建算法有很好的剂量减少效果，但经典的滤波反投影算法及其变体在实际环境中被广泛使用，特别是在需要快速成像的情况下。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-与经典方法相比重建时间较长。该项目开发了一种新的算法方法，利用近似理论的技术和性能优化工具来解决MBIR中的计算挑战。该项目有一套相互关联的目标，以开发拟议的3D光学框架，专门用于常见的X射线和探测器几何形状。此外，信号处理工具是在这个框架内开发的，用于在一系列广泛的CT逆问题中以计算高效的方式提高图像分辨率。该项目还包括一项广泛的评估计划，使用已建立的基准，以及由美国国家医学图书馆维护的数据库，专门用于验证寻求实现MBIR方法常规使用的加速方法。这些发展可能会在低剂量成像在实践中的适用性以及最终在更广泛的应用中采用方面发挥改变游戏规则的作用。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。

项目成果

Exact gram filtering and efficient backprojection for iterative CT reconstruction

用于迭代 CT 重建的精确克过滤和高效反投影

• DOI: 10.1002/mp.15547
• 发表时间: 2022
• 期刊: Medical Physics
• 影响因子: 3.8
• 作者: Shu, Ziyu;Entezari, Alireza
• 通讯作者: Entezari, Alireza

Sparse-view and limited-angle CT reconstruction with untrained networks and deep image prior

• DOI: 10.1016/j.cmpb.2022.107167
• 发表时间: 2022-10
• 期刊: Computer methods and programs in biomedicine
• 影响因子: 6.1
• 作者: Ziyu Shu;A. Entezari