Catalyst Project: Computer algorithms and simulations for CT image reconstruction and segmentation utilizing optimal sampling lattices and efficient domains

Catalyst 项目：利用最佳采样点阵和有效域进行 CT 图像重建和分割的计算机算法和模拟

• 批准号: 2000158
• 负责人: Xiqiang Zheng
• 金额: $ 20万
• 依托单位: Voorhees College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000158&HistoricalAwards=false
• 关键词: Catalyst; Project; Computer; algorithms; simulations

Catalyst Projects provide support for Historically Black Colleges and Universities to work towards establishing research capacity of faculty to strengthen science, technology, engineering and mathematics undergraduate education and research. It is expected that the award will further the faculty member's research capability, improve research and teaching at the institution, and involve undergraduate students in research experiences. This project at Vorhees College seeks to develop computed tomography (CT) image reconstruction and segmentation methods using optimal sampling lattices and provides an opportunity for undergraduate students to enhance their education through research experiences in computer modeling. The researcher has established a strong collaboration with faculty at the University of South Carolina. Computed tomography is an important tool to create the internal image of a physical body. Image reconstruction is to compute the internal image from the scanned data and image segmentation may help to locate the internal objects or their boundaries in the image. The usual CT computations are done on a Cartesian lattice whose pixels are squares or cubes. However optimal sampling lattices, such as 2D hexagonal and 3D face centered cubic and body centered cubic lattices, provide more efficient sampling and better adjacency relation than the traditional Cartesian lattices. In this project, for the 2D case, images will be reconstructed from scanned data using the filtered back-projection method over a hexagonal lattice and in a regular hexagonal region. Then image segmentation methods such as graph-cuts on the reconstructed images are applied. Because a CT machine rotates to perform scans from different directions, a 2D object to be scanned may be assumed to be circular. Since the circular region can be embedded into a regular hexagon more tightly than into a square, fewer number of lattice points may be involved and the lattice points can be efficiently indexed for image segmentation. Hence the computational time for image segmentation may be reduced and the quality may be improved. Computer simulations will be done to evaluate the new algorithms in terms of image segmentation quality and computational efficiency.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.

催化剂项目为历史上的黑人学院和大学提供支持，努力建立教师的研究能力，以加强科学，技术，工程和数学本科教育和研究。预计该奖项将进一步提高教师的研究能力，改善研究和教学机构，并参与本科生的研究经验。在Vorhees学院的这个项目旨在开发计算机断层扫描（CT）图像重建和分割方法，使用最佳采样网格，并为本科生提供了一个机会，通过计算机建模的研究经验，以提高他们的教育。研究人员与南卡罗来纳州大学的教师建立了密切的合作关系。计算机断层扫描是一种重要的工具，以创建一个物理身体的内部图像。图像重建是从扫描数据计算内部图像，图像分割可以帮助定位图像中的内部对象或其边界。通常的CT计算是在笛卡尔网格上进行的，其像素是正方形或立方体。然而，最佳采样格，如二维六边形和三维面心立方和体心立方格，提供了更有效的采样和更好的邻接关系比传统的笛卡尔格。在这个项目中，对于2D的情况下，图像将被重建从扫描数据使用过滤反投影方法在一个六边形晶格和一个规则的六边形区域。然后，图像分割方法，如图切割的重建图像上应用。因为CT机旋转以从不同方向执行扫描，所以可以假设要扫描的2D对象是圆形的。由于圆形区域可以比正方形更紧密地嵌入到正六边形中，因此可以涉及更少数量的格点，并且可以有效地索引格点以用于图像分割。因此，可以减少用于图像分割的计算时间并且可以提高质量。计算机模拟将在图像分割质量和计算效率方面对新算法进行评估。该奖项反映了NSF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。

项目成果

Some efficient algorithms for morphological operations on hexagonal lattices and regular hexagonal domains

六方格子和正六方域形态学运算的一些有效算法

• DOI: 10.1117/12.2579531
• 发表时间: 2020
• 期刊: Video Processing and Artificial Intelligence
• 作者: Zheng, Xiqiang

Computer simulations for the denoising effect of morphological reconstructions for CT images on hexagonal grids and regular hexagonal regions

六边形网格和正六边形区域CT图像形态重建去噪效果的计算机模拟

• DOI: 10.1117/12.2611798
• 发表时间: 2021
• 期刊: Video Processing and Artificial Intelligence
• 作者: Zheng, Xiqiang