.png){kind=icon}
返回
CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction
.png){kind=icon}
OA
AI
DOI：10.1088/1361-6560/aadac1.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In tomographic medical imaging (PET, SPECT, CT), differences in data acquisition and organization are a major hurdle for the development of tomographic reconstruction software. The implementation of a given reconstruction algorithm is usually limited to a specific set of conditions, depending on the modality, the purpose of the study, the input data, or on the characteristics of the reconstruction algorithm itself. It causes restricted or limited use of algorithms, differences in implementation, code duplication, impractical code development, and difficulties for comparing different methods. This work attempts to address these issues by proposing a unified and generic code framework for formatting, processing and reconstructing acquired multi-modal and multidimensional data. The proposed iterative framework processes in the same way elements from list-mode (i.e. events) and histogrammed (i.e. sinogram or other bins) data sets. Each element is processed separately, which opens the way for highly parallel execution. A unique iterative algorithm engine makes use of generic core components corresponding to the main parts of the reconstruction process. Features that are specific to different modalities and algorithms are embedded into specific components inheriting from the generic abstract components. Temporal dimensions are taken into account in the core architecture. The framework is implemented in an open-source C++ parallel platform, called CASToR (customizable and advanced software for tomographic reconstruction). Performance assessments show that the time loss due to genericity remains acceptable, being one order of magnitude slower compared to a manufacturer's software optimized for computational efficiency for a given system geometry. Specific optimizations were made possible by the underlying data set organization and processing and allowed for an average speed-up factor ranging from 1.54 to 3.07 when compared to more conventional implementations. Using parallel programming, an almost linear speedup increase (factor of 0.85 times number of cores) was obtained in a realistic clinical PET setting. In conclusion, the proposed framework offers a substantial flexibility for the integration of new reconstruction algorithms while maintaining computation efficiency.
Keyword:
PET
SPECT
CT
tomographic
image reconstruction
software
multimodal
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.4
论文数:
1.4W
被引数:
3.1W
机构
引用论文
Monte Carlo study of the effects of system geometry and antiscatter grids on cone-beam CT scatter distributions蒙特卡罗研究系统几何形状和反散射网格对锥束CT散射分布的影响.png)
MEDICAL PHYSICS
OAAI
MEDICAL PHYSICSIF3.2
OAAI分享收藏A Fluorometric Assay for DNA Cleavage Reactions Characterized with BamHI Restriction EndonucleaseOAAI
OAAI分享收藏
查看更多内容