基于搜索空间扩展和全变分的层析重建与正则化
Tomographic Reconstruction and Regularisation with Search Space Expansion and Total Variation
AI总结：
本文针对医学成像中不完整数据重建问题，提出结合搜索空间扩展和全变分正则化的群智能优化方法，在Shepp-Logan体模上实现了比标准算法和领先优化器更低的重建误差。
摘要：
利用射线投影重建图像是医学成像中的常用技术。当患者易受潜在辐射伤害或无法承受较长扫描时间时，处理不完整数据尤为重要。本文将该问题重新表述为优化任务，随后采用基于群智能的重建方法处理高度欠采样数据，粒子在图像空间中移动以最小化重建误差。该过程易受噪声影响，除近期引入的搜索空间扩展技术外，本文还引入并研究了全变分正则化这一平滑过程。结果表明，在临床重要的Shepp-Logan体模上，与标准层析重建工具箱算法及一种领先的高维优化器相比，所提方法具有更低的重建误差。
英文摘要：
The use of ray projections to reconstruct images is a common technique in medical imaging. Dealing with incomplete data is particularly important when a patient is vulnerable to potentially damaging radiation or is unable to cope with the long scanning time. This paper utilises the reformulation of the problem into an optimisation tasks, followed by using a swarm-based reconstruction from highly undersampled data where particles move in image space in an attempt to minimise the reconstruction error. The process is prone to noise and, in addition to the recently introduced search space expansion technique, a further smoothing process, total variation regularisation, is adapted and investigated. The proposed method is shown to produce lower reproduction errors compared to standard tomographic reconstruction toolbox algorithms as well as one of the leading high-dimensional optimisers on the clinically important Shepp-Logan phantom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
