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.

这篇论文利用光线投影技术在医学影像中进行图像重建，通过将问题转化为优化任务并利用基于群体的重建方法从高度欠采样的数据中恢复图像。为了减少重建误差，该方法采用了搜索空间扩展和全变差正则化技术，在临床重要的 Shepp-Logan 幻像模型中显示出比传统断层重建算法和一种领先的高维优化器更低的重建误差。