Tomographic Reconstruction and Regularisation with Search Space Expansion and Total Variation
This work addresses incomplete data reconstruction in medical imaging for vulnerable patients, but it is incremental as it builds on existing techniques like search space expansion and total variation regularization.
The paper tackles tomographic image reconstruction from highly undersampled data, a problem in medical imaging to reduce radiation exposure and scanning time, by proposing a swarm-based optimization method with search space expansion and total variation regularization, resulting in lower reproduction errors compared to standard toolbox algorithms and a leading high-dimensional optimizer on the Shepp-Logan phantom.
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.