Randomized Iterative Reconstruction for Sparse View X-ray Computed Tomography
This incremental improvement addresses efficiency in medical imaging reconstruction for practitioners, potentially reducing radiation exposure or scan time.
The paper tackles the problem of reducing the number of projection angles needed for X-ray computed tomography by combining analytical reconstruction with a randomized iterative algorithm, achieving up to 35% fewer angles while maintaining reconstruction quality and execution time.
With the availability of more powerful computers, iterative reconstruction algorithms are the subject of an ongoing work in the design of more efficient reconstruction algorithms for X-ray computed tomography. In this work, we show how two analytical reconstruction algorithms can be improved by correcting the corresponding reconstructions using a randomized iterative reconstruction algorithm. The combined analytical reconstruction followed by randomized iterative reconstruction can also be viewed as a reconstruction algorithm which, in the experiments we have conducted, uses up to $35\%$ less projection angles as compared to the analytical reconstruction algorithms and produces the same results in terms of quality of reconstruction, without increasing the execution time significantly.