Implementation of the computer tomography parallel algorithms with the incomplete set of data

Area of Science:

• Medical Imaging
• Computer Science
• Applied Mathematics

Background:

• Computer tomography (CT) applications often require complete and high-quality scan data.
• Difficult access to examined objects can lead to incomplete datasets in CT.
• Previous research indicated CT algorithms can handle incomplete data, but reconstruction time is a significant issue.

Purpose of the Study:

• To examine the usefulness of a proposed parallel-block approach for CT reconstruction with incomplete data.
• To investigate methods for reducing the reconstruction time in such scenarios.
• To validate the theoretical parallel-block approach through practical implementation.

Main Methods:

• Dividing the system of linear equations into blocks for parallel processing by different threads.
• Examining the parallel-block approach for CT reconstruction with incomplete datasets.
• Investigating the selection of optimal reconstruction parameters and controlling maximum error.

Main Results:

• The parallel-block approach is effective for incomplete data in CT.
• Optimal reconstruction parameters can be selected, and a desired maximum error achieved.
• The real implementation of the parallel algorithm demonstrates convergence, confirming its usefulness.

Conclusions:

• The parallel-block approach offers a practical solution for CT reconstruction challenges posed by incomplete data.
• This method successfully reduces reconstruction time while maintaining accuracy.
• The study confirms the convergence and utility of parallel computing in advanced CT applications.