Introduction: a brief overview of iterative algorithms in X-ray computed tomography

Area of Science:

• Medical Imaging
• Computational Science
• Algorithm Development

Background:

• Traditional iterative algorithms for X-ray computed tomography (CT) image reconstruction, such as Algebraic Reconstruction Technique (ART), Simultaneous Algebraic Reconstruction Technique (SART), and Ordered Subsets Simultaneous Algebraic Reconstruction Technique (OS-SART), face significant computational time limitations.
• The increasing demand for large-scale CT applications necessitates more efficient image reconstruction methods.

Purpose of the Study:

• To provide an overview of basic and advanced iterative algorithms for CT image reconstruction.
• To address the computational time bottleneck in traditional iterative methods.
• To explore the potential of Krylov subspace methods for accelerating CT image reconstruction.

Main Methods:

• Review of algebraic iterative algorithms including ART, SART, and OS-SART.
• Introduction of Krylov subspace based methods, specifically conjugate gradients (CG) and its variants.
• Discussion of implementation using high-performance computing tools for large-scale CT.

Main Results:

• Identified computational time as a major limitation of traditional iterative CT reconstruction algorithms.
• Demonstrated the applicability of Krylov subspace methods (e.g., CG) for solving linear systems in large-scale CT.
• Highlighted the potential of modern high-performance computing for implementing these advanced methods.

Conclusions:

• Krylov subspace methods offer a promising alternative to traditional iterative algorithms for CT image reconstruction due to their efficiency.
• Further development and international collaboration are crucial for creating next-generation X-ray CT image reconstruction software.
• High-performance computing is essential for realizing the full potential of advanced reconstruction techniques.