Grangeat-type helical half-scan computerized tomography algorithm for reconstruction of a short object

Area of Science:

• Medical Physics
• Biomedical Imaging
• Image Reconstruction

Background:

• Cone-beam CT (CBCT) and micro-CT scanners are rapidly advancing for biomedical use.
• Half-scan algorithms improve temporal resolution and reduce artifacts but existing methods are limited.
• Previous work established a Grangeat framework for circular trajectory half-scan CBCT.

Purpose of the Study:

• To extend the Grangeat framework for helical half-scan CBCT reconstruction without data truncation.
• To develop a novel algorithm addressing data redundancy and inconsistency in helical scanning.
• To validate the algorithm's performance using the Shepp-Logan phantom.

Main Methods:

• Modification of Grangeat's formula for Radon data utilization and estimation.
• Categorization of Radon space into singly, doubly, triply sampled, and shadow regions.
• Development of a smooth weighting strategy and introduction of projected trajectories and transition points for helical scanning.

Main Results:

• A novel Grangeat-type helical half-scan algorithm was formulated and implemented.
• The algorithm effectively compensates for data redundancy and inconsistency.
• Phantom studies verified the algorithm's correctness and demonstrated its advantages over existing methods.

Conclusions:

• The developed Grangeat-type helical half-scan algorithm is suitable for quantitative and dynamic CBCT applications.
• This method offers improved image quality and artifact reduction in biomedical imaging.
• The algorithm serves as a foundation for addressing the long object problem in CT.