A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection...

Area of Science:

• Medical Imaging
• Biomedical Engineering
• Computational Imaging

Background:

• Synchrotron X-ray Micro Computed Tomography (Micro-CT) is vital for non-invasive in vivo preclinical imaging.
• High radiation doses and long scan times limit its application due to the large number of projections required for high-quality image reconstruction.

Purpose of the Study:

• To develop a novel reconstruction algorithm for Synchrotron Micro-CT.
• To reduce radiation dose and scan time without compromising image quality in large-scale, reduced-view imaging.

Main Methods:

• Combined gradient-based Douglas-Rachford splitting with discrete wavelet packet shrinkage for image denoising.
• Reconstructed images from reduced projection sets were compared against high-dose reference images.
• Evaluated performance using a synthetic head phantom and a femoral cortical bone sample.

Main Results:

• The proposed algorithm demonstrated superior performance compared to existing methods.
• Achieved acceptable quality metrics with a reduced number of projections.
• Validated on a synthetic phantom and a real bone sample at the Canadian Light Source.

Conclusions:

• The developed algorithm effectively reduces radiation dose and scan time in Synchrotron Micro-CT.
• This advancement improves in vivo imaging protocols by enabling high-quality reconstructions from fewer projections.