Combining Acceleration Techniques for Low-Dose X-Ray Cone Beam Computed Tomography Image Reconstruction.

Area of Science:

• Medical Imaging
• Computational Imaging
• Image Reconstruction

Background:

• Low-dose computed tomography (CT) image quality has improved with compressive sensing (CS) methods.
• Existing CS reconstruction algorithms face challenges with high computational cost and slow convergence.

Purpose of the Study:

• To propose a novel, fast reconstruction framework for low-dose CT.
• To integrate multiple speed-up techniques into a CS-based reconstruction algorithm.

Main Methods:

• Implemented total difference minimization (TDM) with soft-threshold filtering (STF).
• Combined TDM-STF with ordered subsets transmission (OSTR) for accelerated convergence.
• Applied power factor and fast iterative shrinkage thresholding algorithm (FISTA) for further speed-up.

Main Results:

• Simulation and phantom studies demonstrated significant improvements in convergence speed.
• The proposed framework achieved substantial acceleration with only a minor increase in computation time (≤10%).

Conclusions:

• A novel CS-based reconstruction framework integrating several acceleration techniques was presented.
• The method shows potential for satisfying fast image reconstruction requirements in practical CT applications.