Robust regression-based estimation of isocenter offset with subpixel precision in tomographic image reconstruction.

Area of Science:

• Medical Imaging
• Image Reconstruction
• Geometric Calibration

Background:

• Optimal tomographic image reconstruction relies on precise geometric measurements and system calibration.
• The isocenter offset is a critical geometric parameter influencing reconstructed image spatial resolution.
• System imperfections like mechanical misalignment hinder accurate isocenter offset achievement, and current methods lack subpixel precision.

Purpose of the Study:

• To propose a purely data-driven method for precise, subpixel level estimation of the isocenter offset.
• To overcome limitations of traditional calibration procedures in achieving high-precision isocenter offset tuning.

Main Methods:

• Utilized the Fourier shift theorem for indirect isocenter offset estimation.
• Applied a generalized M-estimator, a robust regression algorithm, to sinogram data from axial scanning geometry.
• Validated the method using simulated phantom data and actual data from a tungsten wire scan.

Main Results:

• The proposed data-driven method accurately estimates and tunes the isocenter offset at the subpixel level.
• Numerical experiments demonstrated high accuracy in isocenter offset estimation.
• The improved isocenter offset significantly enhances the quality of reconstructed tomographic images, especially spatial resolution.

Conclusions:

• The developed Fourier shift theorem-based method offers a precise and robust solution for isocenter offset calibration.
• This data-driven approach effectively addresses the limitations of conventional calibration techniques.
• Accurate isocenter offset estimation is crucial for improving spatial resolution and overall image quality in tomographic reconstruction.