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Binary tomography reconstruction from few projections with Total Variation regularization for bone microstructure

L Wang¹, B Sixou¹, S Rit¹

¹CREATIS, CNRS UMR 5220, Inserm U1044, INSA de Lyon, Universite de Lyon, France.

Journal of X-Ray Science and Technology

|April 1, 2016

Summary

This summary is machine-generated.

This study introduces a Total Variation regularization method for binary image reconstruction in X-ray CT imaging. Adding box convex constraints significantly improves reconstruction of complex structures from limited projections.

Keywords:

X-ray imaging discrete tomography inverse problems

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Area of Science:

Medical Imaging
Computer Vision
Image Reconstruction

Background:

Discrete tomography reconstructs discrete-valued images, crucial for applications like medical imaging.
Binary image reconstruction from limited X-ray CT projections is challenging but vital for reducing radiation dose.
Existing methods require further refinement for complex structures.

Purpose of the Study:

To develop and evaluate a Total Variation (TV) regularization approach for binary image reconstruction in X-ray CT.
To assess the impact of additional box convex constraints on reconstruction accuracy.
To compare performance across different noise levels and object complexities.

Main Methods:

Utilized Total Variation (TV) regularization for binary image reconstruction.
Employed the Alternating Direction Minimization Method (ADMM) for functional minimization.
Compared reconstruction results with and without box convex constraints.
Tested on simple disk images and complex bone cross-sections.

Main Results:

Both TV regularization methods performed comparably on simple disk images.
The addition of box convex constraints notably enhanced reconstruction quality for complex structures with fine details.
Reconstructions were evaluated across various noise levels.

Conclusions:

TV regularization is effective for binary image reconstruction in limited-projection X-ray CT.
Box convex constraints are beneficial for improving the reconstruction of intricate details in medical and material science applications.
The ADMM algorithm efficiently minimizes the regularization functional.