Bilateral Weighted Relative Total Variation for Low-Dose CT Reconstruction

Area of Science:

• Medical Imaging
• Image Processing
• Radiology

Background:

• Low-dose computed tomography (LDCT) reduces patient X-ray dose but suffers from significant image noise.
• Degraded image quality in LDCT hinders clinical applications and diagnostic accuracy.

Purpose of the Study:

• To develop an advanced image reconstruction model for LDCT.
• To enhance image quality by reducing noise while preserving structural details.

Main Methods:

• Proposed the bilateral weighted relative total variation (BRTV) for noise reduction and edge preservation.
• Introduced the BRTV-regularized projections onto convex sets (POCS-BRTV) model for LDCT reconstruction.
• Utilized spatial closeness and gray value similarity for adaptive detail extraction.

Main Results:

• The POCS-BRTV model demonstrated superior performance compared to existing algorithms.
• Experimental results showed significant improvements in noise reduction.
• The model effectively preserved edges, structures, and textures in LDCT images.

Conclusions:

• The POCS-BRTV model offers a promising solution for improving LDCT image quality.
• This method enhances diagnostic capabilities by providing clearer and more detailed images.
• The approach balances noise suppression with the preservation of critical anatomical information.