Sparse-view image reconstruction via total absolute curvature combining total variation for X-ray computed tomography

Area of Science:

• Medical Imaging
• Computational Imaging
• Image Reconstruction

Background:

• Sparse-view X-ray computed tomography (CT) offers faster scanning and lower radiation doses.
• Conventional total variation (TV) methods use first-order image information, often leading to suboptimal reconstructions.
• Image curvature, a second-order feature, can enhance image reconstruction quality.

Purpose of the Study:

• To develop a novel image reconstruction method incorporating image curvature for sparse-view CT.
• To improve the description of complex image features in sparse-view CT reconstruction.
• To enhance the quality of reconstructed images in low-dose and fast scanning CT.

Main Methods:

• Proposed a new total absolute curvature and total variation (TAC-TV) optimization model.
• Utilized the alternating direction method of multipliers (ADMM) for algorithm development.
• Implemented TAC-TV iterations with FFTs, soft-thresholding, and projection operations, suitable for GPU acceleration.

Main Results:

• The TAC-TV method demonstrated superior reconstruction quality compared to traditional TV-based methods.
• Qualitative and quantitative evaluations on sparse-view datasets confirmed the algorithm's effectiveness.
• The proposed approach showed a satisfactory convergence property.

Conclusions:

• The TAC-TV method effectively improves image reconstruction quality in sparse-view CT.
• Incorporating second-order image features like curvature enhances the description of complex image details.
• The ADMM-based algorithm provides an efficient and practical solution for sparse-view CT reconstruction.