Low-dose X-ray computed tomography image reconstruction with a combined low-mAs and sparse-view protocol

Area of Science:

• Medical Imaging
• Computational Imaging
• Image Reconstruction

Background:

• Low-dose X-ray computed tomography (CT) is crucial for reducing patient radiation exposure.
• Common low-dose strategies include reducing milliampere-seconds (mAs) and using fewer projection views (sparse-view).
• Reconstructing high-quality images from limited data remains a significant challenge.

Purpose of the Study:

• To develop and evaluate a novel two-step image reconstruction strategy for low-dose CT.
• To address the challenges of noise and insufficient data in sinograms acquired with combined low-mAs and sparse-view protocols.
• To improve image quality metrics such as noise reduction, contrast-to-noise ratio, and edge preservation.

Main Methods:

• A two-step reconstruction strategy termed ASR-TV-POCS was proposed.
• The first step involves an adaptive sinogram restoration (ASR) method, considering the statistical properties of sinogram data.
• The second step utilizes a modified total variation based projection onto convex sets (TV-POCS) method.

Main Results:

• The ASR-TV-POCS method demonstrated significant noise reduction in low-dose CT images.
• Quantitative and qualitative evaluations showed improved contrast-to-noise ratio (CNR) compared to existing methods.
• Enhanced preservation of edge details was observed, indicating superior image fidelity.

Conclusions:

• The proposed ASR-TV-POCS method offers a promising solution for low-dose CT image reconstruction.
• This strategy effectively suppresses noise and preserves important image features from challenging low-dose acquisitions.
• The findings suggest ASR-TV-POCS can advance the clinical application of low-dose CT imaging.