Characterization of the previous normal-dose CT scan induced nonlocal means regularization method for low-dose CT image reconstruction

TL;DR

This paper investigates the effectiveness and limitations of the ndiNLM regularization method for low-dose CT image reconstruction, especially when previous normal-dose images differ in structure from current low-dose images, using clinical lung nodule data.

Contribution

The study provides a detailed characterization of the ndiNLM regularization method's performance in scenarios with structural differences between prior and current CT images.

Findings

ndiNLM can improve low-dose CT reconstruction but may introduce false structures.

Performance varies depending on structural differences between previous and current images.

The method's limitations are characterized through clinical case simulations.

Abstract

Repeated computed tomography (CT) scans are required in some clinical applications such as image-guided radiotherapy and follow-up observations over a time period. To optimize the radiation dose utility, a normal-dose (or full-dose) CT scan is often first performed to set up reference, followed by a series of low-dose scans. Using the previous normal-dose scan to improve follow-up low-dose scans reconstruction has drawn great interests recently, such as the previous normal-dose induced nonlocal means (ndiNLM) regularization method. However, one major concern with this method is that whether it would introduce false structures or miss true structures when the previous normal-dose image and current low-dose image have different structures (e.g., a tumor could be present, grow, shrink or absent in either image). This study aims to investigate the performance of the ndiNLM regularization method in the above mentioned situations. A patient with lung nodule for biopsy was recruited to this study. A normal-dose scan was acquired to set up biopsy operation, followed by a few low-dose scans during needle intervention toward the nodule. We used different slices to mimic different possible cases wherein the previous normal-dose image and current low-dose image have different structures. The experimental results characterize performance of our ndiNLM regularization method.