Motion Correction via Locally Linear Embedding for Helical Photon-counting CT

TL;DR

This paper presents a novel motion correction method for helical photon-counting CT using locally linear embedding, improving image quality and resolution by addressing patient motion artifacts and bad pixels.

Contribution

The authors extend their LLE-based motion correction technique to helical PCD-CT, incorporating an unreliable-volume mask and incremental optimization for enhanced accuracy and efficiency.

Findings

Significant reduction in motion estimation errors at volume ends

Improved image resolution revealing fine structures

Enhanced clinical image quality post-correction

Abstract

X-ray photon-counting detector (PCD) offers low noise, high resolution, and spectral characterization, representing a next generation of CT and enabling new biomedical applications. It is well known that involuntary patient motion may induce image artifacts with conventional CT scanning, and this problem becomes more serious with PCD due to its high detector pitch and extended scan time. Furthermore, PCD often comes with a substantial number of bad pixels, making analytic image reconstruction challenging and ruling out state-of-the-art motion correction methods that are based on analytical reconstruction. In this paper, we extend our previous locally linear embedding (LLE) cone-beam motion correction method to the helical scanning geometry, which is especially desirable given the high cost of large-area PCD. In addition to our adaption of LLE-based parametric searching to helical cone-beam photon-counting CT geometry, we introduce an unreliable-volume mask to improve the motion estimation accuracy and perform incremental updating on gradually refined sampling grids for optimization of both accuracy and efficiency. Our numerical results demonstrate that our method reduces the estimation errors near the two longitudinal ends of the reconstructed volume and overall image quality. The experimental results on clinical photon-counting scans of the patient extremities show significant resolution improvement after motion correction using our method, which reveals subtle fine structures previously hidden under motion blurring and artifacts.