Application of Gated Recurrent Units for CT Trajectory Optimization

TL;DR

This paper introduces a novel method using Gated Recurrent Units to optimize CT scan trajectories, improving image quality and efficiency in dual-robot systems by selecting projections that enhance resolution and contrast.

Contribution

The paper presents a new GRU-based approach for CT trajectory optimization that outperforms traditional methods in image quality metrics and scan efficiency.

Findings

SSIM improved from 0.38 to 0.49

CNR increased from 6.97 to 9.08

Outperforms traditional circular trajectories

Abstract

Recent advances in computed tomography (CT) imaging, especially with dual-robot systems, have introduced new challenges for scan trajectory optimization. This paper presents a novel approach using Gated Recurrent Units (GRUs) to optimize CT scan trajectories. Our approach exploits the flexibility of robotic CT systems to select projections that enhance image quality by improving resolution and contrast while reducing scan time. We focus on cone-beam CT and employ several projection-based metrics, including absorption, pixel intensities, contrast-to-noise ratio, and data completeness. The GRU network aims to minimize data redundancy and maximize completeness with a limited number of projections. We validate our method using simulated data of a test specimen, focusing on a specific voxel of interest. The results show that the GRU-optimized scan trajectories can outperform traditional circular CT trajectories in terms of image quality metrics. For the used specimen, SSIM improves from 0.38 to 0.49 and CNR increases from 6.97 to 9.08. This finding suggests that the application of GRU in CT scan trajectory optimization can lead to more efficient, cost-effective, and high-quality imaging solutions.