Low dose CT reconstruction assisted by an image manifold prior

TL;DR

This paper introduces a deep learning-based manifold prior to improve low-dose CT image reconstruction, significantly reducing noise and artifacts while maintaining high image quality, thus lowering radiation exposure risks.

Contribution

It proposes a novel data-driven manifold learning approach using a CNN to enhance low-dose CT reconstruction, outperforming traditional methods in image quality and error reduction.

Findings

Achieves average reconstruction error of < 30 HU, outperforming FBP and TV methods.

Restores high-quality CT images with ~20 HU error.

Demonstrates effectiveness on patient abdomen CT images.

Abstract

X-ray Computed Tomography (CT) is an important tool in medical imaging to obtain a direct visualization of patient anatomy. However, the x-ray radiation exposure leads to the concern of lifetime cancer risk. Low-dose CT scan can reduce the radiation exposure to patient while the image quality is usually degraded due to the appearance of noise and artifacts. Numerous studies have been conducted to regularize CT image for better image quality. Yet, exploring the underlying manifold where real CT images residing on is still an open problem. In this paper, we propose a fully data-driven manifold learning approach by incorporating the emerging deep-learning technology. An encoder-decoder convolutional neural network has been established to map a CT image to the inherent low-dimensional manifold, as well as to restore the CT image from its corresponding manifold representation. A novel reconstruction algorithm assisted by the leant manifold prior has been developed to achieve high quality low-dose CT reconstruction. In order to demonstrate the effectiveness of the proposed framework, network training, testing, and comprehensive simulation study have been performed using patient abdomen CT images. The trained encoder-decoder CNN is capable of restoring high-quality CT images with average error of ~20 HU. Furthermore, the proposed manifold prior assisted reconstruction scheme achieves high-quality low-dose CT reconstruction, with average reconstruction error of < 30 HU, more than five times and two times lower than that of filtered back projection method and total-variation based iterative reconstruction method, respectively.