CT-image Super Resolution Using 3D Convolutional Neural Network

TL;DR

This paper introduces a novel 3D super resolution CNN for CT images, improving resolution efficiently and effectively, with a single model capable of multi-scale super resolution, outperforming traditional methods in quality and speed.

Contribution

The paper presents a new 3D CNN architecture for CT image super resolution that handles multiple scales with one model and optimizes training with advanced strategies.

Findings

Outperforms conventional methods in PSNR and SSIM

Achieves better efficiency and resolution enhancement

Single model handles multiple scale factors

Abstract

Computed Tomography (CT) imaging technique is widely used in geological exploration, medical diagnosis and other fields. In practice, however, the resolution of CT image is usually limited by scanning devices and great expense. Super resolution (SR) methods based on deep learning have achieved surprising performance in two-dimensional (2D) images. Unfortunately, there are few effective SR algorithms for three-dimensional (3D) images. In this paper, we proposed a novel network named as three-dimensional super resolution convolutional neural network (3DSRCNN) to realize voxel super resolution for CT images. To solve the practical problems in training process such as slow convergence of network training, insufficient memory, etc., we utilized adjustable learning rate, residual-learning, gradient clipping, momentum stochastic gradient descent (SGD) strategies to optimize training procedure. In addition, we have explored the empirical guidelines to set appropriate number of layers of network and how to use residual learning strategy. Additionally, previous learning-based algorithms need to separately train for different scale factors for reconstruction, yet our single model can complete the multi-scale SR. At last, our method has better performance in terms of PSNR, SSIM and efficiency compared with conventional methods.