Improved 2-D Chest CT Image Enhancement With Multi-Level VGG Loss

Abstract

Chest CT scans play an important role in diagnosing abnormalities associated with the lungs, such as tuberculosis, sarcoidosis, pneumonia, and, more recently, COVID-19. However, because conventional normal-dose chest CT scans require a much larger amount of radiation than x-rays, practitioners seek to replace conventional CT with low-dose CT (LDCT). LDCT often generates a low-quality CT image that poses noise and, in turn, negatively affects the accuracy of diagnosis. Therefore, in the context of COVID-19, due to the large number of affected populations, efficient image-denoising techniques are needed for LDCT images. Here, we present a deep learning (DL) model that combines two neural networks to enhance the quality of low-dose chest CT images. The DL model leverages a previously developed densenet and deconvolution-based network (DDNet) for feature extraction and extends it with a pretrained VGG network inside the loss function to suppress noise. Outputs from selected multiple levels in the VGG network (ML-VGG) are leveraged for the loss calculation. We tested our DDNet with ML-VGG loss using several sources of CT images and compared its performance to DDNet without VGG loss as well as DDNet with an empirically selected single-level VGG loss (DDNet-SL-VGG) and other state-of-the-art DL models. Our results show that DDNet combined with ML-VGG (DDNet-ML-VGG) achieves state-of-the-art denoising capabilities and improves the perceptual and quantitative image quality of chest CT images. Thus, DDNet with multilevel VGG loss could potentially be used as a post-acquisition image enhancement tool for medical professionals to diagnose and monitor chest diseases with higher accuracy.