Vector Quantized Convolutional Autoencoder for Low-Dose CT Image Reconstruction with Perceptual and Bias Reducing Loss

Abstract

In medical imaging, Computed Tomography (CT) is one of the most often utilized imaging modalities for diagnosing different disorders. Deep learning has become significant in the field of medical imaging, specifically investigated for low-dose CT. In recently available CT Scanners, Low Dose CT (LDCT) reconstruction is presented with a post-processing approach, which uses deep learning-based methods to reduce the noise level. Applying low radiation dosage can decrease damage to patients but the projected image is corrupted with noise due to lower intensity and fewer angle measurements, resulting in excessive noise in the reconstructed CT image. Thus, the presence of noise and artifacts in LDCT images limits their potential use. Here, a vector quantized convolutional encoder network is proposed for the image reconstruction task. The network is trained using the LoDoPaB-CT Dataset and tested on chest CT images. The qualitative and quantitative results produced better quality results when compared with the recent state-of-art deep learning-based methods. The quality of the results is optimized with perceptual and Bias-Reducing loss functions.