MVCT image enhancement using reference-based encoder–decoder convolutional neural network

TL;DR: This study proposes a reference-based encoder-decoder CNN (RefED-CNN) to enhance noisy MVCT images using KVCT images as auxiliary references, achieving superior denoising and structural detail preservation compared to other methods on phantom and patient data.

Abstract: Daily MVCT (Megavoltage Computed Tomography) in TomoTherapy plays a crucial role in patients’ setup and dose reconstruction. However, MVCT images suffer from high noise and low tissue contrast due to the limited number of X-ray photons and low detector quantum efficiency (DQE). In this study, we propose an approach to enhance MVCT images using the KVCT (Kilovoltage Computed Tomography) image of the same patient as an auxiliary reference image. Specifically, we propose a reference-based encoder–decoder convolutional neural network (RefED-CNN) by incorporating a feature extraction and alignment (FEA) module to introduce the features of the reference image as side information into the MVCT image enhancement process. The FEA module automatically searches and aligns relevant features between the reference image and the noisy image, and transfers the relevant texture from reference KVCT images to the target MVCT image. Evaluations conducted on both phantom and real patient data show that our method outperforms other denoising methods by effectively reducing noise and preserving intricate structural details.