  Cone-beam computed tomography (CBCT) is routinely collected during
image-guided radiation therapy (IGRT) to provide updated patient anatomy
information for cancer treatments. However, CBCT images often suffer from
streaking artifacts and noise caused by under-rate sampling projections and
low-dose exposure, resulting in low clarity and information loss. While recent
deep learning-based CBCT enhancement methods have shown promising results in
suppressing artifacts, they have limited performance on preserving anatomical
details since conventional pixel-to-pixel loss functions are incapable of
describing detailed anatomy. To address this issue, we propose a novel
feature-oriented deep learning framework that translates low-quality CBCT
images into high-quality CT-like imaging via a multi-task customized
feature-to-feature perceptual loss function. The framework comprises two main
components: a multi-task learning feature-selection network(MTFS-Net) for
customizing the perceptual loss function; and a CBCT-to-CT translation network
guided by feature-to-feature perceptual loss, which uses advanced generative
models such as U-Net, GAN and CycleGAN. Our experiments showed that the
proposed framework can generate synthesized CT (sCT) images for the lung that
achieved a high similarity to CT images, with an average SSIM index of 0.9869
and an average PSNR index of 39.9621. The sCT images also achieved visually
pleasing performance with effective artifacts suppression, noise reduction, and
distinctive anatomical details preservation. Our experiment results indicate
that the proposed framework outperforms the state-of-the-art models for
pulmonary CBCT enhancement. This framework holds great promise for generating
high-quality anatomical imaging from CBCT that is suitable for various clinical
applications.
