Low-Dose CT Image Reconstruction by Fine-Tuning a UNet Pretrained for
  Gaussian Denoising for the Downstream Task of Image Enhancement
Tim Selig Thomas März Martin Storath Andreas Weinmann
Computed Tomography (CT) is a widely used medical imaging modality, and as it
is based on ionizing radiation, it is desirable to minimize the radiation dose.
However, a reduced radiation dose comes with reduced image quality, and
reconstruction from low-dose CT (LDCT) data is still a challenging task which
is subject to research. According to the LoDoPaB-CT benchmark, a benchmark for
LDCT reconstruction, many state-of-the-art methods use pipelines involving
UNet-type architectures. Specifically the top ranking method, ItNet, employs a
three-stage process involving filtered backprojection (FBP), a UNet trained on
CT data, and an iterative refinement step. In this paper, we propose a less
complex two-stage method. The first stage also employs FBP, while the novelty
lies in the training strategy for the second stage, characterized as the CT
image enhancement stage. The crucial point of our approach is that the neural
network is pretrained on a distinctly different pretraining task with non-CT
data, namely Gaussian noise removal on a variety of natural grayscale images
(photographs). We then fine-tune this network for the downstream task of CT
image enhancement using pairs of LDCT images and corresponding normal-dose CT
images (NDCT). Despite being notably simpler than the state-of-the-art, as the
pretraining did not depend on domain-specific CT data and no further iterative
refinement step was necessary, the proposed two-stage method achieves
competitive results. The proposed method achieves a shared top ranking in the
LoDoPaB-CT challenge and a first position with respect to the SSIM metric.