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.

这篇论文提出了一种计算机断层扫描（CT）图像增强方法，采用低剂量 CT 图像重建和神经网络预训练技术，取得了与当前最先进方法相媲美的结果。

通过对预训练的用于高斯降噪的 UNet 进行微调，用于低剂量 CT 图像重建与图像增强的下游任务

Low-Dose CT Image Reconstruction by Fine-Tuning a UNet Pretrained for  Gaussian Denoising for the Downstream Task of Image Enhancement

Traditional model-based image reconstruction (MBIR) methods combine forward
and noise models with simple object priors. Recent application of deep learning
methods for image reconstruction provides a successful data-driven approach to
addressing the challenges when reconstructing images with undersampled
measurements or various types of noise. In this work, we propose a hybrid
supervised-unsupervised learning framework for X-ray computed tomography (CT)
image reconstruction. The proposed learning formulation leverages both sparsity
or unsupervised learning-based priors and neural network reconstructors to
simulate a fixed-point iteration process. Each proposed trained block consists
of a deterministic MBIR solver and a neural network. The information flows in
parallel through these two reconstructors and is then optimally combined.
Multiple such blocks are cascaded to form a reconstruction pipeline. We
demonstrate the efficacy of this learned hybrid model for low-dose CT image
reconstruction with limited training data, where we use the NIH AAPM Mayo
Clinic Low Dose CT Grand Challenge dataset for training and testing. In our
experiments, we study combinations of supervised deep network reconstructors
and MBIR solver with learned sparse representation-based priors or analytical
priors. Our results demonstrate the promising performance of the proposed
framework compared to recent low-dose CT reconstruction methods.

我们提出了一种混合的监督 - 无监督学习框架，用于 X 射线计算机断层扫描（CT）图像重构。该学习方法利用了稀疏或无监督学习为先验条件以及神经网络重构器来模拟迭代过程，通过级联多个这样的模块组成重构流水线。实验证明，与最近的低剂量 CT 重建方法相比，该框架表现出很高的性能。