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

Computed tomography (CT) is a beneficial imaging tool for diagnostic
purposes. CT scans provide detailed information concerning the internal
anatomic structures of a patient, but present higher radiation dose and costs
compared to X-ray imaging. In this paper, we build on previous research to
convert orthogonal X-ray images into simulated CT volumes by exploring larger
datasets and various model structures. Significant model variations include
UNet architectures, custom connections, activation functions, loss functions,
optimizers, and a novel back projection approach.

该研究论文通过使用更大的数据集和不同的模型结构，探索将正交 X 射线图像转换成模拟 CT 体积的方法，以解决 CT 扫描在辐射剂量和成本方面相对于 X 射线成像的问题。关键模型变化包括 UNet 架构、自定义连接、激活函数、损失函数、优化器和一种新颖的反投影方法。