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。

融合监督和无监督学习提升低剂量 CT 图像重构

Enhancing Low-dose CT Image Reconstruction by Integrating Supervised and  Unsupervised Learning

We introduce a model-based image reconstruction framework with a convolution
neural network (CNN) based regularization prior. The proposed formulation
provides a systematic approach for deriving deep architectures for inverse
problems with the arbitrary structure. Since the forward model is explicitly
accounted for, a smaller network with fewer parameters is sufficient to capture
the image information compared to black-box deep learning approaches, thus
reducing the demand for training data and training time. Since we rely on
end-to-end training, the CNN weights are customized to the forward model, thus
offering improved performance over approaches that rely on pre-trained
denoisers. The main difference of the framework from existing end-to-end
training strategies is the sharing of the network weights across iterations and
channels. Our experiments show that the decoupling of the number of iterations
from the network complexity offered by this approach provides benefits
including lower demand for training data, reduced risk of overfitting, and
implementations with significantly reduced memory footprint. We propose to
enforce data-consistency by using numerical optimization blocks such as
conjugate gradients algorithm within the network; this approach offers faster
convergence per iteration, compared to methods that rely on proximal gradients
steps to enforce data consistency. Our experiments show that the faster
convergence translates to improved performance, especially when the available
GPU memory restricts the number of iterations.

该研究提出了一种基于卷积神经网络的模型重建框架，实现了解决逆问题的途径。该框架克服了深度学习方法中过多参数所占用的训练数据和时间上的需求问题，并在重构图像方面取得了显著成果。