论文标题
AHP网络：基于自适应的HYPER参数深度学习的图像重建方法多级低剂量CT
AHP-Net: adaptive-hyper-parameter deep learning based image reconstruction method for multilevel low-dose CT
论文作者
论文摘要
在许多临床应用中，低剂量CT（LDCT）成像是可取的，以减少患者的X射线辐射剂量。受深度学习（DL）的启发，基于模型的迭代重建（MBIR）方法的最新有前途的方向是通过优化 - 未滚动的DL调节图像重建，其中预定的图像先验被可学习的数据自适应先验代替。但是，LDCT在临床上是多级别的，因为临床扫描具有不同的噪声水平，这些噪声水平取决于扫描部位，患者大小和临床任务。因此，这项工作旨在开发基于自适应的HYPER参数基于DL的图像重建方法（AHP-NET），该方法可以处理不同噪声水平的多级LDCT。 AHP-NET展开一个半季度分裂方案，具有可学习的图像先验构建在Framelet Filter Bank上的可学习图像，并学习一个网络，该网络自动调整了各种噪声水平的超参数。结果，AHP网络提供了一个可以处理多级LDCT的单一通用训练模型。使用临床扫描进行了广泛的实验评估表明，AHP网络的表现优于常规MBIR技术和最先进的基于深度学习的方法，用于不同噪声水平的多级LDCT。
Low-dose CT (LDCT) imaging is desirable in many clinical applications to reduce X-ray radiation dose to patients. Inspired by deep learning (DL), a recent promising direction of model-based iterative reconstruction (MBIR) methods for LDCT is via optimization-unrolling DL-regularized image reconstruction, where pre-defined image prior is replaced by learnable data-adaptive prior. However, LDCT is clinically multilevel, since clinical scans have different noise levels that depend of scanning site, patient size, and clinical task. Therefore, this work aims to develop an adaptive-hyper-parameter DL-based image reconstruction method (AHP-Net) that can handle multilevel LDCT of different noise levels. AHP-Net unrolls a half-quadratic splitting scheme with learnable image prior built on framelet filter bank, and learns a network that automatically adjusts the hyper-parameters for various noise levels. As a result, AHP-Net provides a single universal training model that can handle multilevel LDCT. Extensive experimental evaluations using clinical scans suggest that AHP-Net outperformed conventional MBIR techniques and state-of-the-art deep-learning-based methods for multilevel LDCT of different noise levels.
