University of Michigan - Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai, 200240, China.
Department of Computational Mathematics, Science and Engineering, Department of Biomedical Engineering, Michigan State University, East Lansing, MI, 48824, USA.
Med Phys. 2021 Oct;48(10):6388-6400. doi: 10.1002/mp.15013. Epub 2021 Sep 13.
Signal models based on sparse representations have received considerable attention in recent years. On the other hand, deep models consisting of a cascade of functional layers, commonly known as deep neural networks, have been highly successful for the task of object classification and have been recently introduced to image reconstruction. In this work, we develop a new image reconstruction approach based on a novel multilayer model learned in an unsupervised manner by combining both sparse representations and deep models. The proposed framework extends the classical sparsifying transform model for images to a Multilayer residual sparsifying transform (MARS) model, wherein the transform domain data are jointly sparsified over layers. We investigate the application of MARS models learned from limited regular-dose images for low-dose CT reconstruction using penalized weighted least squares (PWLS) optimization.
We propose new formulations for multilayer transform learning and image reconstruction. We derive an efficient block coordinate descent algorithm to learn the transforms across layers, in an unsupervised manner from limited regular-dose images. The learned model is then incorporated into the low-dose image reconstruction phase.
Low-dose CT experimental results with both the XCAT phantom and Mayo Clinic data show that the MARS model outperforms conventional methods such as filtered back-projection and PWLS methods based on the edge-preserving (EP) regularizer in terms of two numerical metrics (RMSE and SSIM) and noise suppression. Compared with the single-layer learned transform (ST) model, the MARS model performs better in maintaining some subtle details.
This work presents a novel data-driven regularization framework for CT image reconstruction that exploits learned multilayer or cascaded residual sparsifying transforms. The image model is learned in an unsupervised manner from limited images. Our experimental results demonstrate the promising performance of the proposed multilayer scheme over single-layer learned sparsifying transforms. Learned MARS models also offer better image quality than typical nonadaptive PWLS methods.
基于稀疏表示的信号模型近年来受到了广泛关注。另一方面，由功能层级联组成的深度模型，通常称为深度神经网络，在物体分类任务中取得了巨大成功，并已被引入到图像重建中。在这项工作中，我们开发了一种新的图像重建方法，该方法基于一种新的多层模型，该模型通过结合稀疏表示和深度模型，以无监督的方式学习。所提出的框架将经典的图像稀疏变换模型扩展到多层残差稀疏变换（MARS）模型，其中变换域数据在层间进行联合稀疏化。我们研究了从有限的常规剂量图像学习的 MARS 模型在使用惩罚加权最小二乘（PWLS）优化的低剂量 CT 重建中的应用。
我们提出了用于多层变换学习和图像重建的新公式。我们提出了一种有效的块坐标下降算法，用于从有限的常规剂量图像中以无监督的方式跨层学习变换。然后将所学习的模型纳入低剂量图像重建阶段。
使用 XCAT 体模和 Mayo 诊所数据的低剂量 CT 实验结果表明，与基于边缘保持（EP）正则化的滤波反投影和 PWLS 方法等传统方法相比，MARS 模型在两个数值指标（RMSE 和 SSIM）和噪声抑制方面具有更好的性能。与单层学习变换（ST）模型相比，MARS 模型在保持一些细微细节方面表现更好。
这项工作提出了一种新的基于数据驱动的 CT 图像重建正则化框架，该框架利用学习的多层或级联残差稀疏变换。图像模型是从有限的图像中以无监督的方式学习的。我们的实验结果表明，所提出的多层方案优于单层学习的稀疏变换。学习的 MARS 模型也提供了比典型的非自适应 PWLS 方法更好的图像质量。
