标题： 基于深度学习的字典学习和断层图像重建 显示英文标题

标题： Deep learning based dictionary learning and tomographic image reconstruction

摘要： 本研究提出了一种结合稀疏信号处理原理与深度学习思想的临床低剂量断层扫描图像重建方法。 首先，我们从统计学的角度描述了稀疏信号表示的字典表示法，并将字典学习解释为对齐生成模型产生的分布与真实信号的经验分布的过程。由此可以看出，使用学习到的字典进行稀疏编码类似于一种特定的变分自编码器，其中解码器是线性函数，而编码器是一种稀疏编码算法。 接下来，我们展示了字典学习也可以从深度学习引入的计算进步中受益，例如并行化和随机优化。 最后，我们表明，通过字典进行的正则化在计算机断层扫描（CT）重建中的表现可与基于最先进的模型和数据驱动的方法相媲美。 显示英文摘要

摘要： This work presents an approach for image reconstruction in clinical low-dose tomography that combines principles from sparse signal processing with ideas from deep learning. First, we describe sparse signal representation in terms of dictionaries from a statistical perspective and interpret dictionary learning as a process of aligning distribution that arises from a generative model with empirical distribution of true signals. As a result we can see that sparse coding with learned dictionaries resembles a specific variational autoencoder, where the decoder is a linear function and the encoder is a sparse coding algorithm. Next, we show that dictionary learning can also benefit from computational advancements introduced in the context of deep learning, such as parallelism and as stochastic optimization. Finally, we show that regularization by dictionaries achieves competitive performance in computed tomography (CT) reconstruction comparing to state-of-the-art model based and data driven approaches.