Dimensionality reduction

Algorithm

Engineering

Theoretical computer science

Iterative reconstruction

Computer science

Mechanical engineering

Graph

Pattern recognition (psychology)

Mathematics

Leverage (statistics)

Artificial intelligence

Nonlinear dimensionality reduction

Combinatorics

Topology (electrical circuits)

Manifold (fluid mechanics)

Artificial neural network

Computer vision

Convolution (computer science)

IEEE Transactions on Medical Imaging

Low-dose computed tomography (LDCT) scans, which can effectively alleviate the radiation problem, will degrade the imaging quality. In this paper, we propose a novel LDCT reconstruction network that unrolls the iterative scheme and performs in both image and manifold spaces. Because patch manifolds of medical images have low-dimensional structures, we can build graphs from the manifolds. Then, we simultaneously leverage the spatial convolution to extract the local pixel-level features from the images and incorporate the graph convolution to analyze the nonlocal topological features in manifold space. The experiments show that our proposed method outperforms both the quantitative and qualitative aspects of state-of-the-art methods. In addition, aided by a projection loss component, our proposed method also demonstrates superior performance for semi-supervised learning. The network can remove most noise while maintaining the details of only 10% (40 slices) of the training data labeled.

MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction