Paper Info
Title
Learning Personalized Representation for Inverse Problems in Medical Imaging Using Deep Neural Network.
Abstract
Recently deep neural networks have been widely and successfully applied in computer vision tasks and attracted growing interests in medical imaging. One barrier for the application of deep neural networks to medical imaging is the need of large amounts of prior training pairs, which is not always feasible in clinical practice. In this work we propose a personalized representation learning framework where no prior training pairs are needed, but only the patientu0027s own prior images. The representation is expressed using a deep neural network with the patientu0027s prior images as network input. We then applied this novel image representation to inverse problems in medical imaging in which the original inverse problem was formulated as a constraint optimization problem and solved using the alternating direction method of multipliers (ADMM) algorithm. Anatomically guided brain positron emission tomography (PET) image reconstruction and image denoising were employed as examples to demonstrate the effectiveness of the proposed framework. Quantification results based on simulation and real datasets show that the proposed personalized representation framework outperform other widely adopted methods.
Year
Venue
Field
2018
arXiv: Computer Vision and Pattern Recognition
Iterative reconstruction,Pattern recognition,Computer science,Medical imaging,Clinical Practice,Image representation,Artificial intelligence,Brain positron emission tomography,Inverse problem,Artificial neural network,Machine learning,Feature learning
DocType
Volume
Citations 
Journal
abs/1807.01759
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Kuang Gong101.01
Kyung Sang Kim2226.56
Jianan Cui301.01
Ning Guo401.35
Ciprian Catana5172.92
Jinyi Qi628435.82
Quanzheng Li718132.36