TY - GEN
T1 - A Neural Network Approach for Image Reconstruction from a Single X-Ray Projection
AU - Rouf, Samiha
AU - Shen, Chenyang
AU - Cao, Yan
AU - Davis, Conner
AU - Jia, Xun
AU - Lou, Yifei
N1 - Funding Information:
Supported by the National Science Foundation?s Enriched Doctoral Training Program, DMS grant # 1514808.
Funding Information:
Supported by the National Science Foundation’s Enriched Doctoral Training Program, DMS grant #1514808.
PY - 2020
Y1 - 2020
N2 - Time-resolved imaging becomes popular in radiotherapy in that it significantly reduces blurring artifacts in volumetric images reconstructed from a set of 2D X-ray projection data. We aim at developing a neural network (NN) based machine learning algorithm that allows for reconstructing an instantaneous image from a single projection. In our approach, each volumetric image is represented as a deformation of a chosen reference image, in which the deformation is modeled as a linear combination of a few basis functions through principal component analysis (PCA). Based on this PCA deformation model, we train an ensemble of neural networks to find a mapping from a projection image to PCA coefficients. For image reconstruction, we apply the learned mapping on an instantaneous projection image to obtain the PCA coefficients, thus getting a deformation. Then, a volumetric image can be reconstructed by applying the deformation on the reference image. Experimentally, we show promising results on a set of simulated data.
AB - Time-resolved imaging becomes popular in radiotherapy in that it significantly reduces blurring artifacts in volumetric images reconstructed from a set of 2D X-ray projection data. We aim at developing a neural network (NN) based machine learning algorithm that allows for reconstructing an instantaneous image from a single projection. In our approach, each volumetric image is represented as a deformation of a chosen reference image, in which the deformation is modeled as a linear combination of a few basis functions through principal component analysis (PCA). Based on this PCA deformation model, we train an ensemble of neural networks to find a mapping from a projection image to PCA coefficients. For image reconstruction, we apply the learned mapping on an instantaneous projection image to obtain the PCA coefficients, thus getting a deformation. Then, a volumetric image can be reconstructed by applying the deformation on the reference image. Experimentally, we show promising results on a set of simulated data.
KW - Neural network
KW - Principal component analysis
KW - Time-resolved volumetric imaging
KW - X-ray projection
UR - http://www.scopus.com/inward/record.url?scp=85079089427&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079089427&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39343-4_18
DO - 10.1007/978-3-030-39343-4_18
M3 - Conference contribution
AN - SCOPUS:85079089427
SN - 9783030393427
T3 - Communications in Computer and Information Science
SP - 208
EP - 219
BT - Medical Image Understanding and Analysis - 23rd Conference, MIUA 2019, Proceedings
A2 - Zheng, Yalin
A2 - Williams, Bryan M.
A2 - Chen, Ke
PB - Springer
T2 - 23rd Conference on Medical Image Understanding and Analysis, MIUA 2019
Y2 - 24 July 2019 through 26 July 2019
ER -