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 -