Physics Contribution
Robust Estimation of Electron Density From
Anatomic Magnetic Resonance Imaging of the
Brain Using a Unifying Multi-Atlas Approach
Shangjie Ren, PhD,
*
,y
Wendy Hara, MD,
y
Lei Wang, PhD,
y
Mark K. Buyyounouski, MD,
y
Quynh-Thu Le, MD,
y
Lei Xing, PhD,
y
and Ruijiang Li, PhD
y
*Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and
Automation, Tianjin University, Tianjin, China; and
y
Department of Radiation Oncology, Stanford
University School of Medicine , Palo Alto, California
Received Jul 25, 2016, and in revised form Nov 23, 2016. Accepted for publication Nov 29, 2016.
Summary
We developed a unifying
multi-atlas approach for
electron density mapping
based on standard-of -care
T1- and T2-weighted MRI.
The proposed method ach-
ieved robust electron density
estimation and bone detec-
tion in 10 patients. Our work
could provide the enabling
tools for using MRI as a
primary modality for radia-
tion treatment planning.
Purpose: To develop a reliable method to estimate electron density based on anatomic
magnetic resonance imaging (MRI) of the brain.
Methods and Materials: We proposed a unifying multi-atlas approach for elect ron
density estimation based on standard T1- and T2-weighted MRI. First, a composite
atlas was constructed through a voxelwise matching process using multiple atlases,
with the goal of mitigating effects of inherent anatomic variations between patients.
Next we computed for each voxel 2 kinds of conditional probabilities: (1) electron
density given its image intensity on T1- and T2-weighted MR images; and (2) electron
density given its spatial location in a refere nce anatomy, obtained by deformable im-
age registration. These were combined into a unifying posterior probability density
function using the Bayesian formalism, which provided the optimal estimates for elec-
tron density. We evaluated the method on 10 patients using leave-one-patient-out
cross-validation. Receiver operating characteristic analyses for detecting different tis-
sue types were performed.
Results: The propos ed method significantly reduced the errors in electron density esti-
mation, with a mean absolute Hounsfield unit error of 119, compared with 140 and 144
(P<.0001) using conventional T1-weighted intensity and geometry-based approaches,
respectively. For detection of bony anatomy, the proposed method achieved an 89%
area under the curve, 86% sensitivity, 88% specificity, and 90% accuracy, which
improved upon intensity and geometry-based approaches (area under the curve:
79% and 80%, respectively).
Reprint requests to: Ruijiang Li, PhD, Department of Radiation
Oncology, Stanford University School of Medicine, 1070 Arastradero Rd,
Palo Alto, CA 94304. Tel: (650) 724-5382; E-mail: rli2@stanford.edu
This work was partially supported by National Institutes of Health
grant 1R01CA193730, Natural Science Foundation of China grant
61401304, and the China Scholarship Council under grant 201506255060.
Conflict of interest: none.
Supplementary material for this article can be found at
www.redjournal.org.
Int J Radiation Oncol Biol Phys, Vol. 97, No. 4, pp. 849e857, 2017
0360-3016/$ - see front matter Ó 2016 Elsevier Inc. All rights reserved.
http://dx.doi.org/10.1016/j.ijrobp.2016.11.053
Radiation Oncology
International Journal of
biology physics
www.redjournal.org