Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach.
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 electron density estimation based on standard T1- and T2-weighted MRI. First, a composite atlas wa...
| Publicado en: | International Journal of Radiation Oncology, Biology, Physics Vol. 97; no. 4; pp. 849 - 858 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Pergamon Press - An Imprint of Elsevier Science
Mar2017
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=121355552&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121355552 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03603016 1ZQ jtl: International Journal of Radiation Oncology, Biology, Physics issn: 03603016 maglogo: N pubinfo: dt: Mar2017 vid: 97 iid: 4 pid: 2410 pub: Pergamon Press - An Imprint of Elsevier Science artinfo: ui: 121355552 121355552 NLM28244422 121355552 10.1016/j.ijrobp.2016.11.053 NLM28244422 121355552 ppf: 849 ppct: 9 formats: tig: atl: Robust Estimation of Electron Density From Anatomic Magnetic Resonance Imaging of the Brain Using a Unifying Multi-Atlas Approach. aug: au: Ren, Shangjie Hara, Wendy Wang, Lei Buyyounouski, Mark K. Le, Quynh-Thu Xing, Lei Li, Ruijiang affil: Tianjin Key Laboratory of Process Measurement and Control, School of Electrical Engineering and Automation, Tianjin University, Tianjin, China sug: subj: Radiotherapy, Computer-Assisted Methods Brain Physiology Image Interpretation, Computer Assisted Methods Magnetic Resonance Imaging Methods Brain Anatomy and Histology Electrons Reproducibility of Results Subtraction Technique Brain Radiation Effects Sensitivity and Specificity Radiation Dosage Algorithms Radiometry Methods Human Funding Source ab: 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 electron 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 reference anatomy, obtained by deformable image registration. These were combined into a unifying posterior probability density function using the Bayesian formalism, which provided the optimal estimates for electron density. We evaluated the method on 10 patients using leave-one-patient-out cross-validation. Receiver operating characteristic analyses for detecting different tissue types were performed.Results: The proposed method significantly reduced the errors in electron density estimation, 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).Conclusion: The proposed multi-atlas approach provides robust electron density estimation and bone detection based on anatomic MRI. If validated on a larger population, our work could enable the use of MRI as a primary modality for radiation treatment planning. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|