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SU‐G‐JeP2‐02: A Unifying Multi‐Atlas Approach to Electron Density Mapping Using Multi‐Parametric MRI for Radiation Treatment Planning
Author(s) -
Ren S,
Hara W,
Le Q,
Wang L,
Xing L,
Li R
Publication year - 2016
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4957022
Subject(s) - probability density function , voxel , density estimation , electron density , computer science , nuclear medicine , artificial intelligence , parametric statistics , pattern recognition (psychology) , mathematics , physics , electron , statistics , medicine , quantum mechanics , estimator
Purpose: MRI has a number of advantages over CT as a primary modality for radiation treatment planning (RTP). However, one key bottleneck problem still remains, which is the lack of electron density information in MRI. In the work, a reliable method to map electron density is developed by leveraging the differential contrast of multi‐parametric MRI. Methods: We propose a probabilistic Bayesian approach for electron density mapping based on T1 and T2‐weighted MRI, using multiple patients as atlases. For each voxel, we compute two conditional probabilities: (1) electron density given its image intensity on T1 and T2‐weighted MR images, and (2) electron density given its geometric location in a reference anatomy. The two sources of information (image intensity and spatial location) are combined into a unifying posterior probability density function using the Bayesian formalism. The mean value of the posterior probability density function provides the estimated electron density. Results: We evaluated the method on 10 head and neck patients and performed leave‐one‐out cross validation (9 patients as atlases and remaining 1 as test). The proposed method significantly reduced the errors in electron density estimation, with a mean absolute HU error of 138, compared with 193 for the T1‐weighted intensity approach and 261 without density correction. For bone detection (HU>200), the proposed method had an accuracy of 84% and a sensitivity of 73% at specificity of 90% (AUC = 87%). In comparison, the AUC for bone detection is 73% and 50% using the intensity approach and without density correction, respectively. Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection based on multi‐parametric MRI of the head with highly heterogeneous anatomy. This could allow for accurate dose calculation and reference image generation for patient setup in MRI‐based radiation treatment planning.