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A Probabilistic Bayesian Approach to Derive Electron Density From MRI for Radiation Therapy Treatment Planning

M Gudur

M Gudur*, W Hara , L Wang , L Xing , R Li , Stanford University, Stanford, CA


TH-A-BRF-1 Thursday 7:30AM - 9:30AM Room: Ballroom F

Purpose: MRI significantly improves the accuracy and reliability of target delineation for patient simulation and treatment planning in radiation therapy, due to its superior soft tissue contrast as compared to CT. An MRI based simulation will reduce cost and simplify clinical workflow with zero ionizing radiation. However, MRI lacks the key electron density information. The purpose of this work is to develop a reliable method to derive electron density from MRI.

Methods: We adopt a probabilistic Bayesian approach for electron density mapping based on T1-weighted head MRI. For each voxel, we compute conditional probability of electron densities given its: (1) T1 intensity and (2) geometry in a reference anatomy, obtained by deformable image registration between the MRI of test patient and atlas. Intensity and geometry information are combined into a unifying posterior probability density function whose mean gives the electron density. Mean absolute HU error between the estimated and true CT, as well as ROC’s for bone detection (HU>200) were calculated for 8 patients. The performance was compared with a global intensity approach based on T1 and no density correction (set whole head to water).

Results: The proposed technique significantly reduced the errors in electron density estimation, with a mean absolute HU error of 132, compared with 139 for deformable registration (p=10⁻³), 371 for the intensity approach (p=10⁻⁵) and 282 without density correction (p=2x10⁻⁴). For 90% sensitivity in bone detection, the proposed method had a specificity of 85% and that for deformable registration, intensity and without density correction are 80%, 24% and 10% respectively.

Conclusion: The proposed unifying method provides accurate electron density estimation and bone detection from MRI of the head with highly heterogeneous regions. This paves the way for accurate dose calculation and generating reference images for patient setup in MRI-based treatment planning.

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