Prediction of CT Substitutes from MR Images Based on Local Sparse Correspondence Combination

Abstract

Prediction of CT substitutes from MR images are clinically desired for dose planning in MR-based radiation therapy and attenuation correction in PET/MR. Considering that there is no global relation between intensities in MR and CT images, we propose local sparse correspondence combination (LSCC) for the prediction of CT substitutes from MR images. In LSCC, we assume that MR and CT patches are located on two nonlinear manifolds and the mapping from the MR manifold to the CT manifold approximates a diffeomorphism under a local constraint. Several techniques are used to constrain locality: 1) for each patch in the testing MR image, a local search window is used to extract patches from the training MR/CT pairs to construct MR and CT dictionaries; 2) k-Nearest Neighbors is used to constrain locality in the MR dictionary; 3) outlier detection is performed to constrain locality in the CT dictionary; 4) Local Anchor Embedding is used to solve the MR dictionary coefficients when representing the MR testing sample. Under these local constraints, the coefficient weights are linearly transferred from MR to CT, and used to combine the samples in the CT dictionary to generate CT predictions. The proposed method has been evaluated for brain images on a dataset of 13 subjects. Each subject has T1- and T2-weighted MR images, as well as a CT image with a total of 39 images. Results show the effectiveness of the proposed method which provides CT predictions with a mean absolute error of 113.8 HU compared with real CTs.