MRI-only Radiation Therapy: Pseudo-CT Based on Cubic-Feature Extraction and Alternative Regression Forest

Abstract

Despite the extensive attention attracted by magnetic resonance imaging (MRI) in the radiation therapy, computed tomography was reintroduced by the researchers. During the calculation process of the 3D dose distribution of tissues, there were some arguments about the electron density information obtained from the CT scan. However, the CT-provided bones are accurate for constructing a radiograph. Recently, the advantages boosted by the soft tissue contrast relying on MRI and as well as the advantages boosted by CT imaging have been combined by the using of MRI/CT. Unfortunately, disadvantages still exist in the MRI/CT workflow because the voxel-intensities are unbalanced in the MRI and the CT scan. Here, based on the mapping method of CT and MRI, the potential of pseudo-CT (PCT) instead of CT planning was studied. The estimated PCT only from the corresponding MRI was obtained by using the patch-based random forest regression. The CT voxel target was trained by 3D Gabor feature in the MRI cube and the Local Binary Pattern (LBP). Besides, the regression task was solved by the alternative regression forest. According to the experiment, the method performs better than the current dictionary learning-based (DLB) method or atlas-based (AB) method.