Pseudo CT Generation Based on 3D Group Feature Extraction and Alternative Regression Forest for MRI-Only Radiotherapy.

In recent decades, magnetic resonance imaging (MRI) has attracted attention in radiation therapy as the only modality. This nontrivial task requires the application of pseudo computed tomography (PCT) generation methods. On the one hand, the electron density information provided by the CT scan is critical for calculating the 3D dose distribution of tissues. On the other hand, the bone image provided by the CT is precise enough for the construction of a radiograph. Lately, the use of MRI/CT has combined all of the soft tissue contrast merits which are contributed by the MRI and the virtue of CT imaging. However, owing to the unbalance of voxel-intensities in the MRI and CT scan, the MRI/CT workflow also has shortcomings. Inspired by the random forest-based PCT estimation, this paper investigated the potential of the 3D group feature as the input of the random forest regression, which is based on the 3D block-matching method, taking the correlated central voxel as the target. Four types of features including the voxel level, sub-regional level, whole cubic level with adaptive weighted conjunction and compressed level were introduced to attain the robust features. The group-based random forest regression was then utilized to obtain the approximated PCT only from corresponding MRI, and the feature is extracted from the 3D cubic MRI patches and mapped to the 3D cubic CT patch, which helps in decreasing the computation difficulty, representing the MR patches into an anatomical feature space. The alternative regression forest was used in solving the regression task for enhancing the prediction power compared with the random forest. The proposed method could efficiently capture the correlation that is observable between the CT as well as the MR images on the basis of the alternative random forest (ARF) with cubic features, and the experimental results show the performance and effectiveness of the proposed method compared with the recent learning-based and atlas-based (AB) methods [ABSTRACT FROM AUTHOR]

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