Oriented histogram-based vector field embedding for characterizing 4D CT data sets in radiotherapy

Abstract

In lung radiotherapy, the primary objective is to optimize treatment outcomes by minimizing exposure to healthy tissues while delivering the prescribed dose to the target volume. The challenge lies in accounting for lung tissue motion due to breathing, which impacts precise treatment alignment. To address this, the paper proposes a prospective approach that relies solely on pre-treatment information, such as planning CT scans and derived data like vector fields from deformable image registration. This data is compared to analogous patient data to tailor treatment strategies, i.e., to be able to review treatment parameters and success for similar patients. To allow for such a comparison, an embedding and clustering strategy of prospective patient data is needed. Therefore, the main focus of this study lies on reducing the dimensionality of deformable registration-based vector fields by employing a voxel-wise spherical coordinate transformation and a low-dimensional 2D oriented histogram representation. Afterwards, a fully unsupervised UMAP embedding of the encoded vector fields (i.e., patient-specific motion information) becomes applicable. The functionality of the proposed method is demonstrated with 71 in-house acquired 4D CT data sets and 33 external 4D CT data sets. A comprehensive analysis of the patient clusters is conducted, focusing on the similarity of breathing patterns of clustered patients. The proposed general approach of reducing the dimensionality of registration vector fields by encoding the inherent information into oriented histograms is, however, applicable to other tasks.