Higher-order singular value decomposition-based lung parcellation for breathing motion management

Positron emission tomography (PET) imaging of the lungs is confounded by respiratory motion-induced blurring artifacts that degrade quantitative accuracy. Gating and motion-compensated image reconstruction are frequently used to correct these motion artifacts in PET. In the absence of voxel-by-voxel deformation measures, surrogate signals from external markers are used to track internal motion and generate gated PET images. The objective of our work is to develop a group-level parcellation framework for the lungs to guide the placement of markers depending on the location of the internal target region. We present a data-driven framework based on higher-order singular value decomposition (HOSVD) of deformation tensors that enables identification of synchronous areas inside the torso and on the skin surface. Four-dimensional (4-D) magnetic resonance (MR) imaging based on a specialized radial pulse sequence with a one-dimensional slice-projection navigator was used for motion capture under free-breathing conditions. The deformation tensors were computed by nonrigidly registering the gated MR images. Group-level motion signatures obtained via HOSVD were used to cluster the voxels both inside the volume and on the surface. To characterize the parcellation result, we computed correlation measures across the different regions of interest (ROIs). To assess the robustness of the parcellation technique, leave-one-out cross-validation was performed over the subject cohort, and the dependence of the result on varying numbers of gates and singular value thresholds was examined. Overall, the parcellation results were largely consistent across these test cases with Jaccard indices reflecting high degrees of overlap. Finally, a PET simulation study was performed which showed that, depending on the location of the lesion, the selection of a synchronous ROI may lead to noticeable gains in the recovery coefficient. Accurate quantitative interpretation of PET images is important for lung cancer management. Therefore, a guided motion monitoring approach is of utmost importance in the context of pulmonary PET imaging.