Volumetric prediction of breathing and slow drifting motion in the abdomen using radial MRI and multi-temporal resolution modeling.

Abdominal organ motions introduce geometric uncertainties to radiotherapy. This study investigates a multi-temporal resolution 3D motion prediction scheme that accounts for both breathing and slow drifting motion in the abdomen in support of MRI-guided radiotherapy. Ten-minute MRI scans were acquired for 8 patients using a volumetric golden-angle stack-of-stars sequence. The first five-minutes was used for patient-specific motion modeling. Fast breathing motion was modeled from high temporal resolution radial k-space samples, which served as a navigator signal to sort k-space data into different bins for high spatial resolution reconstruction of breathing motion states. Slow drifting motion was modeled from a lower temporal resolution image time series which was reconstructed by sequentially combining a large number of breathing-corrected k-space samples. Principal components analysis (PCA) was performed on deformation fields between different motion states. Gaussian kernel regression and linear extrapolation were used to predict PCA coefficients of future motion states for breathing motion (340 ms ahead of acquisition) and slow drifting motion (8.5 s ahead of acquisition) respectively. k-space data from the remaining five-minutes was used to compare ground truth motions states obtained from retrospective reconstruction/deformation with predictions. Median distances between predicted and ground truth centroid positions of gross tumor volume (GTV) and organs at risk (OARs) were less than 1 mm on average. 95- percentile Hausdorff distances between predicted and ground truth GTV contours of various breathing motions states were 2 mm on average, which was smaller than the imaging resolution and 95-percentile Hausdorff distances between predicted and ground truth OAR contours of different slow drifting motion states were less than 0.2 mm. These results suggest that multi-temporal resolution motion models are capable of volumetric predictions of breathing and slow drifting motion with sufficient accuracy and temporal resolution for MRI-based tracking, and thus have potential for supporting MRI-guided abdominal radiotherapy.