A conditional point cloud diffusion model for deformable liver motion tracking via a single arbitrarily-angled x-ray projection.
Authors
Affiliations (6)
Affiliations (6)
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas, 75390-9096, UNITED STATES.
- Department of Radiation Oncology, The University of Texas Southwestern Medical Center, 2280 Inwood Rd, Dallas, Texas, 75390-9096, UNITED STATES.
- UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas, 75390-9096, UNITED STATES.
- Department of Radiation Oncology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Texas, 75390-9096, UNITED STATES.
- Department of Radiation Oncology, University of Texas Southwestern Medical Centre, 5323 Harry Hines Blvd, Dallas, Texas, 75390-9183, UNITED STATES.
- Department of Radiation Oncology, UT Southwestern Medical Center, 2280 inwood road, Dallas, Texas, 75235, UNITED STATES.
Abstract
Deformable liver motion tracking using a single X-ray projection enables real-time motion monitoring and treatment intervention. We introduce a conditional point cloud diffusion model-based framework for accurate and robust liver motion tracking from arbitrarily angled single X-ray projections. We propose a conditional point cloud diffusion model for liver motion tracking (PCD-Liver), which estimates volumetric liver motion by solving deformable vector fields (DVFs) of a prior liver surface point cloud, based on a single X-ray image. It is a patient-specific model of two main components: a rigid alignment model to estimate the liver's overall shifts, and a conditional point cloud diffusion model that further corrects for the liver surface's deformation. Conditioned on the motion-encoded features extracted from a single X-ray projection by a geometry-informed feature pooling layer, the diffusion model iteratively solves detailed liver surface DVFs in a projection angle-agnostic fashion. The liver surface motion solved by PCD-Liver is subsequently fed as the boundary condition into a UNet-based biomechanical model to infer the liver's internal motion to localize liver tumors. A dataset of 10 liver cancer patients was used for evaluation. We used the root mean square error (RMSE) and 95-percentile Hausdorff distance (HD95) metrics to examine the liver point cloud motion estimation accuracy, and the center-of-mass error (COME) to quantify the liver tumor localization error. The mean (±s.d.) RMSE, HD95, and COME of the prior liver or tumor before motion estimation were 8.82 mm (±3.58 mm), 10.84 mm (±4.55 mm), and 9.72 mm (±4.34 mm), respectively. After PCD-Liver's motion estimation, the corresponding values were 3.63 mm (±1.88 mm), 4.29 mm (±1.75 mm), and 3.46 mm (±2.15 mm). Under highly noisy conditions, PCD-Liver maintained stable performance. This study presents an accurate and robust framework for liver deformable motion estimation and tumor localization for image-guided radiotherapy.