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Updated: Sep 13, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
Time-resolved dynamic CBCT reconstruction using prior-model-free spatiotemporal Gaussian representation (PMF-STGR)
Jiacheng Xie1,2,3, Hua-Chieh Shao1,2,3, You Zhang1,2,3
1Department of Radiation Oncology, The Advanced Imaging and Informatics for Radiation Therapy (AIRT) Laboratory, Dallas, TX 75390, United States of America.
Abstract:
Objective.Time-resolved CBCT imaging, which reconstructs a dynamic sequence of CBCTs reflecting intra-scan motion (one CBCT per x-ray projection without phase sorting/binning), is highly desired for regular/irregular motion characterization, patient setup, and motion-adapted radiotherapy. Representing patient anatomy and associated motion fields as 3D Gaussians, we developed a Gaussian representation-based framework (PMF-STGR) for fast and accurate dynamic CBCT reconstruction.Approach.PMF-STGR comprises three major components: a dense set of 3D Gaussians to reconstruct a reference-frame CBCT for the dynamic sequence; another 3D Gaussian set to capture three-level, coarse-to-fine motion-basis-components (MBCs) to model the intra-scan motion; and a CNN-based motion encoder to solve projection-specific temporal coefficients for the MBCs. Scaled by the temporal coefficients, the learned MBCs will be combined into deformation vector fields to deform the reference CBCT into projection-specific, time-resolved CBCTs to capture the dynamic motion. Due to the strong representation power of 3D Gaussians, PMF-STGR can reconstruct dynamic CBCTs in a 'one-shot' training fashion from a standard 3D CBCT scan, without using any prior anatomical/motion model.Main results.We evaluated PMF-STGR using XCAT phantom simulations and real patient full/half-fan scans. Metrics, including the image relative error (RE), structural-similarity-index-measure (SSIM), tumor center-of-mass-error (COME), and landmark localization error (LE), were used to evaluate the accuracy of solved dynamic CBCTs and motion. PMF-STGR shows clear advantages over a state-of-the-art, implicit neural representation (INR)-based approach, PMF-STINR. Compared with PMF-STINR, PMF-STGR reduces reconstruction time by ∼50% while reconstructing less blurred images with comparable/better motion accuracy. For XCAT, the mean(±s.d.) RE, SSIM, and COME were 0.128(0.009), 0.990(0.002), and 0.71 mm(0.40 mm) for PMF-STGR, compared with 0.149(0.016), 0.944(0.006), and 0.94 mm(0.18 mm) for PMF-STINR. For patients, the mean(±s.d.) landmark LE was 1.40 mm(0.34 mm) for PMF-STGR, and 1.54 mm(0.35 mm) for PMF-STINR.Significance.With improved efficiency/accuracy, PMF-STGR enhances the applicability of dynamic CBCT imaging for potential clinical translation.

