Intra-Fractional Respiratory Motion-Synchronized Internal Deformation Vector Field Estimation
Abstract
Purpose
To advance sparse-information constrained respiratory modeling toward an information-augmentation driven paradigm by incorporating data augmentation, multimodality guidance, prior-informed representation, and improved optimization pipelines.
Methods
We introduce a patient-specific motion-modeling framework for internal deformation reconstruction. To enhance respiratory-phase diversity and variability characterization, a multi-stage data augmentation pipeline integrates PCA-based motion characterization, static velocity field(SVF) interpolation, setup error simulation, and variational autoencoder(VAE)-based generation, which i) effectively expands temporal-phases, ii) incorporates irregular respiratory variabilities, and iii) preserves diffeomorphism and physical plausibility. Instead of direct image synthesizing/structural tracking, we estimate intermediate DVFs to mitigate image blurring, which benefits from prior-enhancements of respiratory characteristics captured by PCA-derived DVF-eigenvectors. We also propose cross-domain collaborative optimization to integrate advantages of supervised-and-unsupervised learning by contributing an implicit optimization-loop with cross-domain informing and regularization, which i) enhances modeling adaptabilities to unknown phases through enforcing surrogate consistency, and ii) eliminates impacts of surrogate variations on modeling robustness. We assemble a multi-center, multi-site 4D database enrolling 35 patients—10 prospective and 25 retrospective—undergoing IMRT/IMPT.
Results
We achieved RMSE of 1.337m-1, PSNR of 31.510dB, and SSIM of 0.958 for CT reconstruction, while deviation amplitude of centroid (DCAM) of 0.385/0.310 mm, DSC of 0.958/0.967 and relative volume changes of 20.2%/14.6% for tumor/whole-lungs tracking. Within a IMRT planning scheme (prescribed dose: 60Gy; beam configuration: 0°,40°,80°,120°,160°,200°), no significant differences were observed in voxel-wise dose differences(P=0.04, two-side T-tests), DVH indices (P=0.03 for V20 of whole-lung; P<0.01 for D95 of tumor), global Gamma-passing-rates(P<0.01), dose HI(P=0.02), and CI(P=0.02). No dramatic fluctuations exhibited across full-respiratory-cycle, with standard deviations of 0.122m-1, 0.687dB, and 0.019 for RMSE, PSNR, and SSIM, respectively. High correlation were demonstrated between reconstructed images and ground-truths, with Pearson correlation coefficients of 0.959±0.029(SI), 0.955±0.036(LR), and 0.923±0.039(AP).
Conclusion
Comprehensive multi-center evaluations have substantiated methodological sophistication of our framework, which represents a promising advance toward adaptive respiratory tracking-enhanced precise radiotherapy.