268 - Simultaneous Anatomical Deformation Reconstruction and Tumor Motion Tracking Guided by Multimodal Respiratory Surrogates: Framework Development and Multi-Center Validation
Presenter(s)
G. Zhang1, Z. Jiang2, and L. Wang3; 1Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan, China, 2Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China, 3Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Ji'nan, China
Purpose/Objective(s): To propose a multimodal surrogates-guided, multi-task respiratory-modeling framework for simultaneous, intra-fractional anatomical motion reconstruction and tumor-tracking.
Materials/Methods: Driving by complementary external-and-internal motion information captured by respiration-synchronized in-room surrogates—optical surface images(OSI) and single-view X-ray projections, this framework delivers a unified global-and-local anatomical tracking pipeline through interrelated multi-task learning(MTL) architecture. The Key components including: i) hybrid attention mechanisms integrating convolutional block attention modules(CBAM) and Swin-transformers to enhance deformation feature encoding, ii) improved back-projection(iBP) algorithm to accelerate sparse-projection propagation, and iii) task-specific prior-enhancements to provide anatomical characterization and clinical delineation. A multi-center, prospective-and-retrospective database including 432 patients(272 thoracic vs. 160 abdominal) was established for development and validation pipelines.
Results: i) Multi-indicator analyses. We achieved high-fidelity CT reconstruction with RMSE of 1.49/1.52m-1(thoracic/abdominal), PSNR of 30.56/30.45 dB, and SSIM of 0.94/0.93. For tumor-tracking, centroid deviation amplitude(DCAM) were 0.47/0.51mm(thoracic/abdominal), DSC were 0.96/0.94, and HD95 were 6.36/6.34mm. No statistically significant differences (two-side T-test) were observed between each validation-fold and testing results. ii) Full-cycle tracking. The predicted tumor motion strongly correlated with ground truths with Pearson correlation coefficients(SI/AP/LR) of 0.98±0.02(SI), 0.97±0.02(AP), 0.93±0.08(LR) for thoracic and 0.98±0.01(SI), 0.96±0.02(AP), 0.90±0.03(LR) for abdominal. iii) Impacts of respiratory surrogate configuration. Single-modal surrogates consistently underperformed the multi-modal configuration across all indicators (P<0.01). With OSI included, single-view projection yielded significantly inferior performances(P<0.01 for RMSE, PSNR, SSIM, and DSC; P=0.02 and 0.04 for DCAM and HD95), with triple- and quad-view projections comparable to dual-view (P>0.05). Improvements were significant in RMSE (P=0.04) and PSNR (P =0.04) when projection number reached five. Various orthogonal-view combinations yield no significant differences. iv) Computational efficiency. The total latency was 159.4 ms—well below the temporal threshold (500ms) recommended by AAPM TG-75.
Conclusion: Extensive, multi-center evaluations demonstrated the framework's superior accuracy in anatomical reconstruction and tumor-tracking, highlighting its conceptual rationality and promising clinical feasibility in respiratory tracking and intra-fractional adaptive therapy.