Enabling Real-Time Volumetric Imaging in Interventional Radiology Suits via a Deep Learning Framework Robust to C-arm Tilt
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arXiv
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914161964875776 |
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| author | Utomo, Fawazilla Reynolds, Tess Hindley, Nicholas |
| author_facet | Utomo, Fawazilla Reynolds, Tess Hindley, Nicholas |
| contents | Contemporary interventional imaging lacks the real-time 3D guidance needed for the precise localization of mobile thoracic targets. While Cone-Beam CT (CBCT) provides 3D data, it is often too slow for dynamic motion tracking. Deep learning frameworks that reconstruct 3D volumes from sparse 2D projections offer a promising solution, but their performance under the geometrically complex, non-zero tilt acquisitions common in interventional radiology is unknown. This study evaluates the robustness of a patient-specific deep learning framework, designed to estimate 3D motion, to a range of C-arm cranial-caudal tilts. Using a 4D digital phantom with a simulated respiratory cycle, 2D X-ray projections were simulated at five cranial-caudal tilt angles across 10 breathing phases. A separate deep learning model was trained for each tilt condition to reconstruct 3D volumetric images. The framework demonstrated consistently high-fidelity reconstruction across all tilts, with a mean Structural Similarity Index (SSIM) > 0.980. While statistical analysis revealed significant differences in performance between tilt groups (p < 0.0001), the absolute magnitude of these differences was minimal (e.g., the mean absolute difference in SSIM across all tilt conditions was ~0.0005), indicating they were not functionally significant. The magnitude of respiratory motion was found to be the dominant factor influencing accuracy, with the impact of C-arm tilt being a much smaller, secondary effect. These findings demonstrate that a patient-specific, motion-estimation-based deep learning approach is robust to geometric variations encountered in realistic clinical scenarios, representing a critical step towards enabling real-time 3D guidance in flexible interventional settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_13980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enabling Real-Time Volumetric Imaging in Interventional Radiology Suits via a Deep Learning Framework Robust to C-arm Tilt Utomo, Fawazilla Reynolds, Tess Hindley, Nicholas Medical Physics Contemporary interventional imaging lacks the real-time 3D guidance needed for the precise localization of mobile thoracic targets. While Cone-Beam CT (CBCT) provides 3D data, it is often too slow for dynamic motion tracking. Deep learning frameworks that reconstruct 3D volumes from sparse 2D projections offer a promising solution, but their performance under the geometrically complex, non-zero tilt acquisitions common in interventional radiology is unknown. This study evaluates the robustness of a patient-specific deep learning framework, designed to estimate 3D motion, to a range of C-arm cranial-caudal tilts. Using a 4D digital phantom with a simulated respiratory cycle, 2D X-ray projections were simulated at five cranial-caudal tilt angles across 10 breathing phases. A separate deep learning model was trained for each tilt condition to reconstruct 3D volumetric images. The framework demonstrated consistently high-fidelity reconstruction across all tilts, with a mean Structural Similarity Index (SSIM) > 0.980. While statistical analysis revealed significant differences in performance between tilt groups (p < 0.0001), the absolute magnitude of these differences was minimal (e.g., the mean absolute difference in SSIM across all tilt conditions was ~0.0005), indicating they were not functionally significant. The magnitude of respiratory motion was found to be the dominant factor influencing accuracy, with the impact of C-arm tilt being a much smaller, secondary effect. These findings demonstrate that a patient-specific, motion-estimation-based deep learning approach is robust to geometric variations encountered in realistic clinical scenarios, representing a critical step towards enabling real-time 3D guidance in flexible interventional settings. |
| title | Enabling Real-Time Volumetric Imaging in Interventional Radiology Suits via a Deep Learning Framework Robust to C-arm Tilt |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2511.13980 |