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Projection-Domain Sensitivity Analysis of Vertebral DRRs Under Intrinsic Calibration Perturbation

arXiv 2607.10551 ↗First posted 12 Jul 2026eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

Accurate geometric calibration is essential for fluoroscopy-guided spinal imaging, digitally reconstructed radiograph (DRR) generation, and 2D--3D vertebral registration. Although calibration quality is typically evaluated using reconstruction-based metrics such as reprojection error, its influence on projection-domain consistency remains poorly understood. This study presents a synthetic framework for evaluating how intrinsic calibration perturbations affect vertebral fluoroscopic projections and downstream registration performance. CT-derived vertebral models and controlled cone-beam imaging geometry were used to generate DRRs with both ground-truth and perturbed intrinsic calibration parameters while maintaining identical anatomy and acquisition pose. Projection-domain changes were quantified using anatomical landmark displacement, contour distance, silhouette overlap, image similarity, and landmark-based 2D--3D registration accuracy in anterior--posterior (AP) and lateral (LAT) views. Results show that even small intrinsic calibration perturbations produce measurable changes in vertebral projection geometry, contour morphology, landmark localization, and DRR appearance. Sensitivity is strongly view dependent, with LAT projections exhibiting substantially greater deformation and anatomical displacement than AP projections. These projection inconsistencies also degrade downstream 2D--3D registration, particularly rotational alignment accuracy. The findings demonstrate that projection-domain consistency complements conventional reconstruction-based calibration metrics and provides a practical framework for assessing calibration robustness. This approach may improve the reliability of DRR generation and fluoroscopy-guided vertebral registration in image-guided spinal applications.

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References · 6

  1. 2018DeepDRR -- A Catalyst for Machine Learning in Fluoroscopy-guided Procedurescited by 55
  2. 2025Hypergraph Tversky-Aware Domain Incremental Learning for Brain Tumor Segmentation with Missing Modalitiescited by 7
  3. 2021Transfer Learning from an Artificial Radiograph-landmark Dataset for Registration of the Anatomic Skull Model to Dual Fluoroscopic X-ray Imagescited by 2
  4. 2025Pedicle Screw Pairing and Registration for Screw Pose Estimation from Dual C-arm Images Using CAD Modelscited by 2
  5. 2026Visual Prompt-Agnostic Evolutioncited by 2
  6. 2026Intrinsic Tolerance in C-Arm Imaging: How Extrinsic Re-optimization Preserves 3D Reconstruction Accuracycited by 1

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