A framework synthesizes DRRs from CCTA volumes with dense 3D-to-2D labels via C-arm geometry simulation and introduces a GIMM module that improves multi-view correspondence matching on the resulting dataset.
arXiv preprint arXiv:2312.11593 (2023)
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Anatomy-Grounded Synthetic Coronary Angiography for Geometry-Informed Multi-View Matching
A framework synthesizes DRRs from CCTA volumes with dense 3D-to-2D labels via C-arm geometry simulation and introduces a GIMM module that improves multi-view correspondence matching on the resulting dataset.