Problem
Research questions and friction points this paper is trying to address.
Coronary centerline extraction
Vascular tree reconstruction
Non-linear hemodynamics
Patient-specific modeling
Ischemia assessment
End-to-End Autonomous Recursive Arborescence Deformable Flow and Non-Linear Hemodynamics for Patient-Specific Coronary Centerline Extraction
🤖 AI Summary
This study addresses the limitations of coronary centerline extraction, including severed bifurcations, non-anatomical shortcuts, and ischemia underestimation by linear flow models. We propose an end-to-end framework that decouples geometric tree generation from nonlinear hemodynamics. Methodologically, we introduce autonomous ostium localization to eliminate seed dependence, alongside a recursive tree state machine and Tree-NMS to ensure topological connectivity. Furthermore, a 3D feature pyramid deformable-flow architecture is integrated with a Picard-iterative nonlinear Kirchhoff solver to accurately capture functional ischemia. Experimental results demonstrate a mean localization error of 7.63 mm and mass conservation residuals reaching machine precision. Crucially, the proposed method successfully identifies ischemic lesions missed by linear models, significantly enhancing diagnostic sensitivity.
📝 Abstract
Extracting patient-specific vascular trees from volumetric medical images is fundamental to computational angiography and non-invasive hemodynamic assessment. Conventional voxel segmentation models often sever delicate bifurcations, while heuristic Euclidean Minimum Spanning Trees introduce non-anatomical shortcuts. Moreover, linear Poiseuille flow neglects quadratic kinetic dissipation across arterial narrowings, underestimating ischemia. We formulate an end-to-end framework decoupling continuous geometric arborescence generation from non-linear hemodynamics. First, an autonomous 3D Ostium Landmark Localization Head with dual-sinus query channels and spherical-gated refinement eliminates centerline seeding dependency, achieving cohort mean localization error of 7.63 mm (7.43 mm LCA, 7.83 mm RCA; 71.4%<= 8.0 mm) from raw contrast context. Second, a Spatially-Grounded Deformable Step Flow Architecture queries continuous 3D feature pyramids via trilinear sampling, sequentially generating trajectories with anchor boundary enforcement (X(0) = P_start). Third, a Top-Down Recursive Arborescence State Machine detects bifurcation peaks via Tree-NMS and parameterizes predecessor parent pointers (p_k<k), guaranteeing single connected acyclic tree topology (beta_0 = 1, beta_1 = 0) with differentiable step termination. Fourth, an iterative Picard non-linear Kirchhoff solver with Young-Tsai / Gould quadratic dissipation enforces machine-precision mass conservation (residual 5.82e-11 mL/s). Across 14 development patients under standardized in-silico stenosis stress testing (Q_0 = 4.0 mL/s), linear Poiseuille flow misclassifies 75% diameter lesions as non-ischemic (FFR>0.80) in 14/14 cases, whereas our non-linear solver captures functional ischemia (FFR = 0.5864, lesion disparity 32.89 mmHg, p = 6.10e-5) with 3.66x collateral shunting. Test set firewall isolation was maintained.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Coronary Centerline Extraction
Deformable Flow
Recursive Arborescence
Non-Linear Hemodynamics
End-to-End Framework
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