基于深度算子网络与测试时自适应的稀疏平面数据四维二尖瓣反流血流动力学重建

中文摘要 AI 辅助

量化二尖瓣反流严重程度仍受临床血流汇聚方法假设的限制，而高保真模拟和体积流速测量对于常规使用而言过于缓慢。我们研究学习型解算子能否从体外实验实际提供的稀疏观测——单个平面速度切片和两条边界压力迹线——重建瞬态三维跨瓣血流动力学。深度算子网络在包含十一个二尖瓣反流口部模型的实验基准URANS数据库上预训练，学习从掩蔽的双分量平面速度快照到周围体积场的映射，随后通过在其稀疏测量上微调来适应未见过的目标病例。适应可靠地修正了监督平面内的流动拓扑，重新定向了预训练算子预测为笔直的高度偏心射流，并在几分钟内产生全场预测，而底层模拟需要数天。然而，其影响随距该平面的距离急剧衰减：与相位分辨粒子图像测速相比，重建误差从2毫米处的24.6%上升到6毫米处的52.6%，且修正与未修正层之间的失配降低了物理一致性。因此，单平面监督约束观测平面的效果远强于周围体积，我们将其识别为从稀疏平面数据实现连贯四维重建的主要障碍。

英文摘要

Quantifying mitral regurgitation severity remains limited by the assumptions of clinical flow convergence methods, while high-fidelity simulation and volumetric velocimetry are too slow for routine use. We investigate whether a learned solution operator can reconstruct transient three-dimensional transvalvular hemodynamics from the sparse observation an in-vitro experiment actually provides: a single planar velocity slice and two boundary pressure traces. A Deep Operator Network is pretrained on an experimentally benchmarked URANS database spanning eleven mitral regurgitation orifice phantoms, learning a mapping from a masked two-component planar velocity snapshot to the surrounding volumetric field, and is subsequently adapted to unseen target cases by fine-tuning on their sparse measurements. Adaptation reliably corrects the flow topology within the supervised plane, reorienting a strongly eccentric jet that the pretrained operator predicts as straight, and yields full-field predictions in minutes rather than the days required by the underlying simulations. Its influence decays sharply with distance from that plane, however: measured against phase-resolved particle image velocimetry, the reconstruction error rises from 24.6% at 2mm to 52.6% at 6mm, and the resulting mismatch between corrected and uncorrected layers degrades physical consistency. Single-plane supervision thus constrains the observed plane far more effectively than the surrounding volume, which we identify as the principal obstacle to coherent 4D reconstruction from sparse planar data.

发表机构

• Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
• The University of Tokyo（东京大学）

机构由 AI 辅助整理，请以论文原文为准。