arXiv cs.AI· Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani·· 14 小时前AI 评分31
Agentic AI 推理与学习采样用于稀疏视角 CT 任务感知投影选择：预测血流动力学
Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics
AI 导读
研究用 agentic AI 选择器为稀疏视角 CTA 挑选任务感知投影，直接预测颅内动脉瘤的血流动力学。在空间分辨 TAWSS 预测上，Agentic 与 Soft top-K 误差最低，Agentic 略优，且在极端稀疏下显著优于固定采样。为血流动力学优化的投影掩码图像重建效果很差，说明任务最优采集与重建最优采集本质不同。
正文 · 原文
Abstract:Sparse-view computed tomography (CT) reduces the number of acquired projections, but conventional sampling strategies are designed primarily for image reconstruction rather than for downstream physical inference. Here, we investigate optimal task-aware projection selection for directly predicting hemodynamics from sparse CT angiography (CTA) measurements. Using computational fluid dynamics simulations of intracranial aneurysms, we generate synthetic fan-beam CTA projections and train a Fourier neural operator (FNO) to predict two targets: the spatially resolved time-averaged wall shear stress (TAWSS) field and the spatially averaged TAWSS. The FNO was coupled with optimal input selection for simultaneous optimal sampling and training. Our goal was to investigate if agentic AI could discover optimal sampling strategies that outperform current methods. We compared fixed uniform and golden-angle sampling with three task-aware optimal selection strategies: Gumbel-Softmax, a Soft top-K method, and an agentic AI selector. The methods were compared at different view selection budgets. A detailed analysis of the AI agent was performed to understand its reasoning and performance. For spatially resolved TAWSS, Agentic and Soft top-K achieve the lowest mean errors (Agentic performs slightly better) and substantially outperform fixed sampling under extreme sparsity. For spatially averaged TAWSS, optimized selection becomes important at extremely sparse acquisitions. Analysis of the selected masks reveals multiple distinct subsets concentrated around informative regions. Moreover, masks optimized for hemodynamic prediction yield poor image reconstructions, showing that task-optimal acquisition differs fundamentally from reconstruction-optimal acquisition. This work suggests the possibility of designing sparse acquisitions directly around downstream physical quantities.
| Subjects: | Artificial Intelligence (cs.AI); Fluid Dynamics (physics.flu-dyn) |
| Cite as: | arXiv:2610.39003 [cs.AI] |
| (or arXiv:2610.39003v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39003 arXiv-issued DOI via DataCite |
Submission history
From: Amirhossein Arzani [view email]
[v1]
Wed, 28 Oct 2026 22:30:30 UTC (9,589 KB)
来源：arXiv cs.AI · arxiv.org