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arXiv AI By Mahmoud Elhadidy, Roshan M. D'Souza, Amirhossein Arzani

Agentic AI Reasoning and Learned Sampling for Task-Aware Projection Selection in Sparse-View CT: Predicting Hemodynamics

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The study explores task‑aware projection selection for predicting hemodynamics from sparse‑view CT angiography. Using synthetic CTA data and a Fourier neural operator, the authors compare fixed sampling schemes with three optimal selection methods—Gumbel‑Softmax, Soft top‑K, and an agentic AI selector—finding that the agentic and Soft top‑K approaches yield the lowest errors for spatially resolved wall shear stress, especially under extreme sparsity. The analysis shows that task‑optimal masks differ markedly from those optimal for image reconstruction, highlighting a new direction for designing sparse acquisitions around downstream physical quantities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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