Agentic Autoresearch for CT Reconstruction
Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data. We ask whether a large language model (LLM) agent can do the labor of reconstruction research on its own, and whether a ranking measured on ideal data predicts behavior under realistic noise. We built an agentic loop: the agent edits a solver, runs a short cluster job, reads one frozen metric, and revises. The metric is a calibrated headroom score against the FBP baseline, inside the field of view; every method shares the same differentiable fan-beam projector. We benchmarked 26 methods on Mayo low-dose CT (noise-limited) and a 128-view sparse-view breast task from the...
| Authors | Andreas Maier, Lucas Kachelriess, Siming Bayer et al. |
|---|---|
| Primary category | physics.med-ph |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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