# CT-PrepAgent: Bounded Policy and Controlled Execution for Adaptive CT Data Preparation

arXiv: https://arxiv.org/abs/2608.01233 · HTML: https://mlsanity.com/paper/2608.01233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 2 Aug 2026.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning](https://mlsanity.com/paper/2311.10537/) (2023)
- [MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning](https://mlsanity.com/paper/2406.00922/) (2024)
- [How Well Can Modern LLMs Act as Agent Cores in Radiology Environments?](https://mlsanity.com/paper/2412.09529/) (2024)
- [LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules](https://mlsanity.com/paper/2511.21042/) (2025)
- [ABRA: Agent Benchmark for Radiology Applications](https://mlsanity.com/paper/2605.11224/) (2026)
- [Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision](https://mlsanity.com/paper/2307.05804/) (2023)
- [CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration](https://mlsanity.com/paper/2410.02316/) (2024)
- [AutoCT: Automated CT registration, segmentation, and quantification](https://mlsanity.com/paper/2310.17780/) (2023)

## Abstract (arXiv)

Heterogeneous computed tomography (CT) acquisitions and diverse downstream task requirements limit the transferability of fixed data preparation workflows across data sources and tasks. Existing approaches typically rely on manually designed or dataset-specific rules, making it difficult to accommodate changes in acquisition conditions and analytical objectives without manual intervention. Large language model (LLM)-based agents have shown promise for automating medical workflows, yet their potential for adaptive CT data preparation remains largely unexplored. To bridge this gap, we propose CT-PrepAgent, which enables adaptive CT data preparation through a bounded policy and controlled deterministic execution. Deterministic inspection constructs structured data--task profiles, from which a policy decides an eligible DICOM series or predefined preprocessing profile, while the controlled execution flow guards, resolves, executes, and verifies the decision with bounded recovery when enabled and safe quarantine otherwise. Across three public CT segmentation tasks, CT-PrepAgent derived data-task adaptive preprocessing decisions and achieved the highest macro-average Dice. On two private raw-DICOM cohorts, CT-PrepAgent increased verified output yield from 61.7% to 70.0% and yielded similar registration metrics on common verified outputs. Controlled fault and replay tests validate bounded recovery, safe quarantine, and policy-free replay under tested fault and drift settings.