Deep learning-based low-dose computed tomography reconstruction methods already achieve high performance on standard image quality metrics like peak signal-to-noise ratio and structural similarity index measure. Yet, they frequently fail to preserve the critical anatomical details needed for diagnostic tasks. This fundamental limitation hinders their clinical applicability despite their high metric scores. We propose a novel task-adaptive reconstruction framework that addresses this gap by incorporating a frozen pre-trained task network as a regularization term in the reconstruction loss function. Unlike existing joint-training approaches that simultaneously optimize both reconstruction and task networks, and risk diverging from satisfactory reconstructions, our method leverages a pre-trained task model to guide reconstruction training while still maintaining diagnostic quality. We validate our framework on a liver and liver tumor segmentation task. Our task-adaptive models achieve Dice scores up to 0.707, approaching the performance of full-dose scans (0.874), and substantially outperforming joint-training approaches (0.331) and traditional reconstruction methods (0.626). Critically, our framework can be integrated into any existing deep learning-based reconstruction model through simple loss function modification, enabling widespread adoption for task-adaptive optimization in clinical practice. Our codes are available at: https://github.com/itu-biai/task_adaptive_ct

翻译：基于深度学习的低剂量计算机断层扫描重建方法在峰值信噪比和结构相似性指数等标准图像质量指标上已取得优异表现，然而这些方法往往无法保留诊断任务所需的关键解剖细节。这一根本性局限导致其尽管在指标评分上表现突出，却难以在临床实践中推广应用。本文提出一种新颖的任务自适应重建框架，通过将冻结的预训练任务网络作为正则化项纳入重建损失函数，以解决上述问题。与现有同时优化重建网络与任务网络、可能偏离满意重建结果的联合训练方法不同，本方法利用预训练任务模型指导重建训练，同时保持诊断质量。我们在肝脏及肝肿瘤分割任务上验证了本框架的有效性。我们的任务自适应模型获得了高达0.707的Dice分数，接近全剂量扫描的性能（0.874），并显著优于联合训练方法（0.331）和传统重建方法（0.626）。重要的是，本框架可通过简单的损失函数修改集成到任何现有基于深度学习的重建模型中，为临床实践中任务自适应优化的广泛采用提供了可能。代码已开源：https://github.com/itu-biai/task_adaptive_ct