Task-Adaptive Low-Dose CT Reconstruction

TL;DR

This paper introduces a task-adaptive low-dose CT reconstruction framework that uses a pre-trained task network to improve diagnostic detail preservation, outperforming existing methods and compatible with various models.

Contribution

The proposed framework uniquely incorporates a frozen pre-trained task network into the reconstruction loss, enhancing diagnostic detail preservation in low-dose CT images.

Findings

Achieves Dice scores up to 0.707 on liver segmentation

Outperforms joint-training approaches and traditional methods

Can be integrated into existing models via simple loss modifications

Abstract

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