LangMamba：一种基于语言驱动的Mamba框架，用于低剂量CT去噪与视觉-语言模型
LangMamba: A Language-driven Mamba Framework for Low-dose CT Denoising with Vision-language Models
AI总结：
本文提出LangMamba，一种语言驱动的Mamba框架，利用视觉-语言模型增强低剂量CT去噪，通过两阶段学习与双空间对齐损失，在公共数据集上显著提升细节保留和视觉保真度。
摘要：
低剂量计算机断层扫描（LDCT）可减少辐射暴露，但往往会降低图像质量，可能影响诊断准确性。现有的基于深度学习的去噪方法主要关注像素级映射，忽略了高层语义指导的潜在益处。视觉-语言模型（VLM）的最新进展表明，语言可以作为捕获结构化语义信息的强大工具，为改进LDCT重建提供了新的机遇。在本文中，我们提出了LangMamba，一种用于LDCT去噪的语言驱动Mamba框架，利用VLM派生的表示来增强来自正常剂量CT（NDCT）的监督。LangMamba采用两阶段学习策略。首先，我们预训练一个语言引导的自编码器（LangAE），利用冻结的VLM将NDCT图像映射到富含解剖信息的语义空间。其次，我们将LangAE与两个关键组件协同，以指导LDCT去噪：语义增强的高效去噪器（SEED），它通过高效的Mamba机制增强与NDCT相关的局部语义并捕获全局特征；以及语言参与的双空间对齐（LangDA）损失，确保去噪图像在感知和语义空间上与NDCT对齐。在两个公共数据集上的大量实验表明，LangMamba优于传统的先进方法，显著提高了细节保留和视觉保真度。值得注意的是，LangAE对未见数据集表现出很强的泛化能力，从而降低了训练成本。此外，LangDA损失通过将语言引导的见解整合到图像重建中，提高了可解释性，并提供了一种即插即用的方式。我们的发现为语言作为监督信号推进LDCT去噪的潜力提供了新的见解。代码可在https://github.com/hao1635/LangMamba上公开获取。
英文摘要：
Low-dose computed tomography (LDCT) reduces radiation exposure but often degrades image quality, potentially compromising diagnostic accuracy. Existing deep learning-based denoising methods focus primarily on pixel-level mappings, overlooking the potential benefits of high-level semantic guidance. Recent advances in vision-language models (VLMs) suggest that language can serve as a powerful tool for capturing structured semantic information, offering new opportunities to improve LDCT reconstruction. In this paper, we introduce LangMamba, a Language-driven Mamba framework for LDCT denoising that leverages VLM-derived representations to enhance supervision from normal-dose CT (NDCT). LangMamba follows a two-stage learning strategy. First, we pre-train a Language-guided AutoEncoder (LangAE) that leverages frozen VLMs to map NDCT images into a semantic space enriched with anatomical information. Second, we synergize LangAE with two key components to guide LDCT denoising: Semantic-Enhanced Efficient Denoiser (SEED), which enhances NDCT-relevant local semantic while capturing global features with efficient Mamba mechanism, and Language-engaged Dual-space Alignment (LangDA) Loss, which ensures that denoised images align with NDCT in both perceptual and semantic spaces. Extensive experiments on two public datasets demonstrate that LangMamba outperforms conventional state-of-the-art methods, significantly improving detail preservation and visual fidelity. Remarkably, LangAE exhibits strong generalizability to unseen datasets, thereby reducing training costs. Furthermore, LangDA loss improves explainability by integrating language-guided insights into image reconstruction and offers a plug-and-play fashion. Our findings shed new light on the potential of language as a supervisory signal to advance LDCT denoising. The code is publicly available on https://github.com/hao1635/LangMamba.
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