超极化129Xe MRI的多模态增强去噪扩散模型研究
收稿日期: 2025-03-26
网络出版日期: 2025-06-16
基金资助
国家自然科学基金(82127802);国家自然科学基金(21921004);中国科学院战略性先导研究计划(XDB0540000);中国科学院战略性先导研究计划(XDC0170000);湖北省重点技术基金(2021ACA013);湖北省尖刀攻关工程专项(2023BAA021);湖北省自然科学基金(2023AFB1061)
Research on a Multi-modal Enhanced Denoising Diffusion Model for Hyperpolarized 129Xe MRI
Received date: 2025-03-26
Online published: 2025-06-16
超极化129Xe MRI作为一种新兴的磁共振成像（MRI）技术，在多种肺部疾病的诊疗中发挥了重要作用．然而在采集过程中产生的噪声会对数据质量造成影响，降低了该技术在临床应用中的可靠性．为此，本文提出了一种基于深度学习的多模态特征增强条件扩散模型，以实现去除噪声，提升图像质量的目的．该模型通过输入相同屏气状态下采集的1H MRI作为约束条件，设计了一种多模态特征增强模块，用于提高模型对多模态信息的利用和对微小局部变换的敏感性．实验结果表明，在与其他方法对比中，本文方法具有更好的去噪性能和更强的图像细节保留能力．在通气缺陷分割任务中，分割结果进一步证明了本文方法对提升129Xe MRI临床可靠性的作用．
张明玉 , 肖洒 , 石胜杰 , 张学成 , 周欣 . 超极化129Xe MRI的多模态增强去噪扩散模型研究[J]. 波谱学杂志, 2025 , 42(4) : 364 -377 . DOI: 10.11938/cjmr20253153
Hyperpolarized 129Xe magnetic resonance imaging (MRI) is an emerging medical imaging technique that plays an important role in the diagnosis and treatment of numerous lung diseases. However, the noise generated during the acquisition process affects the data quality and limits the reliability of the technique in clinical diagnosis and treatment. In this paper, we propose a multimodal feature-enhanced conditional diffusion model based on deep learning that aims to remove noise and improve image quality. The model inputs acquired 1H MRI as constraints, and a multimodal feature enhancement module is specially designed, which aims to enhance the effectiveness of the model in exploiting multimodal information and the sensitivity to changes in local details of the image. The experimental results show that the method has the best denoising performance and detail preservation compared to other methods, and demonstrate in a ventilation defect segmentation task that the method can enhance the reliability of 129Xe MRI in clinical practice.
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