文章：

基于深度学习方法加速脑肿瘤患者放射治疗计划中的磁共振波谱代谢物定量分析

An Accelerated Spectroscopic MRI Metabolite Quantification Based on a Deep Learning Method for Radiation Therapy Planning in Brain Tumor Patients

原文发布日期：27 January 2025

DOI: 10.3390/cancers17030423

类型: Article

开放获取: 是

英文摘要：

Background: Spectroscopic MRI (sMRI) is a quantitative imaging technique that maps infiltrated tumors in the brain without contrast injections. In a previous study (NCT03137888), sMRI-guided radiation treatment extended patient survival, showing promise for clinical translation. The spectral fitting of individual voxels in an sMRI dataset generate metabolite concentration maps that guide treatment. The established spectral analysis methods use iterative least-squares fitting (FITT) that are computationally demanding. This study compares the performance of NNFit, a neural network-based, accelerated spectral fitting model, to the established FITT for metabolite quantification and radiation treatment planning. Methods: NNFit is a self-supervised deep learning model trained on 50 ms echo-time (TE) sMRI data to estimate metabolite levels of choline (Cho), creatine (Cr), and NAA. We trained the model on 30 GBM patients (56 scans) and tested it on 17 GBM patients (29 scans). NNFit’s performance was compared to the FITT using structural similarity indices (SSIM) and the Dice coefficient. Results: NNFit significantly improved processing speed while maintaining strong agreement with FITT. The radiation target volumes defined by Cho/NAA ≥ 2x were visually comparable, with fewer artifacts in NNFit. Structural similarity indices (SSIM) indicated minimal bias and high consistency across methods. Conclusions: This study highlights NNFit’s potential for rapid, accurate, and artifact-reduced metabolic imaging, enabling faster radiotherapy planning.

摘要翻译： 

背景：光谱磁共振成像（sMRI）是一种无需注射造影剂即可定量绘制脑部浸润性肿瘤的成像技术。在先前的研究（NCT03137888）中，sMRI引导的放射治疗延长了患者生存期，显示出临床转化的潜力。sMRI数据集中单个体素的光谱拟合可生成指导治疗的代谢物浓度图。现有光谱分析方法采用计算量较大的迭代最小二乘拟合（FITT）。本研究比较了基于神经网络的加速光谱拟合模型NNFit与现有FITT方法在代谢物定量和放射治疗计划中的性能。 方法：NNFit是一种自监督深度学习模型，基于50毫秒回波时间（TE）的sMRI数据训练，用于估算胆碱（Cho）、肌酸（Cr）和N-乙酰天冬氨酸（NAA）的代谢物水平。模型使用30例胶质母细胞瘤（GBM）患者（56次扫描）的数据进行训练，并在17例GBM患者（29次扫描）中测试。通过结构相似性指数（SSIM）和Dice系数比较NNFit与FITT的性能。 结果：NNFit在保持与FITT高度一致性的同时显著提高了处理速度。以Cho/NAA ≥ 2倍阈值定义的放射靶区在视觉上具有可比性，且NNFit产生的伪影更少。结构相似性指数（SSIM）显示方法间偏差极小且一致性高。 结论：本研究证实NNFit能够实现快速、准确且伪影减少的代谢成像，有助于加速放射治疗计划制定。

原文链接：

An Accelerated Spectroscopic MRI Metabolite Quantification Based on a Deep Learning Method for Radiation Therapy Planning in Brain Tumor Patients