Proof of concepts of resting state fMRI implementation for presurgical planning in a large general hospital

IF 2.7 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Physica Medica-European Journal of Medical Physics Pub Date : 2025-07-19 DOI:10.1016/j.ejmp.2025.105052

Beatrice Macchi , Marco Maria Jacopo Felisi , Gaia Muti , Davide Cicolari , Marco Parisotto , Luciana Gennari , Ivana Sartori , Paolo Arosio , Mariangela Piano , Paola Enrica Colombo , Silvia Squarza

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引用次数: 0

Abstract

Purpose

Task-based functional MRI (tb-fMRI) effectiveness as a support tool in brain mapping may be limited by patients’ poor cooperation. Resting-state fMRI (rs-fMRI) represents an alternative or complementary approach.

In this work, we developed and validated an analysis pipeline for rs-fMRI acquisitions, primarily aimed at language mapping in drug-resistant epileptic patients. The workflow relies on open-source software and semi-automatized solutions, ensuring easy clinical adoption.

Methods

Rs-fMRI data were acquired from 26 subjects (15 volunteers, 11 patients) using a 3 T-MRI scanner. The developed pipeline starts with preprocessing of raw data, subsequently analyzed through Independent Component Analysis (ICA), performed with MELODIC-FSL tool. Manual classification, semi-automated classifiers (FIX, ICA-AROMA) and a template matching procedure were employed to classify the ICA components and extract each patient rs-language network. Finally, verb-generation tb-fMRI and Diffusion Tensor Imaging were acquired to map language regions and reconstruct the arcuate fasciculus, respectively. The rs-language networks were validated evaluating the three acquisition modalities agreement.

Results

Trained FIX showed AUC = 0.95 and ICA-AROMA 97 % of classification accuracy, considering manual classification as ground truth. Manual classification identified one (46 %), two (31 %), or three (19 %) language-related components per subject. The manually selected language components were among the top three ranked by the template matching in 88 % of cases, 100 % considering the top five.

The Dice index between rs-fMRI and tb-fMRI language maps resulted 0.36 ± 0.13. Rs-language areas resulted qualitatively well-connected by the reconstructed arcuate fasciculus.

Conclusion

The developed pipeline confirmed strong potential for clinical applicability in a large general hospital, especially when tb-fMRI is infeasible.

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大型综合医院静息状态功能磁共振成像在术前规划中的应用证明

目的：基于任务的功能MRI （tb-fMRI）作为脑制图辅助工具的有效性可能受到患者配合不力的限制。静息状态功能磁共振成像（rs-fMRI）是一种替代或补充的方法。在这项工作中，我们开发并验证了rs-fMRI获取的分析管道，主要针对耐药性癫痫患者的语言映射。该工作流程依赖于开源软件和半自动化解决方案，确保易于临床采用。方法26例（15名志愿者，11例患者）使用3台T-MRI扫描仪获取sr - fmri数据。开发的管道从原始数据预处理开始，随后通过MELODIC-FSL工具进行独立成分分析（ICA）分析。采用人工分类、半自动分类器（FIX、ICA- aroma）和模板匹配程序对ICA成分进行分类，提取每个患者的rs-language网络。最后，利用动词生成的tfmri和弥散张量成像技术分别绘制语言区域和重建弓状束。对rs语言网络进行了验证，评估了三种获取方式协议。结果strain FIX的AUC = 0.95， ICA-AROMA的分类准确率为97%，以人工分类为基础。人工分类确定了每个主题一个（46%）、两个（31%）或三个（19%）与语言相关的组件。在88%的情况下，手动选择的语言组件是模板匹配排名前三的，100%考虑前五名。rs-fMRI与tb-fMRI语言图的Dice指数为0.36±0.13。重建的弓形神经束连通了rs语言区。结论所开发的管道在大型综合医院具有较强的临床应用潜力，特别是在无法进行tb-fMRI的情况下。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Physica Medica-European Journal of Medical Physics 生物-生物物理

CiteScore

6.80

自引率

14.70%

发文量

493

审稿时长

78 days

期刊介绍： Physica Medica, European Journal of Medical Physics, publishing with Elsevier from 2007, provides an international forum for research and reviews on the following main topics: Medical Imaging Radiation Therapy Radiation Protection Measuring Systems and Signal Processing Education and training in Medical Physics Professional issues in Medical Physics.