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Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis 期刊论文

编号：

b4e2e276-470b-44a1-95fb-88e67e6b5405
作者：

Huang, Huiyuan^[1,2,3];Lu, Junfeng(路俊锋)^[4]Wu, Jinsong(吴劲松)^[4]Ding, Zhongxiang^[5];Chen, Shuda^[6];Duan, Lisha^[7];Cui, Jianling^[7];Chen, Fuyong^[8];Kang, Dezhi^[8];Qi, Le^[9];Qiu, Wusi^[10];Lee, SeongWhan^[11,12];Qiu, ShiJun^[13];Shen, Dinggang^[11,12,13];Zang, YuFeng^[1,3];Zhang, Han^[1,3,11,12];
语种：

英文
期刊：

SCIENTIFIC REPORTS ISSN：2045-2322 2018 年 8 卷 ; JAN 19
收录：
摘要：

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of using presurgical fMRI for tumor delineation. In particular, we introduce a novel method dedicated to tumor detection based on independent component analysis (ICA) of resting-state fMRI (rs-fMRI) with automatic tumor component identification. Multi-center rs-fMRI data of 32 glioma patients from three centers, plus the additional proof-of-concept data of 28 patients from the fourth center with non-brain musculoskeletal tumors, are fed into individual ICA with different total number of components (TNCs). The best-fitted tumorrelated components derived from the optimized TNCs setting are automatically determined based on a new template-matching algorithm. The success rates are 100%, 100% and 93.75% for glioma tissue detection for the three centers, respectively, and 85.19% for musculoskeletal tumor detection. We propose that the high success rate could come from the previously overlooked ability of BOLD rs-fMRI in characterizing the abnormal vascularization, vasomotion and perfusion caused by tumors. Our findings suggest an additional usage of the rs-fMRI for comprehensive presurgical assessment.
推荐引用方式
GB/T 7714：

Huang Huiyuan,Lu Junfeng,Wu Jinsong, et al. Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis [J].SCIENTIFIC REPORTS,2018,8.
APA：

Huang Huiyuan,Lu Junfeng,Wu Jinsong,Ding Zhongxiang,&Zhang Han.(2018).Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis .SCIENTIFIC REPORTS,8.
MLA：

Huang Huiyuan, et al. "Tumor Tissue Detection using Blood-Oxygen-Level-Dependent Functional MRI based on Independent Component Analysis" .SCIENTIFIC REPORTS 8(2018).
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