Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images.
Multiparametric tissue characterization of brain neoplasms and their recurrence using pattern classification of MR images.
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DOI：
10.1016/j.acra.2008.01.029
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发表时间：
2008-08
影响因子：
4.8
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Verma, Ragini;Zacharaki, Evangelia I.;Ou, Yangming;Cai, Hongmin;Chawla, Sanjeev;Lee, Seung-Koo;Melhem, Elias R.;Wolf, Ronald;Davatzikos, Christos
关键词：
更好地描绘大块肿瘤边界以及更微妙的肿瘤浸润的范围和程度，可以极大地有益于脑肿瘤的治疗。 MRI 是治疗前后评估的主要成像方式，通常将传统序列与灌注加权成像和扩散张量成像 (DTI) 等更先进的技术相结合。本研究的目的是通过统计图像分析方法整合结构 MRI 和 DTI 来量化肿瘤的多参数成像特征，以便潜在地捕获从任何单个图像或参数中都不明显的复杂和微妙的组织特征。五个结构 MR 序列，即 B0、扩散加权图像、FLAIR、T1 加权和钆增强 T1 加权，以及从 DTI 计算的两个标量图（即分数各向异性和表观扩散系数）用于创建基于强度的组织轮廓。该技术被纳入非线性模式分类技术中，以创建多参数概率组织表征，该技术应用于 14 名新诊断的原发性高级别肿瘤患者的数据，这些患者在成像前未接受任何治疗。初步结果表明，这种多参数组织表征有助于更好地区分肿瘤、水肿和健康组织，并识别未来可能进展为肿瘤的组织。这已在专家评估的组织上得到验证。这种方法在治疗中具有潜在的应用，通过确定健康组织和肿瘤组织的空间分布来帮助计算机辅助手术，以及识别相对更容易发生肿瘤复发的组织。
Treatment of brain neoplasms can greatly benefit from better delineation of bulk neoplasm boundary and the extent and degree of more subtle neoplastic infiltration. MRI is the primary imaging modality for evaluation before and after therapy, typically combining conventional sequences with more advanced techniques like perfusion-weighted imaging and diffusion tensor imaging (DTI). The purpose of this study is to quantify the multi-parametric imaging profile of neoplasms by integrating structural MRI and DTI via statistical image analysis methods, in order to potentially capture complex and subtle tissue characteristics that are not obvious from any individual image or parameter. Five structural MR sequences, namely, B0, Diffusion Weighted Images, FLAIR, T1-weighted, and gadolinium-enhanced T1-weighted, and two scalar maps computed from DTI, i.e., fractional anisotropy and apparent diffusion coefficient, are used to create an intensity-based tissue profile. This is incorporated into a non-linear pattern classification technique to create a multi-parametric probabilistic tissue characterization, which is applied to data from 14 patients with newly diagnosed primary high grade neoplasms who have not received any therapy prior to imaging. Preliminary results demonstrate that this multi-parametric tissue characterization helps to better differentiate between neoplasm, edema and healthy tissue, and to identify tissue that is likely progress to neoplasm in the future. This has been validated on expert assessed tissue. This approach has potential applications in treatment, aiding computer-assisted surgery by determining the spatial distributions of healthy and neoplastic tissue, as well as in identifying tissue that is relatively more prone to tumor recurrence.
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