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一种用于测量MRI皮质下脑结构的自动配准算法。

An automated registration algorithm for measuring MRI subcortical brain structures.

作者信息

Iosifescu D V, Shenton M E, Warfield S K, Kikinis R, Dengler J, Jolesz F A, McCarley R W

机构信息

Laboratory of Neuroscience, Harvard Medical School, Brockton, Massachusetts 02401, USA.

出版信息

Neuroimage. 1997 Jul;6(1):13-25. doi: 10.1006/nimg.1997.0274.

Abstract

An automated registration algorithm was used to elastically match an anatomical magnetic resonance (MR) atlas onto individual brain MR images. Our goal was to evaluate the accuracy of this procedure for measuring the volume of MRI brain structures. We applied two successive algorithms to a series of 28 MR brain images, from 14 schizophrenia patients and 14 normal controls. First, we used an automated segmentation program to differentiate between white matter, cortical and subcortical gray matter, and cerebrospinal fluid. Next, we elastically deformed the atlas segmentation to fit the subject's brain, by matching the white matter and subcortical gray matter surfaces. To assess the accuracy of these measurements, we compared, on all 28 images, 11 brain structures, measured with elastic matching, with the same structures traced manually on MRI scans. The similarity between the measurements (the relative difference between the manual and the automated volume) was 97% for whole white matter, 92% for whole gray matter, and on average 89% for subcortical structures. The relative spatial overlap between the manual and the automated volumes was 97% for whole white matter, 92% for whole gray matter, and on average 75% for subcortical structures. For all pairs of structures rendered with the automated and the manual method, Pearson correlations were between r = 0.78 and r = 0.98 (P < 0.01, N = 28), except for globus pallidus, where r = 0.55 (left) and r = 0. 44 (right) (P < 0.01, N = 28). In the schizophrenia group, compared to the controls, we found a 16.7% increase in MRI volume for the basal ganglia (i.e., caudate nucleus, putamen, and globus pallidus), but no difference in total gray/white matter volume or in thalamic MR volume. This finding reproduces previously reported results, obtained in the same patient population with manually drawn structures, and suggests the utility/efficacy of our automated registration algorithm over more labor-intensive manual tracings.

摘要

使用一种自动配准算法将解剖磁共振(MR)图谱弹性匹配到个体脑MR图像上。我们的目标是评估该程序测量MRI脑结构体积的准确性。我们将两种连续算法应用于来自14名精神分裂症患者和14名正常对照的一系列28幅MR脑图像。首先,我们使用一个自动分割程序来区分白质、皮质和皮质下灰质以及脑脊液。接下来,通过匹配白质和皮质下灰质表面,我们将图谱分割进行弹性变形以拟合受试者的大脑。为了评估这些测量的准确性,我们在所有28幅图像上,将通过弹性匹配测量的11个脑结构与在MRI扫描上手动描绘的相同结构进行比较。对于整个白质,测量值之间的相似性(手动和自动体积之间的相对差异)为97%,对于整个灰质为92%,对于皮质下结构平均为89%。手动和自动体积之间的相对空间重叠对于整个白质为97%,对于整个灰质为92%,对于皮质下结构平均为75%。对于用自动和手动方法呈现的所有结构对,除了苍白球(左侧r = 0.55,右侧r = 0.44)(P < 0.01,N = 28)外,Pearson相关性在r = 0.78和r = 0.98之间(P < 0.

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