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使用 KL 散度对 CT 扫描中的正中矢状面进行稳健计算。

Robust calculation of the midsagittal plane in CT scans using the Kullback-Leibler's measure.

机构信息

Biomedical Imaging Lab, Agency for Science, Technology and Research, Matrix, Singapore, Singapore.

出版信息

Int J Comput Assist Radiol Surg. 2009 Nov;4(6):535-47. doi: 10.1007/s11548-009-0366-2. Epub 2009 Jun 13.

Abstract

OBJECTIVE

The identification of the interhemispheric fissure (IF) is important in clinical applications for brain landmark identification, registration, symmetry assessment, and pathology detection. The IF is usually approximated by the midsagittal plane (MSP) separating the brain into two hemispheres. We present a fast accurate, automatic, and robust algorithm for finding the MSP for CT scans acquired in emergency room (ER) with a large slice thickness, high partial volume effect, and substantial head tilt.

MATERIALS AND METHODS

An earlier algorithm for MSP identification from MRI using the Kullback-Leibler's measure was extended for CT by estimating patient's head orientation using model fitting, image processing, and atlas-based techniques. The new algorithm was validated on 208 clinical scans acquired mainly in the ER with slice thickness ranging from 1.5 to 6 mm and severe head tilt.

RESULTS

The algorithm worked robustly for all 208 cases. An angular discrepancy (degrees) and maximum distance (mm) between the calculated MSP and ground truth have the mean value (SD) 0.0258 degrees (0.9541 degrees) and 0.1472 (0.7373) mm, respectively. In average, the algorithm takes 10 s to process of a typical CT case.

CONCLUSION

The proposed algorithm is robust to head rotation, and correctly identifies the MSP for a standard clinical CT scan with a large slice thickness. It has been applied in our several CT stroke CAD systems.

摘要

目的

在临床应用中,识别大脑间裂(IF)对于脑地标识别、配准、对称性评估和病理检测非常重要。IF 通常通过中矢状面(MSP)来近似,将大脑分为两个半球。我们提出了一种快速、准确、自动和鲁棒的算法,用于从急诊室(ER)获取的厚层、部分容积效应高且头部倾斜较大的 CT 扫描中找到 MSP。

材料和方法

通过使用模型拟合、图像处理和基于图谱的技术来估计患者头部的方向,将之前用于 MRI 中 MSP 识别的基于 Kullback-Leibler 测度的算法扩展到 CT 中。新算法在 208 例主要在 ER 采集的临床扫描中进行了验证,扫描层厚从 1.5 毫米到 6 毫米不等,头部倾斜严重。

结果

该算法对所有 208 例均稳健运行。计算出的 MSP 和真实值之间的角度差异(度)和最大距离(mm)的平均值(标准差)分别为 0.0258 度(0.9541 度)和 0.1472(0.7373)mm。平均而言,该算法处理一个典型 CT 病例需要 10 秒。

结论

所提出的算法对头部旋转具有鲁棒性,并且可以正确识别标准临床 CT 扫描的 MSP,其层厚较大。它已应用于我们的几个 CT 中风 CAD 系统中。

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