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磁共振波谱成像的病变分割采用卷积差分法。

Lesion segmentation for MR spectroscopic imaging using the convolution difference method.

机构信息

Department of Radiology, University of Miami School of Medicine, Miami, Florida.

出版信息

Magn Reson Med. 2019 Mar;81(3):1499-1510. doi: 10.1002/mrm.27500. Epub 2018 Oct 10.

Abstract

PURPOSE

Delineation of lesion boundaries from volumetric MRSI metabolite ratio maps using a method that accounts for the spatial response function of the acquisition and variable spectral quality and is robust to signal heterogeneity within the lesion.

METHODS

A novel method for lesion segmentation, termed convolution difference, has been developed that is robust to signal heterogeneity within the lesion and to differences in the spatial response function. Procedures are described for processing metabolite ratio maps and to exclude regions of inadequate spectral quality. This method was evaluated using computer simulations, and the results were compared with an iterative thresholding technique that determines an optimal amplitude threshold, and with the use of a fixed amplitude threshold. These methods were evaluated for segmentation of volumetric MRSI studies of gliomas using maps of the choline to N-acetylaspartate ratio, and a qualitative comparison of lesion volumes carried out.

RESULTS

Simulation studies indicated improved performance for the convolution difference method when applied to ratio maps. Variations in tumor volume were observed for the in vivo studies between the convolution difference and the iterative thresholding methods; however, visual analysis indicates that both showed improved accuracy in comparison to using a fixed amplitude threshold.

CONCLUSION

This study reinforces previous reports indicating that the use of fixed threshold values for segmentation of maps with broad spatial response functions can result in errors in lesion volume definition. A novel segmentation method, termed the convolution difference, has been introduced and demonstrated to be robust for segmentation of volumetric MRSI metabolite data.

摘要

目的

利用一种能够考虑到采集的空间响应函数和可变谱质量,并对病变内信号异质性具有鲁棒性的方法,从容积 MRSI 代谢物比图中描绘病变边界。

方法

开发了一种新的病变分割方法,称为卷积差,它对病变内的信号异质性和空间响应函数的差异具有鲁棒性。本文描述了处理代谢物比图和排除光谱质量不足区域的程序。该方法通过计算机模拟进行了评估,并将结果与一种迭代阈值技术进行了比较,该技术确定了最佳幅度阈值,并与使用固定幅度阈值进行了比较。这些方法用于使用胆碱与 N-乙酰天冬氨酸比的比率图对脑肿瘤的容积 MRSI 研究进行分割,并进行了病变体积的定性比较。

结果

模拟研究表明,当应用于比率图时,卷积差方法的性能得到了改善。在体内研究中,卷积差和迭代阈值方法之间观察到肿瘤体积的变化;然而,视觉分析表明,与使用固定幅度阈值相比,这两种方法都显示出了更高的准确性。

结论

本研究再次证实了之前的报告,即使用固定阈值值对具有广泛空间响应函数的图谱进行分割可能导致病变体积定义的错误。引入了一种新的分割方法,称为卷积差,该方法已被证明对容积 MRSI 代谢物数据的分割具有鲁棒性。

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本文引用的文献

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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).多模态脑肿瘤图像分割基准(BRATS)。
IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024. doi: 10.1109/TMI.2014.2377694. Epub 2014 Dec 4.
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