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首页> 外文期刊>Medical Physics >Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HiSS) MRI: A pilot study
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Residual analysis of the water resonance signal in breast lesions imaged with high spectral and spatial resolution (HiSS) MRI: A pilot study

机译:高光谱和空间分辨率(HISS)MRI成像乳腺病变中水共振信号的残余分析:试验研究

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Purpose: High spectral and spatial resolution magnetic resonance imaging (HiSS MRI) yields information on the local environment of suspicious lesions. Previous work has demonstrated the advantages of HiSS (complete fat-suppression, improved image contrast, no required contrast agent, etc.), leading to initial investigations of water resonance lineshape for the purpose of breast lesion classification. The purpose of this study is to investigate a quantitative imaging biomarker, which characterizes non-Lorentzian components of the water resonance in HiSS MRI datasets, for computer-aided diagnosis (CADx). Methods: The inhomogeneous broadening and non-Lorentzian or off-peak components seen in the water resonance of proton spectra of breast HiSS images are analyzed by subtracting a Lorentzian fit from the water peak spectra and evaluating the difference spectrum or residual. The maxima of these residuals (referred to hereafter as off-peak components) tend to be larger in magnitude in malignant lesions, indicating increased broadening in malignant lesions. The authors considered only those voxels with the highest magnitude off-peak components in each lesion, with the number of selected voxels dependent on lesion size. Our voxel-based method compared the magnitudes and frequencies of off-peak components of all voxels from all lesions in a database that included 15 malignant and 8 benign lesions (yielding <3900 voxels) based on the lesions' biopsy-confirmed diagnosis. Lesion classification was accomplished by comparing the average off-peak component magnitudes and frequencies in malignant and benign lesions. The area under the ROC curve (AUC) was used as a figure of merit for both the voxel-based and lesion-based methods. Results: In the voxel-based task of distinguishing voxels from malignant and benign lesions, off-peak magnitude yielded an AUC of 0.88 (95 confidence interval 0.84, 0.91). In the lesion-based task of distinguishing malignant and benign lesions, average off-peak magnitude yielded an AUC 0.83 (95 confidence interval 0.61, 0.98). Conclusions: These promising AUC values suggest that analysis of the water-resonance in each HiSS image voxel using residual analysis could have high diagnostic utility and could be used to enhance current CADx methods and allow detection of breast cancer without the need to inject contrast agents.
机译:目的:高光谱和空间分辨率磁共振成像(HISS MRI)产生关于可疑病变的当地环境的信息。以前的工作证明了Hiss的优势(完全脂肪抑制,改进的图像对比,没有必需的造影剂等),导致对乳房病变分类的目的的水共振线初探。本研究的目的是研究定量成像生物标志物,其表征了HISS MRI数据集中的水共振的非Lorentzian组分,用于计算机辅助诊断(CADX)。方法:通过从水峰光谱中减去Lorentzian型和评估差异光谱或残留物来分析在乳房Hiss图像的质子谱的水共振中出现的不均匀扩大和非洛尔表谱或偏峰组分。这些残留物的最大值(以下称为截止峰组分)趋于较大的恶性病变,表明恶性病变增加较宽。作者仅考虑每个病变中具有最高峰值峰值成分的体素,所以取决于病变大小的选定体素数。基于体素的方法将所有体素的偏峰组分与包含15个恶性和8个良性病变(产生<3900体血管凝胶)的所有病变的偏振率组分的大小和频率与基于病变的活组织检查确诊的诊断。通过比较恶性和良性病变中的平均偏峰组分幅度和频率来实现病变分类。 ROC曲线(AUC)下的区域被用作基于体素和基于病变的方法的优异图。结果:在从恶性和良性病变中区分体素的基于体素的任务中,非峰值幅度为0.88(95次置信区间0.84,0.91)。在区分恶性和良性病变的基于病变的任务中,平均非峰值幅度产生AUC 0.83(95次置信区间0.61,0.98)。结论:这些有前途的AUC值建议使用残差分析可能有较高的诊断工具和可用于增强现有的CADx方法,并允许乳腺癌的检测,而不需要注入造影剂的每个希斯图像体素水共振的这种分析。

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