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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成像的乳腺病变中水共振信号的残留分析:一项初步研究

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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)产生有关可疑病变的局部环境的信息。先前的工作证明了HiSS的优点(完全抑制脂肪,改善图像对比度,无需使用造影剂等),从而导致了对以乳腺病变分类为目的的水共振线形的初步研究。这项研究的目的是研究定量成像生物标志物,该标志物表征HiSS MRI数据集中水共振的非洛伦兹成分,用于计算机辅助诊断(CADx)。方法:通过从水峰光谱中减去洛伦兹拟合并评估差异谱或残差,分析乳腺HiSS图像质子光谱在水共振中看到的非均匀加宽和非洛伦兹或非峰分量。这些残留物的最大值(以下称为非高峰成分)在恶性病变中趋于较大,表明恶性病变的增宽增加。作者只考虑了每个病变中非峰成分数量最多的体素,所选体素的数量取决于病变的大小。我们基于体素的方法比较了所有病灶中所有体素的非峰成分的量级和频率,该数据库根据病灶活检确认的诊断结果,包括15个恶性病灶和8个良性病灶(约3900个体素)。通过比较恶性和良性病变的平均非高峰成分幅度和频率来完成病变分类。 ROC曲线下的面积(AUC)用作基于体素和基于病变的方法的优值。结果:在将体素与恶性和良性病变区分开的基于体素的任务中,非高峰幅度产生的AUC为0.88(95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,平均非高峰量产生的AUC为0.83(95置信区间0.61、0.98)。结论:这些有希望的AUC值表明,使用残差分析对每个HiSS图像体素中的水共振进行分析可能具有较高的诊断效用,并可用于增强当前的CADx方法并无需注射造影剂即可检测乳腺癌。

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