Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer
Preliminary assessment of dispersion versus absorption analysis of high spectral and spatial resolution magnetic resonance images in the diagnosis of breast cancer
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DOI：
10.1117/1.jmi.2.2.024502
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发表时间：
2015-04-01
影响因子：
2.4
通讯作者：
Giger, Maryellen L.
中科院分区：
文献类型：
--
作者：
Weiss, William A.;Medved, Milica;Giger, Maryellen L.
水共振线形观察乳腺病变成像与高光谱和空间分辨率（HiSS）磁共振成像已被证明包含诊断有用的非洛伦兹成分。这项工作的目的是更新以前的乳腺病变诊断方法，包括相位校正的吸收和色散光谱。该更新包括有关复杂水共振形状的信息，这可以提高计算机辅助诊断乳腺病变分类方案的性能。通过将频谱的真实的分量对虚分量的曲线图与完美的复洛伦兹频谱的曲线图进行比较来表征在复杂的乳腺病变水共振频谱中观察到的非洛伦兹特性，这是一种“色散对吸收”（DISPA）分析技术。所观察到的光谱的形状的失真指示潜在的生理变化，其已被证明与恶性肿瘤相关。每个病变体素中的这些谱形状失真通过将DISPA半径与所有傅立叶分量上的理想复洛伦兹谱的偏差相加来量化，从而产生“总径向差”（TRD）。我们将我们的分析限制在每个病变中具有最大TRD的体素。考虑的体素数量取决于病变大小。TRD用于对来自15个恶性病变和8个良性病变的体素进行分类（类似于体素消除后的2400个体素）。评价了每个病变内TRD的平均值和方差的病变辨别性能。受试者工作特征曲线下面积（ROC AUC）用于评估基于体素和病灶的区分方法在区分恶性和良性的任务。在区分体素与恶性和良性病变的任务中，TRD产生的AUC为0.89（95%置信区间[0.84，0.91]）。在区分恶性和良性病变的任务中，平均径向差异产生的AUC为0.90（95%置信区间[0.71，1.00]），径向差异的方差产生的AUC为0.84（95%置信区间[0.61，0.99]）。我们已经应用了DISPA光谱分析方法HiSS数据，以确定和量化显示非洛伦兹特征的乳腺病变的体素。我们已经表明，基于从HiSS采集获得的吸收和色散光谱数据的乳腺病变分类方案可能优于基于单个离峰分量分析的类似分类器，因为它使用整个光谱的形状细节而不是单个光谱位置处的幅度。(C)2015年，美国光电仪器工程师学会（SPIE）
Water resonance lineshapes observed in breast lesions imaged with high spectral and spatial resolution (HiSS) magnetic resonance imaging have been shown to contain diagnostically useful non-Lorentzian components. The purpose of this work is to update a previous method of breast lesion diagnosis by including phase-corrected absorption and dispersion spectra. This update includes information about the shape of the complex water resonance, which could improve the performance of a computer-aided diagnosis breast lesion classification scheme. The non-Lorentzian characteristics observed in complex breast lesion water resonance spectra are characterized by comparing a plot of the real versus imaginary components of the spectrum to that of a perfect complex Lorentzian spectrum, a "dispersion versus absorption" (DISPA) analysis technique. Distortion in the shape of the observed spectra indicates underlying physiologic changes, which have been shown to be correlated with malignancy. These spectral shape distortions in each lesion voxel are quantified by summing the deviations in DISPA radius from an ideal complex Lorentzian spectrum over all Fourier components, yielding a "total radial difference" (TRD). We limited our analysis to those voxels in each lesion with the largest TRD. The number of voxels considered was dependent on the lesion size. The TRD was used to classify voxels from 15 malignant and 8 benign lesions (similar to 2400 voxels after voxel elimination). Lesion discrimination performance was evaluated for both the average and variance of the TRD within each lesion. Area under the receiver operating characteristic curve (ROC AUC) was used to assess both the voxel-and lesion-based discrimination methods in the task of distinguishing between malignant and benign. In the task of distinguishing voxels from malignant and benign lesions, TRD yielded an AUC of 0.89 (95% confidence interval [0.84, 0.91]). In the task of distinguishing malignant from benign lesions, the average radial difference yielded an AUC of 0.90 (95% confidence interval [0.71, 1.00]) and the variance in the radial difference yielded an AUC of 0.84 (95% confidence interval [0.61, 0.99]). We have applied the DISPA spectroscopic analysis method to HiSS data in order to identify and quantify voxels in breast lesions displaying non-Lorentzian characteristics. We have shown that a breast lesion classification scheme based on the absorption and dispersion spectral data obtained from HiSS acquisitions may outperform a similar classifier based on single off-peak component analysis, as it uses shape details of the entire spectrum instead of the magnitude at a single spectral location. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)