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首页> 外文期刊>IEEE journal of selected topics in quantum electronics >Near-infrared (NIR) tomography breast image reconstruction with a priori structural information from MRI: algorithm development for reconstructing heterogeneities
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Near-infrared (NIR) tomography breast image reconstruction with a priori structural information from MRI: algorithm development for reconstructing heterogeneities

机译：利用MRI的先验结构信息进行近红外（NIR）层析成像乳房图像重建：重建异质性的算法开发

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摘要

A combined magnetic resonance and near-infrared (MRI-NIR) imaging modality can potentially yield high resolution maps of optical properties from noninvasive simultaneous measurement. The main disadvantage of near-infrared (NIR) tomography lies in the low spatial resolution resulting from the highly scattering nature of tissue for these wavelengths. MRI has achieved high resolution, but suffers from low specificity. In this study, NIR image reconstruction algorithms that incorporate a priori structural information provided by MRI are investigated in an attempt to optimize recovery of a simulated optical property distribution. The effect of high levels of tissue heterogeneity are evaluated to determine the limitations of incorporating prior information into a realistic set of patient breast images. We assume absorption coefficient (Μa) variations near ±40%, and transport scattering coefficient (Μs/) variations near ±20%, in a coronal breast MRI geometry. Changes in tissue pathology due to tumor growth can be observed with NIR tompgraphy, and so the goal here is to determine how best to quantify these tumor-based contrast regions within the presence of high tissue heterogeneity. By applying knowledge of tissue's layered structure in reconstruction through various constraints in the iterative algorithm, quantitative recovery of the tumor optical properties improves from 69% to 74%, and localization improves as well. However, only when the true heterogeneity of the tissue distribution was included was accurate quantification of the tumor region possible. Using a good initial guess of Μa and Μs/, derived from the regional structure of the model, quantification of the region reaches 99% of the true value, and spatial resolution retains a similar value to the original MRI image.
机译：磁共振和近红外（MRI-NIR）成像技术相结合可以潜在地从非侵入式同时测量中获得高分辨率的光学特性图。近红外（NIR）层析成像的主要缺点在于，由于组织对于这些波长的高度散射性，导致空间分辨率较低。 MRI已实现高分辨率，但特异性低。在这项研究中，研究结合了MRI提供的先验结构信息的NIR图像重建算法，以试图优化模拟光学特性分布的恢复。评估组织异质性高水平的影响，以确定将先验信息合并到一组现实的患者乳房图像中的局限性。我们假设在冠状乳房MRI几何结构中吸收系数（Μa）的变化接近±40％，传输散射系数（μs/）的变化接近±20％。 NIR断层扫描可以观察到由于肿瘤生长引起的组织病理学变化，因此，这里的目标是确定在存在高度组织异质性的情况下如何最好地量化这些基于肿瘤的对比区域。通过在迭代算法中通过各种约束条件应用组织的分层结构知识进行重建，肿瘤光学特性的定量恢复率将从69％提高到74％，并且定位也得到改善。然而，只有当包括组织分布的真正异质性时，才可能对肿瘤区域进行精确定量。使用从模型的区域结构得出的Ma和Ms /的良好初始猜测，该区域的量化将达到真实值的99％，并且空间分辨率保留与原始MRI图像相似的值。

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