Near-infrared (NIR) tomography breast image reconstruction with a priori structural information from MRI:: Algorithm development for reconstructing heterogeneities
Near-infrared (NIR) tomography breast image reconstruction with a priori structural information from MRI:: Algorithm development for reconstructing heterogeneities
复制标题
DOI：
10.1109/jstqe.2003.813304
复制
发表时间：
2003-03-01
影响因子：
4.9
通讯作者：
Paulsen, KD
中科院分区：
文献类型：
--
作者：
Brooksby, BA;Dehghani, H;Paulsen, KD
磁共振和近红外(MRI-NIR)相结合的成像方式可能会从非侵入性的同时测量中产生高分辨率的光学特性图。近红外(NIR)断层成像的主要缺点是由于组织对这些波长的高度散射特性而导致的低空间分辨率。MRI具有较高的分辨率，但特异性较低。在这项研究中，结合了MRI提供的先验结构信息的近红外图像重建算法被研究，试图优化恢复模拟的光学性质分布。评估高水平的组织异质性的影响，以确定将先验信息合并到一组真实的患者乳房图像中的局限性。我们假设在冠状乳腺MRI几何结构中，吸收系数(Mu(A))变化接近+/-40%，传输散射系数(Mu/(S))变化接近+/-20%。近红外断层扫描可以观察到肿瘤生长引起的组织病理变化，因此这里的目标是确定如何在组织高度异质性的情况下最好地量化这些基于肿瘤的对比区域。通过迭代算法中的各种约束条件，将组织的层状结构知识应用到重建中，使肿瘤光学特性的定量恢复从69%提高到74%，并实现了定位。也会有所改善。然而，只有当组织分布的真正异质性被包括在内时，才有可能准确地量化肿瘤区域。利用模型的区域结构得到的良好的mU(A)和mU/(S)的初始猜测，区域的量化达到真实值的99%，空间分辨率保持了与原始磁共振图像相似的值。
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 (mu(a)) variations near +/-40%, and transport scattering coefficient (mu/(s)) 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 mu(a) and mu/(s), 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.