Azar Fred S, Lee Kijoon, Khamene Ali, Choe Regine, Corlu Alper, Konecky Soren D, Sauer Frank, Yodh Arjun G
Siemens Corporate Research, Department of Imaging and Visualization, 755 College Road East Princeton, New Jersy 08540, USA.
J Biomed Opt. 2007 Sep-Oct;12(5):051902. doi: 10.1117/1.2798630.
We present a novel methodology for combining breast image data obtained at different times, in different geometries, and by different techniques. We combine data based on diffuse optical tomography (DOT) and magnetic resonance imaging (MRI). The software platform integrates advanced multimodal registration and segmentation algorithms, requires minimal user experience, and employs computationally efficient techniques. The resulting superposed 3-D tomographs facilitate tissue analyses based on structural and functional data derived from both modalities, and readily permit enhancement of DOT data reconstruction using MRI-derived a-priori structural information. We demonstrate the multimodal registration method using a simulated phantom, and we present initial patient studies that confirm that tumorous regions in a patient breast found by both imaging modalities exhibit significantly higher total hemoglobin concentration (THC) than surrounding normal tissues. The average THC in the tumorous regions is one to three standard deviations larger than the overall breast average THC for all patients.
我们提出了一种新颖的方法，用于组合在不同时间、不同几何形状以及通过不同技术获取的乳腺图像数据。我们基于漫射光学断层扫描（DOT）和磁共振成像（MRI）对数据进行组合。该软件平台集成了先进的多模态配准和分割算法，所需用户经验极少，并采用了计算效率高的技术。由此产生的叠加三维断层图像有助于基于从两种模态获得的结构和功能数据进行组织分析，并且能够利用MRI衍生的先验结构信息轻松增强DOT数据重建。我们使用模拟体模演示了多模态配准方法，并展示了初步的患者研究，这些研究证实，两种成像模态在患者乳腺中发现的肿瘤区域的总血红蛋白浓度（THC）明显高于周围正常组织。所有患者肿瘤区域的平均THC比整个乳腺的平均THC大1至3个标准差。
