Hoffmann Nico, Weidner Florian, Urban Peter, Meyer Tobias, Schnabel Christian, Radev Yordan, Schackert Gabriele, Petersohn Uwe, Koch Edmund, Gumhold Stefan, Steiner Gerald, Kirsch Matthias
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Biomed Tech (Berl). 2017 Nov 27;62(6):599-607. doi: 10.1515/bmt-2016-0075.
Multimodal medical image fusion combines information of one or more images in order to improve the diagnostic value. While previous applications mainly focus on merging images from computed tomography, magnetic resonance imaging (MRI), ultrasonic and single-photon emission computed tomography, we propose a novel approach for the registration and fusion of preoperative 3D MRI with intraoperative 2D infrared thermography. Image-guided neurosurgeries are based on neuronavigation systems, which further allow us track the position and orientation of arbitrary cameras. Hereby, we are able to relate the 2D coordinate system of the infrared camera with the 3D MRI coordinate system. The registered image data are now combined by calibration-based image fusion in order to map our intraoperative 2D thermographic images onto the respective brain surface recovered from preoperative MRI. In extensive accuracy measurements, we found that the proposed framework achieves a mean accuracy of 2.46 mm.
多模态医学图像融合通过合并一个或多个图像的信息来提高诊断价值。虽然先前的应用主要集中于融合计算机断层扫描、磁共振成像（MRI）、超声和单光子发射计算机断层扫描的图像，但我们提出了一种用于术前3D MRI与术中2D红外热成像的配准和融合的新方法。图像引导神经外科手术基于神经导航系统，该系统进一步允许我们跟踪任意相机的位置和方向。由此，我们能够将红外相机的2D坐标系与3D MRI坐标系相关联。现在，通过基于校准的图像融合将配准后的图像数据进行合并，以便将我们的术中2D热成像图像映射到从术前MRI恢复的相应脑表面上。在广泛的精度测量中，我们发现所提出的框架实现了2.46毫米的平均精度。
