Department of Anatomy, Ajou University School of Medicine, Suwon, Korea.
Department of Radiology, Ajou University School of Medicine, Suwon, Korea.
J Korean Med Sci. 2019 Sep 2;34(34):e218. doi: 10.3346/jkms.2019.34.e218.
The sectioned images of a cadaver head made from the Visible Korean project have been used for research and educational purposes. However, the image resolution is insufficient to observe detailed structures suitable for experts. In this study, advanced sectioned images with higher resolution were produced for the identification of more detailed structures.
The head of a donated female cadaver was scanned for 3 Tesla magnetic resonance images and diffusion tensor images (DTIs). After the head was frozen, the head was sectioned serially at 0.04-mm intervals and photographed repeatedly using a digital camera.
On the resulting 4,000 sectioned images (intervals and pixel size, 0.04 mm³; color depth, 48 bits color; a file size, 288 Mbytes), minute brain structures, which can be observed not on previous sectioned images but on microscopic slides, were observed. The voxel size of this study (0.04 mm³) was very minute compared to our previous study (0.1 mm³; resolution, 4,368 × 2,912) and Visible Human Project of the USA (0.33 mm³; resolution, 2,048 × 2,048). Furthermore, the sectioned images were combined with tractography of the DTIs to elucidate the white matter with high resolution and the actual color of the tissue.
The sectioned images will be used for diverse research, including the applications for the cross sectional anatomy and three-dimensional models for virtual experiments.
可视韩国项目制作的人体头颅分段图像已被用于研究和教育目的。然而，图像分辨率不足以观察适合专家的详细结构。在这项研究中，制作了具有更高分辨率的先进分段图像，以识别更详细的结构。
对一具捐赠女性尸体的头部进行 3 特斯拉磁共振成像和扩散张量成像（DTI）扫描。头部冷冻后，以 0.04 毫米的间隔连续切片，并使用数码相机重复拍摄。
在得到的 4000 张分段图像（间隔和像素大小为 0.04 毫米³；彩色深度为 48 位彩色；文件大小为 288 Mbytes）上，可以观察到以前的分段图像上无法观察到的微小脑结构，但在显微镜载玻片上可以观察到。与我们之前的研究（0.1 毫米³；分辨率为 4368×2912）和美国可视人计划（0.33 毫米³；分辨率为 2048×2048）相比，本研究的体素大小（0.04 毫米³）非常微小。此外，分段图像与 DTI 的示踪相结合，以高分辨率阐明白质和组织的实际颜色。
分段图像将用于各种研究，包括用于横断面解剖的应用和虚拟实验的三维模型。
