Shin Chun-Yuan, Chao Yi-Ping, Kuo Li-Wei, Chang Yi-Peng Eve, Weng Jun-Cheng
Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan.
Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Front Neuroinform. 2023 Feb 16;17:956600. doi: 10.3389/fninf.2023.956600. eCollection 2023.
Understanding neural connections facilitates the neuroscience and cognitive behavioral research. There are many nerve fiber intersections in the brain that need to be observed, and the size is between 30 and 50 nanometers. Improving image resolution has become an important issue for mapping the neural connections non-invasively. Generalized q-sampling imaging (GQI) was used to reveal the fiber geometry of straight and crossing. In this work, we attempted to achieve super-resolution with a deep learning method on diffusion weighted imaging (DWI).
A three-dimensional super-resolution convolutional neural network (3D SRCNN) was utilized to achieve super-resolution on DWI. Then, generalized fractional anisotropy (GFA), normalized quantitative anisotropy (NQA), and the isotropic value of the orientation distribution function (ISO) mapping were reconstructed using GQI with super-resolution DWI. We also reconstructed the orientation distribution function (ODF) of brain fibers using GQI.
With the proposed super-resolution method, the reconstructed DWI was closer to the target image than the interpolation method. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were also significantly improved. The diffusion index mapping reconstructed by GQI also had higher performance. The ventricles and white matter regions were much clearer.
This super-resolution method can assist in postprocessing low-resolution images. With SRCNN, high-resolution images can be effectively and accurately generated. The method can clearly reconstruct the intersection structure in the brain connectome and has the potential to accurately describe the fiber geometry on a subvoxel scale.
了解神经连接有助于神经科学和认知行为研究。大脑中有许多需要观察的神经纤维交叉点，其大小在30到50纳米之间。提高图像分辨率已成为无创绘制神经连接的一个重要问题。广义q采样成像（GQI）被用于揭示直线和交叉纤维的几何形状。在这项工作中，我们尝试用深度学习方法在扩散加权成像（DWI）上实现超分辨率。
利用三维超分辨率卷积神经网络（3D SRCNN）在DWI上实现超分辨率。然后，使用具有超分辨率DWI的GQI重建广义分数各向异性（GFA）、归一化定量各向异性（NQA）和取向分布函数的各向同性值（ISO）映射。我们还使用GQI重建了脑纤维的取向分布函数（ODF）。
通过所提出的超分辨率方法，重建的DWI比插值方法更接近目标图像。峰值信噪比（PSNR）和结构相似性指数测量（SSIM）也有显著提高。由GQI重建的扩散指数映射也具有更高的性能。脑室和白质区域更加清晰。
这种超分辨率方法可以辅助低分辨率图像的后处理。利用SRCNN，可以有效且准确地生成高分辨率图像。该方法可以清晰地重建脑连接组中的交叉结构，并有可能在亚体素尺度上准确描述纤维几何形状。
