A 3D Convolutional Neural Network with Gradient Guidance for Image Super-Resolution of Late Gadolinium Enhanced Cardiac MRI.
A 3D Convolutional Neural Network with Gradient Guidance for Image Super-Resolution of Late Gadolinium Enhanced Cardiac MRI.
复制标题
梯度引导的三维卷积神经网络用于晚期Gd增强心脏MRI的图像超分辨率
DOI：
10.1109/embc48229.2022.9871783
复制
发表时间：
2022-07
期刊：
影响因子：
--
通讯作者：
中科院分区：
文献类型：
--
作者：
在本文中，我们描述了一个3D卷积神经网络（CNN）框架来计算和生成超分辨率晚期钆增强（LGE）心脏磁共振成像（MRI）图像。提出的CNN框架由两个分支组成：一个超分辨率分支，以3D密集深度反投影网络（DBPN）为骨干，学习低分辨率LGE心脏体积到高分辨率LGE心脏体积的映射;一个梯度分支，学习低分辨率LGE心脏体积的梯度图到其高分辨率对应物的梯度图的映射。CNN的梯度分支向超分辨率分支提供额外的心脏结构信息，以生成结构上更准确的超分辨率LGE MRI图像。我们在2018年心房分割挑战数据集上进行了实验。所提出的CNN框架在分别以比例因子2和4下采样的低分辨率图像上训练模型时，平均峰值信噪比（PSNR）为30.91和25.66，平均结构相似性指数（SSIM）为0.91和0.75。
In this paper, we describe a 3D convolutional neural network (CNN) framework to compute and generate super-resolution late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI) images. The proposed CNN framework consists of two branches: a super-resolution branch with a 3D dense deep back-projection network (DBPN) as the backbone to learn the mapping of low-resolution LGE cardiac volumes to high-resolution LGE cardiac volumes, and a gradient branch that learns the mapping of the gradient map of low resolution LGE cardiac volumes to the gradient map of their high-resolution counterparts. The gradient branch of the CNN provides additional cardiac structure information to the super-resolution branch to generate structurally more accurate super-resolution LGE MRI images. We conducted our experiments on the 2018 atrial segmentation challenge dataset. The proposed CNN framework achieved a mean peak signal-to-noise ratio (PSNR) of 30.91 and 25.66 and a mean structural similarity index measure (SSIM) of 0.91 and 0.75 on training the model on low-resolution images downsampled by a scale factor of 2 and 4, respectively.