A Deep Learning Framework for Image Super-Resolution for Late Gadolinium Enhanced Cardiac MRI.
A Deep Learning Framework for Image Super-Resolution for Late Gadolinium Enhanced Cardiac MRI.
复制标题
用于晚期Gd增强心脏MRI图像超分辨率的深度学习框架。
DOI：
10.23919/cinc53138.2021.9662790
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发表时间：
2021-09
期刊：
影响因子：
--
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中科院分区：
文献类型：
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心脏磁共振成像(MRI)为3D图像提供了高分辨率的面内信息，但由于在分辨率、图像采集时间和信噪比之间的权衡，它们的穿透平面分辨率较低。这导致了各向异性的3D图像，这可能导致诊断困难，特别是在晚期Gd增强(LGE)心脏MRI，这是定位各种心血管疾病(如心肌梗死和心房颤动)心肌纤维化程度的参考成像方式。为了解决这个问题，我们提出了一种基于自监督深度学习的方法来提高LGE MRI图像的穿透平面分辨率。我们在随机提取的短轴LGE MRI图像块上训练卷积神经网络(CNN)模型，训练后的CNN模型用于利用从高分辨率面内数据中学习的信息来提高贯穿平面的分辨率。我们在2018年心房分割挑战中提供的LGE MRI数据集上进行了实验。我们提出的方法在训练CNN模型时，平均峰值信噪比(PSNR)分别为36.99和35.92，平均结构相似性指数(SSIM)分别为0.9和0.84。
Cardiac magnetic resonance imaging (MRI) provides 3D images with high-resolution in-plane information, however, they are known to have low through-plane resolution due to the trade-off between resolution, image acquisition time and signal-to-noise ratio. This results in anisotropic 3D images which could lead to difficulty in diagnosis, especially in late gadolinium enhanced (LGE) cardiac MRI, which is the reference imaging modality for locating the extent of myocardial fibrosis in various cardiovascular diseases like myocardial infarction and atrial fibrillation. To address this issue, we propose a self-supervised deep learning-based approach to enhance the through-plane resolution of the LGE MRI images. We train a convolutional neural network (CNN) model on randomly extracted patches of short-axis LGE MRI images and this trained CNN model is used to leverage the information learnt from the high-resolution in-plane data to improve the through-plane resolution. We conducted experiments on LGE MRI dataset made available through the 2018 atrial segmentation challenge. Our proposed method achieved a mean peak signal-to-noise-ratio (PSNR) of 36.99 and 35.92 and a mean structural similarity index measure (SSIM) of 0.9 and 0.84 on training the CNN model using low-resolution images downsampled by a scale factor of 2 and 4, respectively.
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影响因子：
10.9
作者：
Xiong, Zhaohan;Xia, Qing;Zhao, Jichao
通讯作者：
Zhao, Jichao
影响因子：
5.7
作者：
Ebner, Michael;Wang, Guotai;Vercauteren, Tom
通讯作者：
Vercauteren, Tom
影响因子：
6.4
作者：
Steeden, Jennifer A.;Quail, Michael;Muthurangu, Vivek
通讯作者：
Muthurangu, Vivek
DOI：
10.1161/circep.113.000689
发表时间：
2014-02
期刊：
Circulation. Arrhythmia and electrophysiology
影响因子：
--
作者：
McGann C;Akoum N;Patel A;Kholmovski E;Revelo P;Damal K;Wilson B;Cates J;Harrison A;Ranjan R;Burgon NS;Greene T;Kim D;Dibella EV;Parker D;Macleod RS;Marrouche NF
通讯作者：
Marrouche NF
影响因子：
10.6
作者：
Bustin, Aurelien;Voilliot, Damien;Odille, Freddy
通讯作者：
Odille, Freddy