Super-resolution head and neck MRA using deep machine learning.
Super-resolution head and neck MRA using deep machine learning.
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DOI：
10.1002/mrm.28738
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发表时间：
2021-07
影响因子：
3.3
通讯作者：
Edelman RR
中科院分区：
文献类型：
--
作者：
Koktzoglou I;Huang R;Ankenbrandt WJ;Walker MT;Edelman RR
探讨基于深度学习的超分辨率（SR）重建应用于头颈部非增强磁共振血管造影（MRA）的可行性。8名受试者（7名健康志愿者，1名患者）在3特斯拉下获得了头颈部的高分辨率3D薄板层叠星静止间隔切片选择（QISS） MRA。高分辨率地真MRA数据在切片编码方向的空间分辨率降低了2 ~ 6倍。应用了四种深度神经网络（DNN） SR重建，其中两种基于U-Net架构（2D和3D），两种（2D和3D）由带有残差连接的串行卷积组成。使用Dice相似系数（DSC）、结构相似指数（SSIM）、动脉直径和动脉清晰度测量将SR图像与地面真实高分辨率数据进行比较。最佳DNN SR重建的图像复查由两位经验丰富的神经放射学家完成。高达2倍和4倍低分辨率（LR）输入体积的DNN SR提供的图像与颅内和颅外动脉段的原始高分辨率基底真值体积相似，并且相对于LR体积改善了DSC、SSIM、动脉直径和动脉清晰度（P<0.001）。三维DNN SR重建优于二维DNN SR重建。根据两位神经放射学家的研究，3D DNN SR重建在LR输入体积方面持续改善了图像质量（P<0.001）。基于dnn的3D头颈部QISS MRA的SR重建可以将颈部血管的采集时间减少4倍，而无需相应地牺牲空间分辨率。
To probe the feasibility of deep learning-based super-resolution (SR) reconstruction applied to nonenhanced magnetic resonance angiography (MRA) of the head and neck. High-resolution 3D thin-slab stack-of-stars quiescent interval slice selective (QISS) MRA of the head and neck was obtained in 8 subjects (7 healthy volunteers, 1 patient) at 3 Tesla. The spatial resolution of high-resolution ground-truth MRA data in the slice-encoding direction was reduced by factors of 2 to 6. Four deep neural network (DNN) SR reconstructions were applied, with two based on U-Net architectures (2D and 3D) and two (2D and 3D) consisting of serial convolutions with a residual connection. SR images were compared to ground-truth high-resolution data using Dice similarity coefficient (DSC), structural similarity index (SSIM), arterial diameter, and arterial sharpness measurements. Image review of the optimal DNN SR reconstruction was done by two experienced neuroradiologists. DNN SR of up to 2-fold and 4-fold lower-resolution (LR) input volumes provided images that resembled those of the original high-resolution ground-truth volumes for intracranial and extracranial arterial segments, and improved DSC, SSIM, arterial diameters, and arterial sharpness relative to LR volumes (P<0.001). 3D DNN SR outperformed 2D DNN SR reconstruction. According to two neuroradiologists, 3D DNN SR reconstruction consistently improved image quality with respect to LR input volumes (P<0.001). DNN-based SR reconstruction of 3D head and neck QISS MRA offers the potential for up to 4-fold reduction in acquisition time for neck vessels without the need to commensurately sacrifice spatial resolution.
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影响因子：
3.3
作者：
Chaudhari AS;Fang Z;Kogan F;Wood J;Stevens KJ;Gibbons EK;Lee JH;Gold GE;Hargreaves BA
通讯作者：
Hargreaves BA
DOI：
10.1016/1049-9652(91)90045-l
发表时间：
1991-05-01
期刊：
CVGIP-GRAPHICAL MODELS AND IMAGE PROCESSING
影响因子：
--
作者：
IRANI, M;PELEG, S
通讯作者：
PELEG, S
影响因子：
5.5
作者：
Glockner, JF;Hu, HH;King, K
通讯作者：
King, K
影响因子：
3.3
作者：
Koktzoglou, Ioannis;Edelman, Robert R.
通讯作者：
Edelman, Robert R.
影响因子：
8.3
作者：
Saver, JL
通讯作者：
Saver, JL