Estimation of High Framerate Digital Subtraction Angiography Sequences at Low Radiation Dose.

Estimation of High Framerate Digital Subtraction Angiography Sequences at Low Radiation Dose.
复制标题

低辐射剂量下高帧率数字减影血管造影序列的估计。

DOI:
10.1007/978-3-030-87231-1_17
复制
发表时间:
2021-09
期刊:
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
影响因子:
--
通讯作者:
Frisken S
Frisken S
中科院分区:
其他
文献类型:
--
作者:
Haouchine N;Juvekar P;Xiong X;Luo J;Kapur T;Du R;Golby A;Frisken S

文献摘要

参考文献

相似文献

数字减影血管造影(DSA)提供高分辨率的动脉和静脉血流图像序列,被认为是神经血管干预中脑血管解剖可视化的金标准。然而,采集帧率通常限制在1-3 fps,以减少辐射暴露,因此DSA序列经常遭受频闪效应。我们提出了第一种方法,允许从低帧率采集产生高帧率DSA序列,消除这些伪影,而不增加患者的辐射暴露。我们的方法使用相位感知卷积神经网络合成新的中间帧。这个网络解释了由于血管几何形状和造影剂的初始速度而导致的非线性血流过程。我们的方法优于现有的方法,并在人脑的几个低帧率DSA序列上进行了测试,结果显示高达17 fps的序列具有平滑和连续的对比度流,没有闪烁伪影。
Digital Subtraction Angiography (DSA) provides high resolution image sequences of blood flow through arteries and veins and is considered the gold standard for visualizing cerebrovascular anatomy for neurovascular interventions. However, acquisition frame rates are typically limited to 1-3 fps to reduce radiation exposure, and thus DSA sequences often suffer from stroboscopic effects. We present the first approach that permits generating high frame rate DSA sequences from low frame rate acquisitions eliminating these artifacts without increasing the patient’s exposure to radiation. Our approach synthesizes new intermediate frames using a phase-aware Convolutional Neural Network. This network accounts for the non-linear blood flow progression due to vessel geometry and initial velocity of the contrast agent. Our approach out-performs existing methods and was tested on several low frame rate DSA sequences of the human brain resulting in sequences of up to 17 fps with smooth and continuous contrast flow, free of flickering artifacts.
DOI: 10.1109/tpami.2010.143
发表时间: 2011-03-01
影响因子: 23.6
作者:
Brox, Thomas;Malik, Jitendra
通讯作者: Malik, Jitendra
用于 DSA 脑血管分割的多尺度密集卷积神经网络
DOI: 10.1016/j.neucom.2019.10.035
发表时间: 2020-01-15
期刊: NEUROCOMPUTING
影响因子: 6
作者:
Meng, Cai;Sun, Kai;Liu, Lei
通讯作者: Liu, Lei
DOI: 10.3174/ajnr.a5963
发表时间: 2019-03-01
影响因子: 3.5
作者:
Hong, J. -S.;Kao, Y. -H.;Lin, C. -J.
通讯作者: Lin, C. -J.
DOI: 10.1136/neurintsurg-2013-010982
发表时间: 2015-02-01
影响因子: 4.8
作者:
Pearl, Monica S.;Torok, Collin;Gailloud, Philippe
通讯作者: Gailloud, Philippe
DOI: 10.3171/2014.7.focus14160
发表时间: 2014-09-01
影响因子: 4.1
作者:
Theofanis, Thana;Chalouhi, Nohra;Tjoumakaris, Stavropoula
通讯作者: Tjoumakaris, Stavropoula