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

被引:4
作者
Haouchine, Nazim [1 ,2 ]
Juvekar, Parikshit [1 ,2 ]
Xiong, Xin [2 ,3 ]
Luo, Jie [1 ,2 ]
Kapur, Tina [1 ,2 ]
Du, Rose [1 ,2 ]
Golby, Alexandra [1 ,2 ]
Frisken, Sarah [1 ,2 ]
机构
[1] Harvard Med Shcool, Boston, MA 02115 USA
[2] Brigham & Womens Hosp, 75 Francis St, Boston, MA 02115 USA
[3] Columbia Univ, New York, NY USA
来源
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VI | 2021年 / 12906卷
关键词
Biomedical image synthesis; Digital subtraction angiography; Video interpolation; Convolutional neural networks;
D O I
10.1007/978-3-030-87231-1_17
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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 outperforms 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.
引用
收藏
页码:171 / 180
页数:10
相关论文
共 19 条
  • [1] Balter S., 2014, JAJR AM J ROENTGENOL, V3, P234, DOI [10.2214/AJR.13.11041, DOI 10.2214/AJR.13.11041]
  • [2] Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
    Brox, Thomas
    Malik, Jitendra
    [J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2011, 33 (03) : 500 - 513
  • [3] Chng S.M., 2007, NEUROL CLIN NEUROSCI, P595
  • [4] Deep Residual Learning for Image Recognition
    He, Kaiming
    Zhang, Xiangyu
    Ren, Shaoqing
    Sun, Jian
    [J]. 2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, : 770 - 778
  • [5] Herbst Evan., 2009, Occlusion Reasoning for Temporal Interpolation Using Optical Flow"
  • [6] Validating the Automatic Independent Component Analysis of DSA
    Hong, J. -S.
    Kao, Y. -H.
    Chang, F. -C.
    Lin, C. -J.
    [J]. AMERICAN JOURNAL OF NEURORADIOLOGY, 2019, 40 (03) : 540 - 542
  • [7] Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
    Jiang, Huaizu
    Sun, Deqing
    Jampani, Varun
    Yang, Ming-Hsuan
    Learned-Miller, Erik
    Kautz, Jan
    [J]. 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 9000 - 9008
  • [8] Perceptual Losses for Real-Time Style Transfer and Super-Resolution
    Johnson, Justin
    Alahi, Alexandre
    Li Fei-Fei
    [J]. COMPUTER VISION - ECCV 2016, PT II, 2016, 9906 : 694 - 711
  • [9] Junheum Park, 2020, Computer Vision - ECCV 2020. 16th European Conference. Proceedings. Lecture Notes in Computer Science (LNCS 12359), P109, DOI 10.1007/978-3-030-58568-6_7
  • [10] Kingma D.P., 2015, Adam: A method for stochastic optimization