Level Set based Real-time Anatomy Tracking

被引:0
|
作者
Liu, Wenyang [1 ]
Ruan, Dan [1 ,2 ]
机构
[1] Univ Calif Los Angeles, Dept Bioengn, Los Angeles, CA 90024 USA
[2] Univ Calif Los Angeles, Dept Radiat Oncol, Los Angeles, CA 90095 USA
来源
2012 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE RECORD (NSS/MIC) | 2012年
关键词
MOTION;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Real-time motion estimation is always challenging especially under MR imaging modality due to its low SNR. This study proposes a novel level set based method to estimate the anatomical boundary motion in real time. We construct a correspondence map on the spatial-temporal domain and advect it with the underlying dynamic level set function that delineates the organ of interest. In real-time, such correspondence map is estimated and moving trajectories for the anatomy boundaries are evolved. Unlike conventional level set-based registration, where velocity is assumed to be normal, we solve for the velocity with tangential component by minimizing an elasticity energy. The proposed method was tested with renal MR image sequences under both synthetic and physiological motion: the former generated by artificially translating a static reference MR image and the latter acquired with EPI pulse sequence under heavy breathing. With the new scheme to handle tangential velocity component, the proposed method is capable to estimate motion with good accuracy and/or physiological implication. In the synthetic motion test where ground-truth velocity is accessible, it significantly improved the accuracy of the motion estimation; in the real-time MR sequence test, the reconstructed motion field is smooth and exhibits periodic temporal behavior in accordance with respiratory motion and spatial variation in agreement with dynamics for renal physiology. In conclusion, the proposed scheme improves the estimation accuracy and provides insights about the spatial-temporal characteristics of anatomical and physiological motion. Such information will be incorporated into real-time motion adaptive radiotherapy to improve cancer target coverage and normal tissue sparing.
引用
收藏
页码:3898 / 3901
页数:4
相关论文
共 50 条
  • [21] Error elimination method in moving target tracking in real-time augmented reality
    Shi, Yingjie
    Zhao, Zijian
    JOURNAL OF REAL-TIME IMAGE PROCESSING, 2021, 18 (02) : 295 - 305
  • [22] Robust real-time ship detection and tracking for visual surveillance of cage aquaculture
    Hu, Wu-Chih
    Yang, Ching-Yu
    Huang, Deng-Yuan
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2011, 22 (06) : 543 - 556
  • [23] TraSe: Real-time two-dimensional ball tracking and control system
    Mednis, M.
    Vilums, S.
    Stalidzans, E.
    INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING EDUCATION, 2016, 53 (02) : 145 - 155
  • [24] Application of Ultrasound Image Tracking Algorithm for Real-Time Diaphragmatic Excursion Measurement
    Kuo, Chan-Yang
    Chuang, Ho-Chiao
    Zhou, Yi-Liang
    Wu, Yu-Peng
    Wang, Jia-Chang
    Kuo, Chia-Chun
    Jeng, Shiu-Chen
    Kao, Hung-Wen
    Huang, Ming-Yuan
    Chiou, Jeng-Fong
    Liao, Ai-Ho
    JOURNAL OF MEDICAL AND BIOLOGICAL ENGINEERING, 2018, 38 (04) : 678 - 684
  • [25] Real-time tumour tracking in particle therapy: technological developments and future perspectives
    Riboldi, Marco
    Orecchia, Roberto
    Baroni, Guido
    LANCET ONCOLOGY, 2012, 13 (09) : E383 - E391
  • [26] Real-time Eye Tracking and Event Identification Techniques for Smart TV applications
    Chen, Yen-Lin
    Chiang, Chuan-Yen
    Yu, Chao-Wei
    Sun, Wei-Chen
    Yuan, Shyan-Ming
    2014 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS - TAIWAN (ICCE-TW), 2014,
  • [27] Part-Based Pedestrian Detection and Feature-Based Tracking for Driver Assistance: Real-Time, Robust Algorithms, and Evaluation
    Prioletti, Antonio
    Mogelmose, Andreas
    Grisleri, Paolo
    Trivedi, Mohan Manubhai
    Broggi, Alberto
    Moeslund, Thomas B.
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2013, 14 (03) : 1346 - 1359
  • [28] Improvement of tracking accuracy and stability by recursive image processing in real-time tumor-tracking radiotherapy system
    Miyamoto, Naoki
    Sutherland, Kenneth
    Suzuki, Ryusuke
    Matsuura, Taeko
    Toramatsu, Chie
    Takao, Seishin
    Nihongi, Hideaki
    Kinoshita, Rumiko
    Shimizu, Shinichi
    Onimaru, Rikiya
    Umegaki, Kikuo
    Shirato, Hiroki
    Ishikawa, Masayori
    MEDICAL IMAGING 2012: IMAGE-GUIDED PROCEDURES, ROBOTIC INTERVENTIONS, AND MODELING, 2012, 8316
  • [29] Real-Time Hand Gesture Tracking for Human-Computer Interface Based on Multi-Sensor Data Fusion
    Li, Jie
    Liu, Xiaofeng
    Wang, Zhelong
    Zhang, Tingting
    Qiu, Sen
    Zhao, Hongyu
    Zhou, Xu
    Cai, Huili
    Ni, Rongrong
    Cangelosi, Angelo
    IEEE SENSORS JOURNAL, 2021, 21 (23) : 26642 - 26654
  • [30] Real-time tumor tracking using fluoroscopic imaging with deep neural network analysis
    Hirai, Ryusuke
    Sakata, Yukinobu
    Tanizawa, Akiyuki
    Mori, Shinichiro
    PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS, 2019, 59 : 22 - 29