Baseline correction of a correlation model for improving the prediction accuracy of infrared marker-based dynamic tumor tracking

被引:8
|
作者
Akimoto, Mami [1 ]
Nakamura, Mitsuhiro [1 ]
Mukumoto, Nobutaka [1 ]
Yamada, Masahiro [1 ]
Tanabe, Hiroaki [2 ]
Ueki, Nami [1 ]
Kaneko, Shuji [1 ]
Matsuo, Yukinori [1 ]
Mizowaki, Takashi [1 ]
Kokubo, Masaki [2 ,3 ]
Hiraoka, Masahiro [1 ]
机构
[1] Kyoto Univ, Grad Sch Med, Dept Radiat Oncol & Image Appl Therapy, Kyoto 6068507, Japan
[2] Inst Biomed Res & Innovat, Div Radiat Oncol, Kobe, Hyogo, Japan
[3] Kobe City Med Ctr Gen Hosp, Div Radiat Oncol, Kobe, Hyogo, Japan
来源
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS | 2015年 / 16卷 / 02期
基金
日本科学技术振兴机构;
关键词
Vero4DRT; IR Tracking; correlation model; baseline drift; GUIDED RADIOTHERAPY SYSTEM; RESPIRATORY MOTION; RADIATION-THERAPY; IRRADIATION; MANAGEMENT;
D O I
10.1120/jacmp.v16i2.4896
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 ; 100207 ; 1009 ;
摘要
We previously found that the baseline drift of external and internal respiratory motion reduced the prediction accuracy of infrared (IR) marker-based dynamic tumor tracking irradiation (IR Tracking) using the Vero4DRT system. Here, we proposed a baseline correction method, applied immediately before beam delivery, to improve the prediction accuracy of IR Tracking. To perform IR Tracking, a four-dimensional (4D) model was constructed at the beginning of treatment to correlate the internal and external respiratory signals, and the model was expressed using a quadratic function involving the IR marker position (x) and its velocity (v), namely function F(x,v). First, the first 4D model, F-1st(x,v), was adjusted by the baseline drift of IR markers (BDIR) along the x-axis, as function F'(x,v). Next, BDdetect, that defined as the difference between the target positions indicated by the implanted fiducial markers (P-detect) and the predicted target positions with F'(x,v) (P-predict) was determined using orthogonal kV X-ray images at the peaks of the P-detect of the end-inhale and end-exhale phases for 10 s just before irradiation. F'(x,v) was corrected with BDdetect to compensate for the residual error. The final corrected 4D model was expressed as F-cor(x,v) = F-1st{(x-BDIR), v}-BDdetect. We retrospectively applied this function to 53 paired log files of the 4D model for 12 lung cancer patients who underwent IR Tracking. The 95th percentile of the absolute differences between P-detect and P-predict (vertical bar E-p vertical bar) was compared between F-1st(x,v) and F-cor(x,v). The median 95th percentile of vertical bar E-p vertical bar (units: mm) was 1.0, 1.7, and 3.5 for F-1st(x,v), and 0.6, 1.1, and 2.1 for F-cor(x,v) in the left-right, anterior-posterior, and superior-inferior directions, respectively. Over all treatment sessions, the 95th percentile of vertical bar E-p vertical bar peaked at 3.2 mm using F-cor(x,v) compared with 8.4 mm using F-1st(x,v). Our proposed method improved the prediction accuracy of IR Tracking by correcting the baseline drift immediately before irradiation.
引用
收藏
页码:14 / 22
页数:9
相关论文
共 50 条
  • [41] Improving accuracy of land surface temperature prediction model based on deep-learning
    Choe, Yu-Jeong
    Yom, Jae-Hong
    SPATIAL INFORMATION RESEARCH, 2020, 28 (03) : 377 - 382
  • [42] An Urban Air Quality Prediction Model based on Dynamic Correlation of Influencing Factors
    Li, Lin
    Mai, Yunqi
    Chu, Yu
    Tao, Xiaohui
    Yong, Jiaming
    PROCEEDINGS OF THE 2024 27 TH INTERNATIONAL CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK IN DESIGN, CSCWD 2024, 2024, : 3188 - 3193
  • [43] Improving the Tracking Accuracy of TDMA-Based Acoustic Indoor Positioning Systems Using a Novel Error Correction Method
    Cao, Shuai
    Chen, Xiang
    Zhang, Xu
    Chen, Xun
    IEEE SENSORS JOURNAL, 2022, 22 (06) : 5427 - 5436
  • [44] Refraction Correction Based on ATL03 Photon Parameter Tracking for Improving ICESat-2 Bathymetry Accuracy
    Chen, Li
    Xing, Shuai
    Zhang, Guoping
    Guo, Songtao
    Gao, Ming
    REMOTE SENSING, 2024, 16 (01)
  • [45] Improving the Accuracy of Ensemble Classifier Prediction model based on FLAME Clustering with Random Forest Algorithm
    Augusty, Seena Mary
    Izudheen, Sminu
    2013 THIRD INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING AND COMMUNICATIONS (ICACC 2013), 2013, : 269 - 273
  • [46] Improving Model-based Optical Proximity Correction accuracy using improved process data generation
    Lu, M
    King, D
    Liang, C
    Melvin, LS
    2ND INTERNATIONAL CONFERENCE ON ADVANCED OPTICAL MANUFACTURING AND TESTING TECHNOLOGIES: ADVANCED OPTICAL MANUFACTURING TECHNOLOGIES, 2006, 6149
  • [47] Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model
    Wenchuan, Wang
    Yanwei, Zhao
    Dongmei, Xu
    Yanghao, Hong
    JOURNAL OF HYDROLOGY, 2024, 643
  • [48] Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning
    Mingxing Xu
    Xianyao Chu
    Yesi Fu
    Changjiang Wang
    Shaohua Wu
    Environmental Earth Sciences, 2021, 80
  • [49] Improving the accuracy of soil organic carbon content prediction based on visible and near-infrared spectroscopy and machine learning
    Xu, Mingxing
    Chu, Xianyao
    Fu, Yesi
    Wang, Changjiang
    Wu, Shaohua
    ENVIRONMENTAL EARTH SCIENCES, 2021, 80 (08)
  • [50] Continuous dynamic clothing pressure prediction model based on human arm and accuracy characterization method
    Xie, Hong
    Zhang, Linwei
    Shen, Yunping
    Fangzhi Xuebao/Journal of Textile Research, 2024, 45 (07): : 150 - 158