Simulation of dosimetric consequences of 4D-CT-based motion margin estimation for proton radiotherapy using patient tumor motion data

被引:19
|
作者
Koybasi, Ozhan [1 ,2 ]
Mishra, Pankaj [2 ,3 ,4 ]
James, Sara St. [2 ,3 ,4 ]
Lewis, John H. [2 ,3 ,4 ]
Seco, Joao [1 ,2 ]
机构
[1] Massachusetts Gen Hosp, Dept Radiat Oncol, Boston, MA 02114 USA
[2] Harvard Univ, Sch Med, Boston, MA USA
[3] Brigham & Womens Hosp, Dept Radiat Oncol, Boston, MA 02115 USA
[4] Dana Farber Canc Inst, Boston, MA 02115 USA
来源
PHYSICS IN MEDICINE AND BIOLOGY | 2014年 / 59卷 / 04期
关键词
four-dimensional computed tomography; internal target volume; lung tumor motion; XCAT phantom; proton radiotherapy; 4D XCAT PHANTOM; LUNG-CANCER; RESPIRATORY MOTION; TARGET VOLUME; THERAPY; TRACKING; PROJECTIONS; ALGORITHM; SIZE;
D O I
10.1088/0031-9155/59/4/853
中图分类号
R318 [生物医学工程];
学科分类号
0831 ;
摘要
For the radiation treatment of lung cancer patients, four-dimensional computed tomography (4D-CT) is a common practice used clinically to image tumor motion and subsequently determine the internal target volume (ITV) from the maximum intensity projection (MIP) images. ITV, which is derived from short pre-treatment 4D-CT scan (<6 s per couch position), may not adequately cover the extent of tumor motion during the treatment, particularly for patients that exhibit a large respiratory variability. Inaccurate tumor localization may result in under-dosage of the tumor or over-dosage of the surrounding tissues. The purpose of this study is therefore to assess the degree of tumor under-dosage in case of regular and irregular breathing for proton radiotherapy using ITV-based treatment planning. We place a spherical lesion into a modified XCAT phantom that is also capable of producing 4D images based on irregular breathing, and move the tumor according to real tumor motion data, which is acquired over multiple days by tracking gold fiducial markers implanted into the lung tumors of patients. We derive ITVs by taking the union of all tumor positions during 6 s of tumor motion in the phantom using the first day patient tumor tracking data. This is equivalent to ITVs generated clinically from cine-mode 4D-CT MIP images. The treatment plans created for different ITVs are then implemented on dynamic phantoms with tumor motion governed by real tumor tracking data from consecutive days. By comparing gross tumor volume dose distribution on days of 'treatment' with the ITV dose distribution, we evaluate the deviation of the actually delivered dose from the predicted dose. Our results have shown that the proton treatment planning on ITV derived from pre-treatment cine-mode 4D-CT can result in under-dosage (dose covering 95% of volume) of the tumor by up to 25.7% over 3 min of treatment for the patient with irregular respiratory motion. Tumor under-dosage is less significant for the patient with relatively regular breathing. We have demonstrated that proton therapy using the pre-treatment 4D-CT based ITV method can lead to significant under-dosage of the tumor, highlighting the need for daily customization to generate a target volume that represents tumor positions during the treatment more accurately.
引用
收藏
页码:853 / 867
页数:15
相关论文
共 50 条
  • [31] Joint estimation of respiratory motion and activity in 4D PET using CT side information
    Jacobson, Matthew W.
    Fessler, Jeffrey A.
    2006 3RD IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: MACRO TO NANO, VOLS 1-3, 2006, : 275 - +
  • [32] Deriving Correlation of External Surface and Internal Tumor Motion Using 4D-CT Images
    Jiang, Y.
    Cervino, L.
    Li, R.
    Lewis, J.
    Jiang, S.
    MEDICAL PHYSICS, 2009, 36 (06)
  • [33] Lung tumor segmentation in 4D CT images using motion convolutional neural networks
    Momin, Shadab
    Lei, Yang
    Tian, Zhen
    Wang, Tonghe
    Roper, Justin
    Kesarwala, Aparna H.
    Higgins, Kristin
    Bradley, Jeffrey D.
    Liu, Tian
    Yang, Xiaofeng
    MEDICAL PHYSICS, 2021, 48 (11) : 7141 - 7153
  • [34] A novel approach for estimating lung tumor motion based on dynamic features in 4D-CT
    Gong, Ye-Jun
    Li, Yue-Ke
    Zhou, Rongrong
    Liang, Zhan
    Zhang, Yingying
    Cheng, Tingting
    Zhang, Zi-Jian
    COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 2024, 115
  • [35] Physiological and Biomechanical Model of Patient Specific Lung Motion Based on 4D CT Images
    Ladjal, Hamid
    Skendraoui, Nadir
    Giroux, Matthieu
    Touileb, Yazid
    Azencot, Joseph
    Shariat, Behzad
    Ladjal, Hamid
    Beuve, Michael
    Giraud, Philippe
    2015 8TH BIOMEDICAL ENGINEERING INTERNATIONAL CONFERENCE (BMEICON), 2015,
  • [36] 3D fluoroscopic image estimation using patient-specific 4DCBCT-based motion models
    Dhou, S.
    Hurwitz, M.
    Mishra, P.
    Cai, W.
    Rottmann, J.
    Li, R.
    Williams, C.
    Wagar, M.
    Berbeco, R.
    Ionascu, D.
    Lewis, J. H.
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (09): : 3807 - 3824
  • [37] Analysis of free breathing motion using artifact reduced 4D CT image data
    Ehrhardt, Jan
    Werner, Rene
    Frenzel, Thorsten
    Lu, Wei
    Low, Daniel
    Handels, Heinz
    MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3, 2007, 6512
  • [38] The study on the impact of AAA wall motion on the hemodynamics based on 4D CT image data
    Peng, Chen
    He, Wei
    Huang, Xingsheng
    Ma, Jun
    Yuan, Tong
    Shi, Yun
    Wang, Shengzhang
    FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, 2023, 11
  • [39] Image quality enhancement using motion estimation in 4D CT images of lung cancer patients
    Wolthaus, J.
    Sonke, J.
    van Herk, M.
    Zijp, L.
    Lebesque, J.
    Damen, E.
    RADIOTHERAPY AND ONCOLOGY, 2007, 84 : S110 - S110
  • [40] A robust proton treatment planning strategy to account the diaphragm motion for the esophagus cancer using 4D CT
    Pan, X.
    Zhang, X.
    Li, Y.
    Mohan, R.
    MEDICAL PHYSICS, 2007, 34 (06) : 2409 - 2410