A Feasibility Study on Ribs as Anatomical Landmarks for Motion Tracking of Lung and Liver Tumors at External Beam Radiotherapy

At external beam radiotherapy for some tumors located at thorax region due to lack of information in gray scale fluoroscopic images tumor position determination is problematic. One of the clinical strategies is to implant clip as internal fiducial marker inside or near tumor to represent tumor position while the contrast of implanted clip is highly observable rather than tumor. As alternative, using natural anatomical landmarks located at thorax region of patient body is proposed to extract tumor position information without implanting clips that is invasive method with possible side effect. Among natural landmarks, ribs of rib-cage structure that result proper visualization at X-ray images may be optimal as representative for tumor motion. In this study, we investigated the existence of possible correlation between ribs as natural anatomical landmarks and various lung and liver tumors located at different sites as challenging issue. A simulation study was performed using data extracted from 4-dimensional extended cardiac-torso anthropomorphic phantom that is able to simulate motion effect of dynamic organs, as well. Several tumor sites with predefined distances originated from chosen ribs at anterior–posterior direction were simulated at 3 upper, middle, and lower parts of chest. Correlation coefficient between ribs and tumors was calculated to investigate the robustness of ribs as anatomical landmarks for tumor motion tracking. Moreover, a consistent correlation model was taken into account to track tumor motion with a rib as best candidate among selected ribs. Final results represent availability of using rib cage as anatomical landmark to track lung and liver tumors in a noninvasive way. Observations of our calculations showed a proper correlation between tumors and ribs while the degree of this correlation is changing depends on tumor site while lung tumors are more varied and complex with less correlation with ribs motion against liver tumors.

[1]  H. Kubo,et al.  Respiration gated radiotherapy treatment: a technical study. , 1996, Physics in medicine and biology.

[2]  D A Jaffray,et al.  The effects of intra-fraction organ motion on the delivery of dynamic intensity modulation. , 1998, Physics in medicine and biology.

[3]  Shinichi Shimizu,et al.  Real-time tumour-tracking radiotherapy , 1999, The Lancet.

[4]  M. V. van Herk,et al.  Physical aspects of a real-time tumor-tracking system for gated radiotherapy. , 2000, International journal of radiation oncology, biology, physics.

[5]  Fluoroscopic real-time tumor-tracking radiation treatment (RTRT) can reduce internal margin (IM) and set-up margin (SM) of planning target volume (PTV) for lung tumors , 2000 .

[6]  R Mohan,et al.  Determining parameters for respiration-gated radiotherapy. , 2001, Medical physics.

[7]  G Starkschall,et al.  Respiratory-driven lung tumor motion is independent of tumor size, tumor location, and pulmonary function. , 2001, International journal of radiation oncology, biology, physics.

[8]  M. V. van Herk,et al.  Precise and real-time measurement of 3D tumor motion in lung due to breathing and heartbeat, measured during radiotherapy. , 2002, International journal of radiation oncology, biology, physics.

[9]  Harry Keller,et al.  Application of the spirometer in respiratory gated radiotherapy. , 2003, Medical physics.

[10]  Martin J Murphy,et al.  Tracking moving organs in real time. , 2004, Seminars in radiation oncology.

[11]  Shinichi Shimizu,et al.  Intrafractional tumor motion: lung and liver. , 2004, Seminars in radiation oncology.

[12]  John Wong,et al.  Accuracy of a wireless localization system for radiotherapy. , 2005, International journal of radiation oncology, biology, physics.

[13]  Manish Kakar,et al.  Respiratory motion prediction by using the adaptive neuro fuzzy inference system (ANFIS). , 2005, Physics in medicine and biology.

[14]  S Webb,et al.  Feasibility of using ultrasound for real-time tracking during radiotherapy. , 2005, Medical physics.

[15]  F. Yin,et al.  The correlation evaluation of a tumor tracking system using multiple external markers. , 2006, Medical physics.

[16]  C.C. Nguyen,et al.  Intelligent Approach to Robotic Respiratory Motion Compensation for Radiosurgery and other Interventions , 2006, 2006 World Automation Congress.

[17]  Raj Shekhar,et al.  Effect of Ultrasound Probe on Dose Delivery During Real-time Ultrasound-Guided Tumor Tracking , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[18]  John A. Mills,et al.  Prediction of Tumour Motion using Interacting Multiple Model Filter , 2006 .

[19]  George Starkschall,et al.  Assessing respiration-induced tumor motion and internal target volume using four-dimensional computed tomography for radiotherapy of lung cancer. , 2007, International journal of radiation oncology, biology, physics.

[20]  Hiroshi Honda,et al.  Reproducibility of the abdominal and chest wall position by voluntary breath-hold technique using a laser-based monitoring and visual feedback system. , 2007, International journal of radiation oncology, biology, physics.

[21]  J H Goodband,et al.  A comparison of neural network approaches for on-line prediction in IGRT. , 2008, Medical physics.

[22]  G. Jiang,et al.  Application of active breathing control in 3-dimensional conformal radiation therapy for hepatocellular carcinoma: the feasibility and benefit. , 2008, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[23]  Steve B. Jiang,et al.  Lung tumor tracking in fluoroscopic video based on optical flow. , 2008, Medical physics.

[24]  Lei Xing,et al.  Predicting respiratory tumor motion with multi-dimensional adaptive filters and support vector regression , 2009, Physics in medicine and biology.

[25]  Philip M Evans,et al.  Feasibility of the use of the Active Breathing Co ordinator (ABC) in patients receiving radical radiotherapy for non-small cell lung cancer (NSCLC). , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[26]  Raj Shekhar,et al.  Do tumors in the lung deform during normal respiration? An image registration investigation. , 2009, International journal of radiation oncology, biology, physics.

[27]  Steve B. Jiang,et al.  Using surface imaging and visual coaching to improve the reproducibility and stability of deep-inspiration breath hold for left-breast-cancer radiotherapy , 2009, Physics in medicine and biology.

[28]  G Baroni,et al.  Four-dimensional targeting error analysis in image-guided radiotherapy , 2009, Physics in medicine and biology.

[29]  Steve B. Jiang,et al.  Fluoroscopic tumor tracking for image-guided lung cancer radiotherapy , 2009, Physics in medicine and biology.

[30]  J. McClelland,et al.  Assessment of two novel ventilatory surrogates for use in the delivery of gated/tracked radiotherapy for non-small cell lung cancer. , 2009, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[31]  Habib Zaidi,et al.  Review of Computational Anthropomorphic Anatomical and Physiological Models , 2009, Proceedings of the IEEE.

[32]  Paul Segars,et al.  Extension of the NCAT phantom for the investigation of intra-fraction respiratory motion in IMRT using 4D Monte Carlo , 2010, Physics in medicine and biology.

[33]  Marco Riboldi,et al.  Targeting Accuracy in Real-time Tumor Tracking via External Surrogates: A Comparative Study , 2010, Technology in cancer research & treatment.

[34]  K. Wells,et al.  Respiratory motion modelling and prediction using probability density estimation , 2010, IEEE Nuclear Science Symposuim & Medical Imaging Conference.

[35]  Matthias Guckenberger,et al.  Feasibility study for markerless tracking of lung tumors in stereotactic body radiotherapy. , 2010, International journal of radiation oncology, biology, physics.

[36]  Dan Ruan,et al.  Kernel density estimation-based real-time prediction for respiratory motion , 2010, Physics in medicine and biology.

[37]  B G Fallone,et al.  Lung dosimetry in a linac-MRI radiotherapy unit with a longitudinal magnetic field. , 2010, Medical physics.

[38]  W. Segars,et al.  4D XCAT phantom for multimodality imaging research. , 2010, Medical physics.

[39]  Fang-Fang Yin,et al.  Four-dimensional magnetic resonance imaging (4D-MRI) using image-based respiratory surrogate: a feasibility study. , 2011, Medical physics.

[40]  Steve B. Jiang,et al.  MRI-guided tumor tracking in lung cancer radiotherapy , 2011, Physics in medicine and biology.

[41]  Sanford L. Meeks,et al.  Expanding the use of real‐time electromagnetic tracking in radiation oncology , 2011, Journal of Applied Clinical Medical Physics.

[42]  S P M Crijns,et al.  Proof of concept of MRI-guided tracked radiation delivery: tracking one-dimensional motion , 2012, Physics in medicine and biology.

[43]  Marco Riboldi,et al.  Real-time tumour tracking in particle therapy: technological developments and future perspectives. , 2012, The Lancet. Oncology.

[44]  Marco Riboldi,et al.  A Clinical Application of Fuzzy Logic , 2012 .

[45]  Michael Ljungberg,et al.  Development and evaluation of an improved quantitative (90)Y bremsstrahlung SPECT method. , 2012, Medical physics.

[46]  Ping Xia,et al.  Assessing Feasibility of Real-Time Ultrasound Monitoring in Stereotactic Body Radiotherapy of Liver Tumors , 2013, Technology in cancer research & treatment.

[47]  Marco Riboldi,et al.  An adaptive fuzzy prediction model for real time tumor tracking in radiotherapy via external surrogates , 2013, Journal of applied clinical medical physics.