Real-time tumor tracking with an artificial neural networks-based method: a feasibility study.

The purpose of this study was to develop and assess the performance of a tumor tracking method designed for application in radiation therapy. This motion compensation strategy is currently applied clinically only in conventional photon radiotherapy but not in particle therapy, as greater accuracy in dose delivery is required. We proposed a tracking method that exploits artificial neural networks to estimate the internal tumor trajectory as a function of external surrogate signals. The developed algorithm was tested by means of a retrospective clinical data analysis in 20 patients, who were treated with state of the art infra-red motion tracking for photon radiotherapy, which is used as a benchmark. Integration into a hardware platform for motion tracking in particle therapy was performed and then tested on a moving phantom, specifically developed for this purpose. Clinical data show that a median tracking error reduction up to 0.7 mm can be achieved with respect to state of the art technologies. The phantom study demonstrates that a real-time tumor position estimation is feasible when the external signals are acquired at 60 Hz. The results of this work show that neural networks can be considered a valuable tool for the implementation of high accuracy real-time tumor tracking methodologies.

[1]  David A. Jaffray,et al.  60 The use of active breathing control (ABC) to minimize breathing motion during radiation therapy , 1997 .

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

[3]  Christoph Bert,et al.  Motion compensation with a scanned ion beam: a technical feasibility study , 2008, Radiation Oncology.

[4]  M. Riboldi,et al.  A multiple points method for 4D CT image sorting. , 2011, Medical physics.

[5]  C. Ling,et al.  Technical aspects of the deep inspiration breath-hold technique in the treatment of thoracic cancer. , 2000, International journal of radiation oncology, biology, physics.

[6]  C. Ling,et al.  Evaluation of respiratory movement during gated radiotherapy using film and electronic portal imaging. , 2002, International journal of radiation oncology, biology, physics.

[7]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.

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

[9]  G. Kraft,et al.  Tumor therapy with heavy charged particles , 2000 .

[10]  J. Adler,et al.  Robotic Motion Compensation for Respiratory Movement during Radiosurgery , 2000, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[11]  Hiroki Shirato,et al.  Accuracy of tumor motion compensation algorithm from a robotic respiratory tracking system: a simulation study. , 2007, Medical physics.

[12]  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.

[13]  H Shirato,et al.  Inference of hysteretic respiratory tumor motion from external surrogates: a state augmentation approach , 2008, Physics in medicine and biology.

[14]  C. Maurer,et al.  The CyberKnife® Robotic Radiosurgery System in 2010 , 2010, Technology in cancer research & treatment.

[15]  Marco Durante,et al.  Dosimetric precision of an ion beam tracking system , 2010, Radiation oncology.

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

[17]  Cristina Garibaldi,et al.  3D optoelectronic analysis of interfractional patient setup variability in frameless extracranial stereotactic radiotherapy. , 2006, International journal of radiation oncology, biology, physics.

[18]  Joao Seco,et al.  Dosimetric impact of motion in free-breathing and gated lung radiotherapy: a 4D Monte Carlo study of intrafraction and interfraction effects. , 2007, Medical physics.

[19]  George T. Y. Chen,et al.  A phantom evaluation of a stereo-vision surface imaging system for radiotherapy patient setup. , 2005, Medical physics.

[20]  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.

[21]  John M Buatti,et al.  Optically guided patient positioning techniques. , 2005, Seminars in radiation oncology.

[22]  W. Stanford,et al.  Analysis of movement of intrathoracic neoplasms using ultrafast computerized tomography. , 1990, International journal of radiation oncology, biology, physics.

[23]  Icru.  Prescribing, recording, and reporting photon beam therapy , 1993 .

[24]  K. Langen,et al.  Organ motion and its management. , 2001, International journal of radiation oncology, biology, physics.

[25]  G J Kutcher,et al.  Deep inspiration breath-hold technique for lung tumors: the potential value of target immobilization and reduced lung density in dose escalation. , 1999, International journal of radiation oncology, biology, physics.

[26]  M. Hoogeman,et al.  Clinical accuracy of the respiratory tumor tracking system of the cyberknife: assessment by analysis of log files. , 2009, International journal of radiation oncology, biology, physics.

[27]  K. Ohara,et al.  Irradiation synchronized with respiration gate. , 1989, International journal of radiation oncology, biology, physics.

[28]  J. Wong,et al.  The use of active breathing control (ABC) to reduce margin for breathing motion. , 1999, International journal of radiation oncology, biology, physics.