A software tool to automatically assure and report daily treatment deliveries by a cobalt‐60 radiation therapy device

The aims of this study were to develop a method for automatic and immediate verification of treatment delivery after each treatment fraction in order to detect and correct errors, and to develop a comprehensive daily report which includes delivery verification results, daily image‐guided radiation therapy (IGRT) review, and information for weekly physics reviews. After systematically analyzing the requirements for treatment delivery verification and understanding the available information from a commercial MRI‐guided radiotherapy treatment machine, we designed a procedure to use 1) treatment plan files, 2) delivery log files, and 3) beam output information to verify the accuracy and completeness of each daily treatment delivery. The procedure verifies the correctness of delivered treatment plan parameters including beams, beam segments and, for each segment, the beam‐on time and MLC leaf positions. For each beam, composite primary fluence maps are calculated from the MLC leaf positions and segment beam‐on time. Error statistics are calculated on the fluence difference maps between the plan and the delivery. A daily treatment delivery report is designed to include all required information for IGRT and weekly physics reviews including the plan and treatment fraction information, daily beam output information, and the treatment delivery verification results. A computer program was developed to implement the proposed procedure of the automatic delivery verification and daily report generation for an MRI guided radiation therapy system. The program was clinically commissioned. Sensitivity was measured with simulated errors. The final version has been integrated into the commercial version of the treatment delivery system. The method automatically verifies the EBRT treatment deliveries and generates the daily treatment reports. Already in clinical use for over one year, it is useful to facilitate delivery error detection, and to expedite physician daily IGRT review and physicist weekly chart review. PACS number(s): 87.55.km

[1]  Sridhar Yaddanapudi,et al.  Initial experience with TrueBeam trajectory log files for radiation therapy delivery verification. , 2013, Practical radiation oncology.

[2]  Sasa Mutic,et al.  A Device for Realtime 3D Image-Guided IMRT , 2005 .

[3]  S. Delorme,et al.  MR-guidance – a clinical study to evaluate a shuttle- based MR-linac connection to provide MR-guided radiotherapy , 2014, Radiation oncology.

[4]  Juan F. Calvo‐Ortega,et al.  A Varian DynaLog file‐based procedure for patient dose‐volume histogram‐based IMRT QA , 2014, Journal of applied clinical medical physics.

[5]  D Yang,et al.  WE-E-BRB-07: Direct 3D Fluence Calculation from Machine Beam Parameters for VMAT Delivery Verification. , 2012, Medical physics.

[6]  Ravikumar Manickam,et al.  Consistency and reproducibility of the VMAT plan delivery using three independent validation methods , 2010, Journal of applied clinical medical physics.

[7]  J. Palta,et al.  Comprehensive QA for radiation oncology: report of AAPM Radiation Therapy Committee Task Group 40. , 1994, Medical physics.

[8]  Young-Bin Cho,et al.  Hybrid adaptive radiotherapy with on-line MRI in cervix cancer IMRT. , 2014, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[9]  Jean M. Moran,et al.  Verification of dynamic and segmental IMRT delivery by dynamic log file analysis , 2002, Journal of applied clinical medical physics.

[10]  Sasa Mutic,et al.  Patient-specific quality assurance for the delivery of (60)Co intensity modulated radiation therapy subject to a 0.35-T lateral magnetic field. , 2015, International journal of radiation oncology, biology, physics.

[11]  T. Bortfeld IMRT: a review and preview , 2006, Physics in medicine and biology.

[12]  Sridhar Yaddanapudi,et al.  SU‐FF‐T‐236: Dynalog Based Quality Assurance for Rapid Arc Therapy , 2009 .

[13]  Fang-Fang Yin,et al.  A planning quality evaluation tool for prostate adaptive IMRT based on machine learning. , 2011, Medical physics.

[14]  Sasa Mutic,et al.  The ViewRay system: magnetic resonance-guided and controlled radiotherapy. , 2014, Seminars in radiation oncology.

[15]  Jonas D. Fontenot,et al.  Feasibility of a remote, automated daily delivery verification of volumetric‐modulated arc therapy treatments using a commercial record and verify system , 2012, Journal of applied clinical medical physics.

[16]  M. Dinesh Kumar,et al.  QA of intensity-modulated beams using dynamic MLC log files , 2006, Journal of medical physics.

[17]  Dietmar Georg,et al.  Adaptive radiation therapy. , 2018, Zeitschrift fur medizinische Physik.

[18]  E. Schreibmann,et al.  Patient-specific quality assurance method for VMAT treatment delivery. , 2009, Medical physics.

[19]  Sridhar Yaddanapudi,et al.  WE‐C‐214‐04: ADQ — A Software Tool That Automatically, Autonomously, Intelligently and Instantly Verify Patient Radiation Therapy Beam Deliveries , 2011 .

[20]  Tejinder Kataria,et al.  Image Guidance in Radiation Therapy: Techniques and Applications , 2014, Radiology research and practice.

[21]  Yi Rong,et al.  Parallel/Opposed: IMRT QA using treatment log files is superior to conventional measurement‐based method , 2015, Journal of applied clinical medical physics.

[22]  C. Agnew,et al.  Correlation of phantom‐based and log file patient‐specific QA with complexity scores for VMAT , 2014, Journal of applied clinical medical physics.