Gradient maintenance: A new algorithm for fast online replanning.

PURPOSE Clinical use of online adaptive replanning has been hampered by the unpractically long time required to delineate volumes based on the image of the day. The authors propose a new replanning algorithm, named gradient maintenance (GM), which does not require the delineation of organs at risk (OARs), and can enhance automation, drastically reducing planning time and improving consistency and throughput of online replanning. METHODS The proposed GM algorithm is based on the hypothesis that if the dose gradient toward each OAR in daily anatomy can be maintained the same as that in the original plan, the intended plan quality of the original plan would be preserved in the adaptive plan. The algorithm requires a series of partial concentric rings (PCRs) to be automatically generated around the target toward each OAR on the planning and the daily images. The PCRs are used in the daily optimization objective function. The PCR dose constraints are generated with dose-volume data extracted from the original plan. To demonstrate this idea, GM plans generated using daily images acquired using an in-room CT were compared to regular optimization and image guided radiation therapy repositioning plans for representative prostate and pancreatic cancer cases. RESULTS The adaptive replanning using the GM algorithm, requiring only the target contour from the CT of the day, can be completed within 5 min without using high-power hardware. The obtained adaptive plans were almost as good as the regular optimization plans and were better than the repositioning plans for the cases studied. CONCLUSIONS The newly proposed GM replanning algorithm, requiring only target delineation, not full delineation of OARs, substantially increased planning speed for online adaptive replanning. The preliminary results indicate that the GM algorithm may be a solution to improve the ability for automation and may be especially suitable for sites with small-to-medium size targets surrounded by several critical structures.

[1]  Sreeram Narayanan,et al.  An on-line replanning scheme for interfractional variationsa). , 2008, Medical physics.

[2]  B. Erickson,et al.  Comparison of various online strategies to account for interfractional variations for pancreatic cancer. , 2013, International journal of radiation oncology, biology, physics.

[3]  X Allen Li,et al.  Characterization and management of interfractional anatomic changes for pancreatic cancer radiotherapy. , 2012, International journal of radiation oncology, biology, physics.

[4]  Vira Chankong,et al.  On-line re-optimization of prostate IMRT plans for adaptive radiation therapy , 2008, Physics in medicine and biology.

[5]  Patricio Simari,et al.  Fully automated simultaneous integrated boosted-intensity modulated radiation therapy treatment planning is feasible for head-and-neck cancer: a prospective clinical study. , 2012, International journal of radiation oncology, biology, physics.

[6]  J. Deasy,et al.  Obstacles and advances in intensity-modulated radiation therapy treatment planning. , 2007, Frontiers of radiation therapy and oncology.

[7]  Radhe Mohan,et al.  Performance evaluation of automatic anatomy segmentation algorithm on repeat or four-dimensional computed tomography images using deformable image registration method. , 2008, International journal of radiation oncology, biology, physics.

[8]  Wolfgang Birkfellner,et al.  A quantitative comparison of the performance of three deformable registration algorithms in radiotherapy , 2013, Zeitschrift fur medizinische Physik.

[9]  Marcel van Herk,et al.  Quantification of shape variation of prostate and seminal vesicles during external beam radiotherapy. , 2005, International journal of radiation oncology, biology, physics.

[10]  Linghong Zhou,et al.  Automatic treatment plan re-optimization for adaptive radiotherapy guided with the initial plan DVHs , 2013, Physics in medicine and biology.

[11]  David A Jaffray,et al.  Emergent technologies for 3-dimensional image-guided radiation delivery. , 2005, Seminars in radiation oncology.

[12]  Fredrik Carlsson,et al.  Multicriteria optimization in intensity-modulated radiation therapy treatment planning for locally advanced cancer of the pancreatic head. , 2008, International journal of radiation oncology, biology, physics.

[13]  Ergun Ahunbay,et al.  Automated registration of large deformations for adaptive radiation therapy of prostate cancer. , 2009 .

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