Gradient maintenance: A new algorithm for fast online replanning. Issue 6 (30th November 2016)

• Record Type: Journal Article
• Authors: Ahunbay, Ergun E.; Li, X. Allen
• Is Part Of: Medical physics. Volume 42:Issue 6(2015)
• Page Start: 2863
• Page End: 2876
• Publication Date: 2016-11-30
• Subjects: adaptive systems -- cancer -- computerised tomography -- dosimetry -- Internet -- medical image processing -- optimisation -- radiation therapy; Therapeutic applications, including brachytherapy -- Dosimetry/exposure assessment -- Computed tomography -- World Wide Web, Internet -- Numerical optimization; Computerised tomographs -- Radiation therapy -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Characterised by path configuration, e.g. lan [local area networks] or wan [wide area networks]; adaptive radiotherapy -- online replanning -- image guided radiotherapy -- dose deformation -- deformable image registration; Computed tomography -- Optimization -- Self assembly -- Medical image quality -- Anatomy -- Image guided radiation therapy -- Cancer -- Dosimetry; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4919847
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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.