Real-time interactive treatment planning

The goal of this work is to develop an interactive treatment planning platform that permits real-time manipulation of dose distributions including DVHs and other dose metrics. The hypothesis underlying the approach proposed here is that the process of evaluating potential dose distribution options and deciding on the best clinical trade-offs may be separated from the derivation of the actual delivery parameters used for the patient's treatment. For this purpose a novel algorithm for deriving an Achievable Dose Estimate (ADE) was developed. The ADE algorithm is computationally efficient so as to update dose distributions in effectively real-time while accurately incorporating the limits of what can be achieved in practice. The resulting system is a software environment for interactive real-time manipulation of dose that permits the clinician to rapidly develop a fully customized 3D dose distribution. Graphical navigation of dose distributions is achieved by a sophisticated method of identifying contributing fluence elements, modifying those elements and re-computing the entire dose distribution. 3D dose distributions are calculated in ~2-20 ms. Including graphics processing overhead, clinicians may visually interact with the dose distribution (e.g. 'drag' a DVH) and display updates of the dose distribution at a rate of more than 20 times per second. Preliminary testing on various sites shows that interactive planning may be completed in ~1-5 min, depending on the complexity of the case (number of targets and OARs). Final DVHs are derived through a separate plan optimization step using a conventional VMAT planning system and were shown to be achievable within 2% and 4% in high and low dose regions respectively. With real-time interactive planning trade-offs between Target(s) and OARs may be evaluated efficiently providing a better understanding of the dosimetric options available to each patient in static or adaptive RT.

[1]  Boyd McCurdy,et al.  PARETO: A novel evolutionary optimization approach to multiobjective IMRT planning. , 2011, Medical physics.

[2]  D. Low,et al.  Experience-based quality control of clinical intensity-modulated radiotherapy planning. , 2011, International Journal of Radiation Oncology, Biology, Physics.

[3]  Bill J Salter,et al.  Intensity-Modulated Radiosurgery: Improving Dose Gradients and Maximum Dose Using Post Inverse-Optimization Interactive Dose Shaping , 2007, Technology in cancer research & treatment.

[4]  W. R. Lee,et al.  Knowledge-based IMRT treatment planning for prostate cancer. , 2011 .

[5]  Steve B. Jiang,et al.  GPU-based high-performance computing for radiation therapy , 2014, Physics in medicine and biology.

[6]  Joel R. Wilkie,et al.  Use of plan quality degradation to evaluate tradeoffs in delivery efficiency and clinical plan metrics arising from IMRT optimizer and sequencer compromises. , 2013, Medical physics.

[7]  Wei Chen,et al.  Multicriteria VMAT optimization. , 2011, Medical physics.

[8]  J Frantzis,et al.  Clinician's guide to prostate IMRT plan assessment and optimisation , 2010, Journal of medical imaging and radiation oncology.

[9]  T. Bortfeld,et al.  Improved planning time and plan quality through multicriteria optimization for intensity-modulated radiotherapy. , 2012, International journal of radiation oncology, biology, physics.

[10]  Pradeep Dubey,et al.  Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU , 2010, ISCA.

[11]  Christian Richter,et al.  Deliverable navigation for multicriteria step and shoot IMRT treatment planning , 2012, Physics in medicine and biology.

[12]  Peter Ziegenhein,et al.  Performance-optimized clinical IMRT planning on modern CPUs , 2013, Physics in medicine and biology.

[13]  Steve B. Jiang,et al.  Ultrafast treatment plan optimization for volumetric modulated arc therapy (VMAT) , 2010, 1005.4396.

[14]  Uwe Oelfke,et al.  TH‐A‐213AB‐02: Isodose Curve Manipulation for Interactive Dose Shaping , 2012 .

[15]  Rasmus Bokrantz,et al.  Deliverable navigation for multicriteria IMRT treatment planning by combining shared and individual apertures , 2013, Physics in medicine and biology.

[16]  Xun Jia,et al.  A GPU-based finite-size pencil beam algorithm with 3D-density correction for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[17]  P. Storchi,et al.  Calculation of a pencil beam kernel from measured photon beam data. , 1999, Physics in medicine and biology.

[18]  Yair Censor,et al.  From analytic inversion to contemporary IMRT optimization: radiation therapy planning revisited from a mathematical perspective. , 2012, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[19]  Xun Jia,et al.  GPU-based fast Monte Carlo simulation for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[20]  Steve B. Jiang,et al.  GPU-based ultra-fast direct aperture optimization for online adaptive radiation therapy , 2010, Physics in medicine and biology.

[21]  Karl Otto,et al.  Volumetric modulated arc therapy: IMRT in a single gantry arc. , 2007, Medical physics.

[22]  K Moore,et al.  SU-E-T-588: A Novel IMRT Plan Optimization Algorithm for Physician-Driven Plan Tuning. , 2013, Medical physics.

[23]  J. Lo,et al.  A knowledge-based approach to improving and homogenizing intensity modulated radiation therapy planning quality among treatment centers: an example application to prostate cancer planning. , 2013, International journal of radiation oncology, biology, physics.

[24]  A. Niemierko Reporting and analyzing dose distributions: a concept of equivalent uniform dose. , 1997, Medical physics.

[25]  Warren D D'Souza,et al.  The minimum knowledge base for predicting organ-at-risk dose–volume levels and plan-related complications in IMRT planning , 2010, Physics in medicine and biology.