Shared data for intensity modulated radiation therapy (IMRT) optimization research: the CORT dataset

GigaScience

Jan Unkelbach¹  Dávid Papp¹  Troy Long³  Mark Bangert²  David Craft¹ 

[1] Massachusetts General Hospital, Harvard Medical School, 02114 Boston, MA, USA;German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany;University of Michigan, 48109 Ann Arbor, Michigan, USA

关键词: Treatment plan optimization;    VMAT;    Beam angle optimization;    Radiation therapy;    Optimization;    IMRT;   

Others  :  1118566
DOI  :  10.1186/2047-217X-3-37

 received in 2014-07-14, accepted in 2014-11-19,  发布年份 2014

【 摘 要 】

Background

We provide common datasets (which we call the CORT dataset: common optimization for radiation therapy) that researchers can use when developing and contrasting radiation treatment planning optimization algorithms. The datasets allow researchers to make one-to-one comparisons of algorithms in order to solve various instances of the radiation therapy treatment planning problem in intensity modulated radiation therapy (IMRT), including beam angle optimization, volumetric modulated arc therapy and direct aperture optimization.

Results

We provide datasets for a prostate case, a liver case, a head and neck case, and a standard IMRT phantom. We provide the dose-influence matrix from a variety of beam/couch angle pairs for each dataset. The dose-influence matrix is the main entity needed to perform optimizations: it contains the dose to each patient voxel from each pencil beam. In addition, the original Digital Imaging and Communications in Medicine (DICOM) computed tomography (CT) scan, as well as the DICOM structure file, are provided for each case.

Conclusions

Here we present an open dataset – the first of its kind – to the radiation oncology community, which will allow researchers to compare methods for optimizing radiation dose delivery.

【 授权许可】

   
2014 Craft et al.; licensee BioMed Central.

【 图 表 】

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【 参考文献 】

• [1]Alber M, Nusslin F: Intensity modulated photon beams subject to a minimal surface constraint. Phys Med Biol 2000, 45:49-52.
• [2]Oelfke U, Bortfeld T: Inverse planning for photon and proton beams. Med Dosim 2001, 26:113-124.
• [3]Webb S: A simple method to control aspects of fluence modulation in IMRT planning. Phys Med Biol 2001, 46:187-195.
• [4]Bangert M, Ziegenhein P, Oelfke U: Characterizing the combinatorial beam angle selection problem. Phys Med Biol 2012, 57(20):6707.
• [5]Wang X, Zhang X, Dong L, Liu H, Wu Q, Mohan R: Development of methods for beam angle optimization for IMRT using an accelerated exhaustive search strategy. Int J Radiat Oncol Biol Phys 2004, 60(4):1325-1337.
• [6]Craft D: Local beam angle optimization with linear programming and gradient search. Phys Med Biol 2007, 52(7):127-135.
• [7]Breedveld S, Storchi P, Voet P, Heijmen B: iCycle: integrated, multicriterial beam angle, and profile optimization for generation of coplanar and noncoplanar IMRT plans. Med Phys 2012, 39:951.
• [8]Yu C, Tang G: Intensity-modulated arc therapy: principles, technologies and clinical implementation. Phys Med Biol 2011, 56(5):31-54.
• [9]Craft D, McQuaid D, Wala J, Chen W, Salari E, Bortfeld T: Multicriteria VMAT optimization. Med Phys 2012, 39:686.
• [10]Bokrantz R: Multicriteria optimization for volumetric-modulated arc therapy by decomposition into a fluence-based relaxation and a segment weight-based restriction. Med Phys 2012, 39:6712.
• [11]Papp D, Unkelbach J: Direct leaf trajectory optimization for volumetric modulated arc therapy planning with sliding window delivery. Med Phys 2014, 41(1):011701.
• [12]Otto K: Volumetric modulated arc therapy: IMRT in a single gantry arc. Med Phys 2008, 35(1):310-317.
• [13]Bzdusek K, Friberger H, Eriksson K, Hårdemark B, Robinson D, Kaus M: Development and evaluation of an efficient approach to volumetric arc therapy planning. Med Phys 2009, 36:2328.
• [14]Meng B, Zhu L, Widrow B, Boyd S, Xing L: A unified framework for 3D radiation therapy and IMRT planning: plan optimization in the beamlet domain by constraining or regularizing the fluence map variations. Phys Med Biol 2010, 55(22):521.
• [15]Khan F, Craft D: 3D conformal planning using low segment multi-criteria IMRT optimization. Pract Radiat Oncol 2014. [http://dx.doi.org/10.1016/j.prro.2014.07.001 webcite]
• [16]Yarmand H, Winey B, Craft D: Guaranteed epsilon-optimal treatment plans with the minimum number of beams for stereotactic body radiation therapy. Phys Med Biol 2013, 58(17):5931.
• [17]Salari E, Unkelbach J: A column-generation-based method for multi-criteria direct aperture optimization. Phys Med Biol 2013, 58(3):621.
• [18]Shepard D, Earl M, Li X, Naqvi S, Yu C: Direct aperture optimization: a turnkey solution for step-and-shoot IMRT. Med Phys 2002, 29:1007-1018.
• [19]Men C, Romeijn H, Taşkın Z, Dempsey J: An exact approach to direct aperture optimization in IMRT treatment planning. Phys Med Biol 2007, 52:7333.
• [20]Cassioli A, Unkelbach J: Aperture shape optimization for IMRT treatment planning. Phys Med Biol 2013, 58(2):301.
• [21]Carlsson F: Combining segment generation with direct step-and-shoot optimization in intensity-modulated radiation therapy. Med Phys 2008, 35:3828.
• [22]Romeijn HE, Ahuja RK, Dempsey JF, Kumar A: A column generation approach to radiation therapy treatment planning using aperture modulation. SIAM J Optimization 2005, 15(3):838-862.
• [23]Craft D, Süss P, Bortfeld T: The tradeoff between treatment plan quality and required number of monitor units in IMRT. Int J Radiat Oncol Biol Phys 2007, 67(5):1596-1605.
• [24]Breedveld S, Storchi P, Keijzer M, Heijmen B: Fast, multiple optimizations of quadratic dose objective functions in IMRT. Phys Med Biol 2006, 51(14):3569.
• [25]Deasy J, Blanco A, Clark V: CERR: A computational environment for radiotherapy research. Med Phys 2003, 30(5):979-985.
• [26]Ahnesjö A, Saxner M, Trepp A: A pencil beam model for photon dose calculation. Med Phys 1992, 19(2):263-273.
• [27]Kalinin E, Deasy J: A method for fast 3-D IMRT dose calculations: The quadrant infinite beam (QIB) algorithm. Med Phys 2003, 30(6):1348-1349.
• [28]Ezzell G, Burmeister J, Dogan N, LoSasso T, Mechalakos J, Mihailidis D, Molineu A, Palta J, Ramsey C, Salter B, Shi J, Xia P, Yue N, Xiao Y: IMRT commissioning: multiple institution planning and dosimetry comparisons, a report from AAPM Task Group 119. Med Phys 2009, 36(11):5359-5373.
• [29]The cancer imaging archive (TCIA) http://www.cancerimagingarchive.net webcite. Accessed: 2014-10-13
• [30]Jelen U, Sohn M, Alber M: A finite size pencil beam for IMRT dose optimization. Phys Med Biol 2005, 50(8):1747-1766.
• [31]Hårdemark B, Liander A, Rehbinder H, Löf J: Direct machine parameter optimization with RayMachine®; in Pinnacle3®;. RaySearch White Paper. Stockholm, Sweden: RaySearch Laboratories AB; 2003.
• [32]Craft D, Bangert M, Long T, Papp D, Unkelbach J: Supporting material for: “Shared data for IMRT optimization research: theCORT dataset”. GigaScience Database 2014. [http://dx.doi.org/10.5524/100110 webcite].