3 March 2017 Fractional labelmaps for computing accurate dose volume histograms
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Abstract
PURPOSE: In radiation therapy treatment planning systems, structures are represented as parallel 2D contours. For treatment planning algorithms, structures must be converted into labelmap (i.e. 3D image denoting structure inside/outside) representations. This is often done by triangulated a surface from contours, which is converted into a binary labelmap. This surface to binary labelmap conversion can cause large errors in small structures. Binary labelmaps are often represented using one byte per voxel, meaning a large amount of memory is unused. Our goal is to develop a fractional labelmap representation containing non-binary values, allowing more information to be stored in the same amount of memory. METHODS: We implemented an algorithm in 3D Slicer, which converts surfaces to fractional labelmaps by creating 216 binary labelmaps, changing the labelmap origin on each iteration. The binary labelmap values are summed to create the fractional labelmap. In addition, an algorithm is implemented in the SlicerRT toolkit that calculates dose volume histograms (DVH) using fractional labelmaps. RESULTS: We found that with manually segmented RANDO head and neck structures, fractional labelmaps represented structure volume up to 19.07% (average 6.81%) more accurately than binary labelmaps, while occupying the same amount of memory. When compared to baseline DVH from treatment planning software, DVH from fractional labelmaps had agreement acceptance percent (1% ΔD, 1% ΔV) up to 57.46% higher (average 4.33%) than DVH from binary labelmaps. CONCLUSION: Fractional labelmaps promise to be an effective method for structure representation, allowing considerably more information to be stored in the same amount of memory.
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Kyle Sunderland, Kyle Sunderland, Csaba Pinter, Csaba Pinter, Andras Lasso, Andras Lasso, Gabor Fichtinger, Gabor Fichtinger, } "Fractional labelmaps for computing accurate dose volume histograms", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101352Y (3 March 2017); doi: 10.1117/12.2254978; https://doi.org/10.1117/12.2254978
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