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SU‐C‐BRA‐05: Fast Generation of Respiratory Gated CT Images at User Selected Breathing Phases On a Graphics Processing Unit
Author(s) -
O'Connell D. P.,
Thomas D. H.,
Dou T. H.,
Lamb J. M.,
Yang L.,
Low D. A.
Publication year - 2015
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.4923815
Subject(s) - breathing , voxel , computer science , artificial intelligence , computer vision , graphics processing unit , nuclear medicine , image registration , medical imaging , image (mathematics) , medicine , anatomy , operating system
Purpose: The previously published 5DCT respiratory gated image acquisition and analysis technique enables generation of images at any user selected breathing phase. This work describes acceleration of the image generation process using a graphics processing unit (GPU) and its application to internal target volume (ITV) definition and the creation of simulated cine scans. Methods: 25 fast helical, free breathing CT scans of 7 lung cancer patients were acquired using a low dose protocol with simultaneous breathing surrogate monitoring. For each patient, the first scan was deformably registered to the following 24. Deformation vectors were used to determine voxel‐specific parameters of a motion model. A single, low noise reference image in the geometry of the first scan was created using image averaging. The motion model was used to predict the deformation from the reference image to selected breathing phases. Internal target volumes were generated by deforming a single contour of the gross tumor volume (GTV) to the most common breathing phases accounting for 90% of observed respiration. Simulated cines were created by generating volumetric images at 0.25 second intervals along the measured breathing trace and taking slices at desired positions. Computations were performed on an NVIDIA Tesla K40. Results: Calculation of motion model parameters took approximately 3 seconds per dataset. Image generation took approximately 0.25 seconds total for a 450 × 450 × 300 image with isotropic 1 mm 3 resolution. Conclusion: GPU acceleration enabled rapid generation of breathing gated CT images using the 5DCT technique and facilitated use of a novel method for defining customized lung tumor ITVs that account for a specified percentage of observed respiration, and the creation of simulated cine images in a clinically acceptable time frame. Investigation of the differences between ITVs generated using the technique described here and ITVs defined on conventional 4DCT datasets is ongoing.