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Sci—Sat AM(1): Planning — 11: Use of a Graphics Processor (GPU) for High‐Performance Deformable Registration of Cone Beam (kV) and Megavoltage (MV) CT Images
Author(s) -
Wang A,
Disher B,
Battista J,
Peters TM
Publication year - 2010
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 - 10.1118/1.3476211
Subject(s) - image registration , tomotherapy , computer science , artificial intelligence , computer vision , image guided radiation therapy , graphics processing unit , radiation treatment planning , medical imaging , nuclear medicine , radiation therapy , medicine , radiology , image (mathematics) , operating system
To compensate for the inter‐fraction deformation in fractionated radiotherapy, it is essential that “images of the day” used for treatment guidance be co‐registered with the 3D images used initially for treatment planning and dose prescription. We implemented a high performance deformable image registration algorithm on the standard graphics process unit (GPU) to accomplish this very efficiently with the ultimate goal of enabling adaptive dose computations at the treatment console. Normalized cross correlation (NCC) was employed as the similarity metric in a block‐matching algorithm. Regularization of the resulting displacement vector field was performed by Gaussian smoothing. A multi‐resolution strategy was adopted to further improve the performance of the algorithm. To evaluate performance, we compared results with two popular deformable registration algorithms (Diffeomorphic Demons and B‐spline) based from the Insight Toolkit (ITK). All three algorithms were first applied to register thoracic planning CT (PCT) to cone‐beam CT (CBCT) scans of three lung cancer patients. Next, they were used to align the pelvic PCT to megavoltage CT (MVCT) scans from a tomotherapy unit of a prostate cancer patient. For both types of anatomy and image features (contrast, noise), manual landmark‐based evaluation was performed to quantify the registration accuracy. In PCT‐CBCT registration experiment, mean registration error (MRE) was 2.53mm. In PCT‐MVCT registration, MRE was 2.15 mm. Compared to Diffeomorphic Demons and B‐spline‐based algorithms, our GPU‐based implementation achieves comparable registration accuracy and is ∼20 times faster (completes registration in 15 seconds). The results highlight the potential utility of our algorithm for on‐line adaptive radiation treatment.