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Accuracy and sensitivity of finite element model‐based deformable registration of the prostate
Author(s) -
Brock Kristy K.,
Nichol Alan M.,
Ménard Cynthia,
Moseley Joanne L.,
Warde Padraig R.,
Catton Charles N.,
Jaffray David A.
Publication year - 2008
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 - 10.1118/1.2965263
Subject(s) - image registration , residual , sensitivity (control systems) , voxel , mathematics , magnetic resonance imaging , nuclear medicine , artificial intelligence , algorithm , computer science , computer vision , image (mathematics) , medicine , radiology , electronic engineering , engineering
Purpose: Evaluate the accuracy and the sensitivity to contour variation and model size of a finite element model‐based deformable registration algorithm for the prostate. Methods and materials: Two magnetic resonance images (MRIs) were obtained for 21 prostate patients with three implanted markers. A single observer contoured the prostate and markers and performed blinded recontouring of the first MRI. A biomechanical‐model based deformable registration algorithm, MORFEUS , was applied to each dataset pair, deforming the second image (B) to the first image (A). The residual error was calculated by comparing the center of mass (COM) of the markers with the predicted COM. Sensitivity to contour variation was calculated by deforming B to the repeat contour of A (A2). The sensitivity to the model size was calculated by reducing the number of nodes ( B ′ ,A ′ , A 2 ′ ) and repeating the analysis. Results: The average residual error of the registration for B to A and B to A2 was 0.22 cm (SD: 0.08 cm ) and 0.24 cm (SD: 0.09 cm ), respectively. The average residual error of the registration ofB ′toA ′andB ′to A 2 ′was 0.22 cm (SD: 0.10 cm ) and 0.25 cm (SD: 0.10 cm ), respectively. The average time to run MORFEUS on the standard and reduced model was 3606 s (SD: 7788 s ) and 56 s (SD: 16 s ), respectively. Conclusions: The accuracy of the algorithm, equal to the image voxel size, is not affected by intraobserver contour variability or model size. Reducing the model size significantly increases algorithm efficiency.