Validation for 2D/3D registration II: The comparison of intensity- and gradient-based merit functions using a new gold standard data set

Purpose: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for re...

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Bibliographic Details
Published in:Medical physics (Lancaster) 2011-03, Vol.38 (3), p.1491-1502
Main Authors: Gendrin, Christelle, Markelj, Primož, Pawiro, Supriyanto Ardjo, Spoerk, Jakob, Bloch, Christoph, Weber, Christoph, Figl, Michael, Bergmann, Helmar, Birkfellner, Wolfgang, Likar, Boštjan, Pernuš, Franjo
Format: Article
Language:English
Subjects:
2D/3D registration
60 APPLIED LIFE SCIENCES
ACCURACY
ALGORITHMS
Animals
biomedical MRI
COMPARATIVE EVALUATIONS
Computed radiography
Computed tomography
computerised tomography
COMPUTERIZED TOMOGRAPHY
Cone beam computed tomography
Databases, Factual
DEFORMATION
diagnostic radiography
Digital radiography
gradient
gradient methods
HEAD
Head - diagnostic imaging
HEALTH HAZARDS
IMAGE PROCESSING
image reconstruction
image registration
image-guided therapy
Imaging, Three-Dimensional - methods
Imaging, Three-Dimensional - standards
IMPLEMENTATION
intensity
MAGNETIC RESONANCE
Magnetic Resonance Imaging
medical image processing
Medical imaging
Medical X‐ray imaging
Multislice
NMR IMAGING
PHANTOMS
Radiation therapy
RADIOLOGY AND NUCLEAR MEDICINE
Radiotherapy, Computer-Assisted
ray-casting
Reconstruction
Reference Standards
Registration
rendering
Soft X‐rays
Swine
Tissues
Tomography, X-Ray Computed
VALIDATION
X RADIATION
X‐ray imaging
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Summary:Purpose: A new gold standard data set for validation of 2D/3D registration based on a porcine cadaver head with attached fiducial markers was presented in the first part of this article. The advantage of this new phantom is the large amount of soft tissue, which simulates realistic conditions for registration. This article tests the performance of intensity- and gradient-based algorithms for 2D/3D registration using the new phantom data set. Methods: Intensity-based methods with four merit functions, namely, cross correlation, rank correlation, correlation ratio, and mutual information (MI), and two gradient-based algorithms, the backprojection gradient-based (BGB) registration method and the reconstruction gradient-based (RGB) registration method, were compared. Four volumes consisting of CBCT with two fields of view, 64 slice multidetector CT, and magnetic resonance-T1 weighted images were registered to a pair of kV x-ray images and a pair of MV images. A standardized evaluation methodology was employed. Targets were evenly spread over the volumes and 250 starting positions of the 3D volumes with initial displacements of up to 25 mm from the gold standard position were calculated. After the registration, the displacement from the gold standard was retrieved and the root mean square (RMS), mean, and standard deviation mean target registration errors (mTREs) over 250 registrations were derived. Additionally, the following merit properties were computed: Accuracy, capture range, number of minima, risk of nonconvergence, and distinctiveness of optimum for better comparison of the robustness of each merit. Results: Among the merit functions used for the intensity-based method, MI reached the best accuracy with an RMS mTRE down to 1.30 mm. Furthermore, it was the only merit function that could accurately register the CT to the kV x rays with the presence of tissue deformation. As for the gradient-based methods, BGB and RGB methods achieved subvoxel accuracy (RMS mTRE down to 0.56 and 0.70 mm, respectively). Overall, gradient-based similarity measures were found to be substantially more accurate than intensity-based methods and could cope with soft tissue deformation and enabled also accurate registrations of the MR-T1 volume to the kV x-ray image. Conclusions: In this article, the authors demonstrate the usefulness of a new phantom image data set for the evaluation of 2D/3D registration methods, which featured soft tissue deformation. The author’s evaluation s
ISSN:0094-2405
2473-4209
DOI:10.1118/1.3553403