2010
DOI: 10.1016/j.cmpb.2009.09.002
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Fast free-form deformation using graphics processing units

Abstract: A large number of algorithms have been developed to perform non-rigid registration and it is a tool commonly used in medical image analysis. The free-form deformation algorithm is a well-established technique, but is extremely time consuming. In this paper we present a parallel-friendly formulation of the algorithm suitable for graphics processing unit execution. Using our approach we perform registration of T1-weighted MR images in less than 1 min and show the same level of accuracy as a classical serial impl… Show more

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Cited by 900 publications
(790 citation statements)
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“…Functional images were first realigned, incorporating field maps for inhomogeneity correction whenever available and then coregistered to the new anatomical image. Freesurfer ROIs were also coregistered to the anatomical image using NiftyReg [Modat et al, 2010]. In CONN regression of noise ROIs (without global signal regression) was carried out using the anatomical Compcorr method [Behzadi et al, 2007], along with six movement parameters, followed by band‐pass filtering between 0.009 and 0.08 Hz, calculation of bivariate correlations and application of a Fisher transform.…”
Section: Methodsmentioning
confidence: 99%
“…Functional images were first realigned, incorporating field maps for inhomogeneity correction whenever available and then coregistered to the new anatomical image. Freesurfer ROIs were also coregistered to the anatomical image using NiftyReg [Modat et al, 2010]. In CONN regression of noise ROIs (without global signal regression) was carried out using the anatomical Compcorr method [Behzadi et al, 2007], along with six movement parameters, followed by band‐pass filtering between 0.009 and 0.08 Hz, calculation of bivariate correlations and application of a Fisher transform.…”
Section: Methodsmentioning
confidence: 99%
“…(Wakana et al, 2007), and both the ROI definitions and fiber tract are extremely close to the paper of Wakana et al (2007). Inter/intra-image registration across multiple modalities (Modat et al, 2010;Ourselin et al, 2001) reg_resample/reg_transform…”
Section: Discussionmentioning
confidence: 68%
“…DiffusionKit integrates a powerful and elegant toolkit, NiftyReg (Modat et al, 2010;Ourselin et al, 2001;Rueckert et al, 1999), as the registration module, where it implements a multi-scale-based block-matching strategy to measure the location displacements between images (Ourselin et al, 2001). It contains several key steps for the image registration process, including the initial rough rigid/affine transformation (the reg_aladin function), precise nonlinear registration (the reg_f3d function) and the operation of applying a deformation matrix (the reg_resample function).…”
Section: Image Processingmentioning
confidence: 99%
“…2011) registered to the space of the early and late scans using nonrigid registration (Modat et al. 2010). The corresponding regions were grouped by hemisphere: left and right.…”
Section: Methodsmentioning
confidence: 99%