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Avoiding asymmetry-induced bias in longitudinal image processing
2011
NeuroImage
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Longitudinal image processing procedures frequently transfer or pool information across time within subject, with the dual goals of reducing the variability and increasing the accuracy of the derived measures ...
In order to reduce variability and detect true change in structure or function, it is important to be able to transfer and share information across time. ...
Biased registration (lack of inverse consistency) In order to study differences across time within the same subject, images are frequently registered using either a rigid or affine registration or higher ...
doi:10.1016/j.neuroimage.2011.02.076
pmid:21376812
pmcid:PMC3260043
fatcat:2uveduwnqfdahcr72bkq3sbs6u
Multi-Contrast Computed Tomography Healthy Kidney Atlas
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2012.12432v1 fatcat:s55k7cezvngn3eznobnnzztfae
Briefly, we introduce a deep learning-based volume of interest extraction method and an automated two-stage hierarchal registration pipeline to register abdominal volumes to a high-resolution CT atlas ...
The identified datasets used for the analysis described were obtained from the Research Derivative (RD), database of clinical and related data. ...
This study was in part using the resources of the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University, Nashville, TN. ...
arXiv:2012.12432v2
fatcat:q24uyoiaavbydlznmfxpwaw4dm
Towards respiration management in radiation treatment of lung tumors: Transferring regions of interest from planning CT to kilovoltage X-ray images
2010
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology
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Our approach gives accurate results for rigid registration and provides a starting point to track tumors using the X-ray images during the treatment. ...
Our approach generates Digitally Reconstructed Radiographs (DRRs) using bilateral filter smoothing and computes rigid registration with kV images since the position and orientation of patient might differ ...
The parameters of the rigid registration (R, t) are selected such that the inner product between the density functions ζ 1 (x) and ζ 2 (f (x)) is maximized, where ζ 1 and ζ 2 are the KDE estimates of a ...
doi:10.1109/iembs.2010.5626126
pmid:21095744
fatcat:mv6cpqinqbg3jg2owbzfi6h3h4
Alignment of Tractography Streamlines using Deformation Transfer via Parallel Transport
[article]
2021
arXiv
pre-print
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We present a novel deformation metric between tracts that allows one to compare tracts while simultaneously obtaining a registration. ...
Another approach by O'Donnell et al. has used distributions of fibers for unbiased group registration of tractography representations [13] . ...
registration over rigid alignment. ...
arXiv:2108.03697v1
fatcat:nie2dqmce5gwxgplxgzshzlmce
Framework for integrated MRI average of the spinal cord white and gray matter: The MNI–Poly–AMU template
2014
NeuroImage
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The template was created by co-registering T 2 -weighted images (N=16 healthy volunteers) using a series of pre-processing steps followed by non-linear registration. ...
) and unbiased extraction of multi-parametric data. ...
These segmented structures can be used as ROIs for multiparametric MRI techniques (e.g., diffusion imaging, magnetization transfer, functional MRI). ...
doi:10.1016/j.neuroimage.2014.08.057
pmid:25204864
fatcat:lquuachaybffdbzfxsjo5povgq
Unbiased diffeomorphic atlas construction for computational anatomy
2004
NeuroImage
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and object segmentation via registration of anatomical labels. ...
Large sets of images are mapped into a common coordinate system to study intra-population variability and inter-population differences, to provide voxel-wise mapping of functional sites, and help tissue ...
This supports use of deformable atlas registration where constraints are provided by a volumetric, unbiased atlas. ...
doi:10.1016/j.neuroimage.2004.07.068
pmid:15501084
fatcat:q4uftrvdnfdkbdkgucvegac6ku
A Motion Correction Strategy for Multi-Contrast based 3D parametric imaging: Application to Inhomogeneous Magnetization Transfer (ihMT)
[article]
2020
bioRxiv
pre-print
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The proposed method was compared to Motion Correction with FMRIB's Linear Image Registration Tool (MCFLIRT) function in FSL over 13 subjects. ...
Methods A framework for motion correction, including image pre-processing enhancement and rigid registration to an iteratively improved target image, was developed. ...
Diffusion Tensor Imaging, Magnetization Transfer) and functional (e.g. Arterial Spin Labeling) evaluations. ...
doi:10.1101/2020.09.11.292649
fatcat:shooargvv5acfiuqn3csa4nhby
Within-subject template estimation for unbiased longitudinal image analysis
2012
NeuroImage
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In this paper we introduce a novel longitudinal image processing framework, based on unbiased, robust, withinsubject template creation, for automatic surface reconstruction and segmentation of brain MRI ...
Then use the rigid transforms to initialize an iterative affine registration employing the intensity normalized full head images. 3. ...
Fine tune those affine registrations by using the affine maps to initialize a final set of registrations of the skull stripped brain images to the template, where only rigid parameters are allowed to change ...
doi:10.1016/j.neuroimage.2012.02.084
pmid:22430496
pmcid:PMC3389460
fatcat:gv4muaiizvfsxizgqkoi6ixupm
Automatic brain segmentation in rhesus monkeys
2007
Medical Imaging 2007: Image Processing
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We determine the atlas from training images by iterative, joint deformable registration into an unbiased average image. On this atlas, probabilistic tissue maps and a lobar parcellation. ...
Other regional definitions on the atlas can also straightforwardly be used as segmentation. ...
These right hemispheric definitions were mirrored at the midsagittal plane using a simple axial flip operation followed by rigid registration to produce the initial left hemispheric lobar parcellation. ...
doi:10.1117/12.710027
dblp:conf/miip/StynerKJCSG07
fatcat:3seeld5rl5c5xkofy4tu7uo5fu
Comprehensive ultrahigh resolution whole brain in vivo MRI dataset as a human phantom
2021
Scientific Data
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reconstruction, 330 µm quantitative susceptibility mapping, up to 450 µm structural T2-weighted imaging, 700 µm T1-weighted back-to-back scans, 800 µm diffusion tensor imaging, one hour continuous resting-state functional ...
However, before doing so, the different directions were motion corrected by rigid registration of the time series data to an unbiased template. ...
In a multistage rigid registration approach, an average volume from all directions was created and then each direction was registered to this unbiased average. ...
doi:10.1038/s41597-021-00923-w
pmid:34035308
fatcat:xa7ojefsvvgknjodkct3swzne4
Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain
2011
Anatomy Research International
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Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. ...
functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. ...
All cases used CC cost function and TPS (N/A to rigid case), except for CG, which is implemented only for SSD and B-Splines. ...
doi:10.1155/2011/287860
pmid:22567290
pmcid:PMC3335496
fatcat:qdwyg6fczzhsxnw4iiqdf7pjri
Front Matter: Volume 10574
2018
Medical Imaging 2018: Image Processing
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using a Base 36 numbering system employing both numerals and letters. ...
SPIE uses a seven-digit CID article numbering system structured as follows: The first five digits correspond to the SPIE volume number. The last two digits indicate publication order within the volume ...
10574 06 Constructing statistically unbiased cortical surface templates using feature-space covariance SESSION 2 DEEP LEARNING: SEGMENTATION
07 Segmentation of anatomical structures in cardiac CTA using ...
doi:10.1117/12.2315755
fatcat:jdfbaent6vhu5dwlrqrqt66vce
Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens
2013
NeuroImage
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registration with the ex-vivo MRI. ...
We evaluated our registration method on specimens resected from patients undergoing anterior temporal lobectomy (N=7) and found our method to have a mean target registration error of 0.76 ± 0.66 and 0.98 ...
We used a fast non-rigid registration that makes use of a B-spline deformation field and a normalized mutual information cost-function (Rueckert et al., 1999; Modat et al., 2010) (NiftyReg,http://sourceforge.net ...
doi:10.1016/j.neuroimage.2013.07.053
pmid:23891884
fatcat:hlndu6kf2zdftijcarvv4yyxde
Performance Evaluation of Grid-Enabled Registration Algorithms Using Bronze-Standards
[chapter]
2006
Lecture Notes in Computer Science
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We computed the accuracy of 4 different rigid registration algorithms on longitudinal MRI images of brain tumors. Results showed an average subvoxel accuracy of 0.4 mm and 0.15 degrees in rotation. ...
Performance Evaluation Using Bronze Standards The accuracy performances of registration algorithms are critical for many clinical procedures but quantifying them is difficult due to the lack of gold standard ...
A complete system would include an ontology of registration problems (modalities, anatomical region, rigid or non-rigid registration, etc), of registration algorithms (input/output data types, method used ...
doi:10.1007/11866763_19
fatcat:6jtwbikqengnha6edy5mzkje54
On Removing Interpolation and Resampling Artifacts in Rigid Image Registration
2013
IEEE Transactions on Image Processing
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These artifacts negatively affect the accuracy of registration by producing local optima, altering the gradient, shifting the global optimum, and making rigid registration asymmetric. ...
We show that image registration using conventional interpolation and summation approximations of continuous integrals can generally fail because of resampling artifacts. ...
They showed that these artifacts disappear if the resampling step is skipped, which they proposed to do in rigid registration by computing the objective function using stochastic integration (instead of ...
doi:10.1109/tip.2012.2224356
pmid:23076044
pmcid:PMC3694571
fatcat:vzu6liuozjclno7ao4f3wsk2n4
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