PET-CT image registration in the chest using free-form deformations

By David Mattes, D. Haynor, H. Vesselle, T. Lewellen, W. Eubank. IEEE Transactions on Medical Imaging, 2003

988 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2340b73d0a788755c7536e857c845905a3b614a9

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In Biomedical Engineering

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Survey of Medical Image Registration (2013, background): “Such problems have not prevented these techniques ﬁnding widespread use in the brain [54], the chest [55] the heart [56,57], the liver [58], the breast [59,60] etc.”
Medical image registration: a review (2014, methodology): “2006; Balci et al. 2007; Karac¸ali 2007), the quasi-Newton (Mattes et al. 2003; Loeckx et al. 2010; Khader and Hamza 2011), the stochastic algorithms (e.g. simulated annealing) (Nikou et al. 1999; Loeckx et al. 2003) and evolutionary algorithms (Butz and Thiran 2001; Pataky et al. 2008; Ruijters et…”
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets (2009, methodology): “For this purpose, a negative mutual information cost function [59] is optimized by a stochastic gradient optimizer.”
Computer analysis of computed tomography scans of the lung: a survey (2006, neutral): “[61].”
3D Slicer as an Image Computing Platform for the Quantitative Imaging Network (2012, background): “These modules support point- [40], surface- and intensity-based registration [41,42].”
Elastic registration of biological images using vector-spline regularization (2005, methodology): “These facts motivated us to apply to biological image registration a generalization of some of the most recent works in medical image registration [12], [13], [14].”
Dreaming More Data: Class-dependent Distributions over Diffeomorphisms for Learned Data Augmentation (2015, background): “…is, however, not limited to MNIST: • Image alignment is a routine task in many medical imaging tasks, such as the analysis of magnetic resonance images (MRI) [7, 13, 37], X-ray Computed Tomography (CT) [34, 41], Positron Emission Tomography (PET) [45, 46] and mam-Class-dependent mograms [24, 38].”
Non-rigid image registration: theory and practice. (2004, background): “Such problems have not prevented these techniques finding widespread use (in the brain [61], the chest [62] the heart [63, 64], the liver [65], the breast [66, 67] etc.”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “include the alignment of data sets from different modalities [1],”
Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease (2013, positive): “Areas of application include the alignment of data sets from different modalities (Mattes et al., 2003) to fuse information, comparison of follow-up with base-line scans (Staring et al., 2007) to follow disease development, alignment of different MR sequences for extraction of quantitative MR…”
A Reproducible Evaluation of ANTs Similarity Metric Performance in Brain Image Registration (2010, methodology): “We may then interpolate Q at continuous positions with a cubic B-Spline kernel as described in (Mattes et al., 2003).”
Toward automated segmentation of the pathological lung in CT (2005, methodology): “Their method employs an analytic gradient computation using B-spline Parzen windows to estimate the joint histogram, as suggested by Mattes et al. [ 30 ].”
Fast free-form deformation using graphics processing units (2010, methodology): “Its computation thus requires a joint histogram which, in our implementation, was filled using a Parzen Window (PW) approach[7].”
Evaluation of Optimization Methods for Nonrigid Medical Image Registration Using Mutual Information and B-Splines (2007, methodology): “For , a third-order B-spline is used, which makes it possible to derive an analytic expression for ; see [4] and [21].”