Brain Functional Localization: A Survey of Image Registration Techniques

By A. Gholipour, Nasser Kehtarnavaz, Richard W. Briggs, Michael D. Devous Sr., Kaundinya S. Gopinath. IEEE Transactions on Medical Imaging, 2007

306 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/43b1ef49a78c8bf20cc33f4814a4930bdee52239

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “The reader is referred to [ 21 ] and [35] for an overview on nonparametric methods.”
Survey of Medical Image Registration (2013, background): “However, non rigid registration has been proposed to deal with the effect of nonlinear local distortions in functional images, specifically in EPI sequences used for fMRI [143,144].”
Fast and robust multi-atlas segmentation of brain magnetic resonance images (2010, unknown): “1053-8119/$ – see front matter © 2009 Elsevier Inc. Al doi:10.1016/j.neuroimage.2009.10.026 Please cite this article as: Lötjönen, J.M.P., e (2009), doi:10.1016/j.neuroimage.2009.10. a b s t r a c t a r t i c l e i n f o”
Fast free-form deformation using graphics processing units (2010, background): “While a huge amount of research has been devoted to the methodological development [1] [2], very little research has focused on the computational burden of the proposed algorithms.”
Survey of Non-Rigid Registration Tools in Medicine (2017, background): “Other authors have drawn attention to specific anatomic regions, for example, cardiac image registration [20], or on techniques used for specific purposes, such as brain functional image localization [21], optical breast imaging [22], minimally invasive surgical procedures [23], and coronary heart d”
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration (2018, background): “range from image-based navigation in imaging and image-guided interventions to longitudinal and group analyses [1], [2], [3], [4], [5], [6], [7].”
Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration (2009, methodology): “…use for registering structural MRIs to each other or to templates for neuromorphometric research or as an intermediary to compare functional or physiological data (Gholipour et al., 2007), but some of them exist only as pre-release code made available by their respective authors for this study.”
A review of atlas-based segmentation for magnetic resonance brain images (2011, background): “[13] A.”
Anatomical connectivity patterns predict face-selectivity in the fusiform gyrus (2011, unknown): “The group-average is commonly used as a way to build face-selective ROIs in fMRI studies 21 , and thus provides a standard that a connectivity-based method should meet.”
A review of multimodal image matching: Methods and applications (2021, background): “Most works focus on medical image registration to deliver specific instruction, either aiming at 3D– 2D registration [31,32], deformable registration [29], or registration of different objects such as breast [33,34], brain [35], and vascular [36].”