Robust multiresolution alignment of MRI brain volumes
By Oscar Nestares, David J. Heeger. Magnetic Resonance in Medicine, 2000
318 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/ebd4778dc1a3df8da81599f733504ff40749b2f2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Bruce Fischl
- Martin Reuter
- Robert F. Dougherty
- Alexander C. Huk
- Brian A. Wandell
- Rory Sayres
- Kalanit Grill-Spector
- Kevin S. Weiner
In Radiology, Nuclear Medicine and Imaging
- Martin Reuter
- Bruce Fischl
- Brian A. Wandell
- Serge O. Dumoulin
- Robert F. Dougherty
- Alyssa A. Brewer
- Alexander C. Huk
- Kalanit Grill-Spector
Citations in the corpus
- Retinotopy and Functional Subdivision of Human Areas MT and MST (2002, methodology): “The inplane anatomical images were aligned to a high-resolution anatomical volume of each subject's brain so that all MR images (across multiple scanning sessions) from a given subject were coregistered with an accuracy of ϳ1 mm (Nestares and Heeger, 2000).”
- Decoding and reconstructing color from responses in human visual cortex (2009, unknown): “We compensated for head movements within and across runs using a robust motion estimation algorithm (Nestares and Heeger, 2000), divided the time series of each voxel by its mean image intensity to convert to percentage signal change and compensate for distance from the radio frequency coil, and lin”
- fMRI-adaptation and category selectivity in human ventral temporal cortex: regional differences across time scales. (2010, methodology): “Functional data were motion corrected using an affine transformation (Nestares and Heeger 2000).”
- Avoiding asymmetry-induced bias in longitudinal image processing (2011, methodology): “Furthermore , based on robust statistics (Nestares and Heeger, 2000), our algorithm increases the accuracy of the registration by reducing the influence of outlier regions (different jaw, neck, eye positions, motion artifacts, morphological change such as atrophy or tumor growths, or differences…”
- Visual field representations and locations of visual areas V1/2/3 in human visual cortex. (2003, methodology): “Data are shown for the left hemisphere of one subject. anatomy data via a semi-automated 3D coregistration algorithm (Nestares & Heeger, 2000).”
- Population receptive field estimates in human visual cortex (2008, background): “Head movement and motion artifacts within and between scans were measured (Nestares and Heeger, 2000).”