Voxel-Based Morphometry Using the RAVENS Maps: Methods and Validation Using Simulated Longitudinal Atrophy

By Christos Davatzikos, Ahmet Genç, Dongrong Xu, Susan M. Resnick. 2001

464 citations recorded by the source corpus.

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

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “For this experiment we used grey matter tissue density (RAVENS (Davatzikos et al., 2001)) maps generated using ADNI data that was provided to us by authors of (Davatzikos et al., 2011).”
Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: Results from ADNI (2008, unknown): “The RAVENS approach has been extensively validated (Goldszal et al.,1998; Davatzikos et al., 2001) and applied to a variety of studies (Resnick et al., 2000; Resnick et al., 2001; Kim et al., 2003; Resnick et al., 2003, 2004; Beresford et al., 2006a,b; Gur et al., 2006; Driscoll et al., 2007;…”
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis (2014, unknown): “Then, the regional volumetric maps, called RAVENS maps, were generated by a tissue preserving image warping method (Davatzikos et al., 2001).”
User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability (2006, background): “Methods based on elastic and fluid registration can identify anatomical structures in the brain by deforming a labeled probabilistic brain atlas onto the subject brain (Joshi and Miller, 2000; Avants et al., in press; Davatzikos et al., 2001; Thirion, 1996).”
Generative-Discriminative Basis Learning for Medical Imaging (2012, methodology): “In morphological analysis, voxel values are typically either: a Jacobian determinant of the deformation [1], transformation-residuals [2], tissue density maps [3], [4], or voxel intensity (e.”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, methodology): “A variety of VBM-type methods exist (Ashburner and Friston, 2000; Chung et al., 2001; Davatzikos et al., 2001, 1996; Thompson et al., 2001), which generally fall under the umbrella of the field of computational neuroanatomy (Ashburner et al., 2003; Miller et al., 1997).”
Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks (2018, methodology): “Region of interest (ROI)-based approach utilizes the structurally or functionally predefined brain regions and extracts representative features from each region [21, 25, 28, 30, 53–55].”
Tensor Regression with Applications in Neuroimaging Data Analysis (2012, methodology): “We quantified the local volumetric group differences by generating RAVENS maps (Davatzikos et al., 2001) for the whole brain and each of the segmented tissue type (GM, WM, VN, and CSF) respectively, using the deformation field we obtained during registration.”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “For the experiment detailed next we obtained these RAVENS maps directly from the authors of Davatzikos et al. (2011).”
Morphological classification of medical images using nonlinear support vector machines (2004, methodology): “In order to partly overcome this limitation, we have adopted the framework of mass-preserving shape transformations, described in detail in [1, 4, 5].”
Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease (2010, background): “The majority of previous studies described characteristically altered brain areas in AD or MCI on a group-level (Apostolova et al., 2007; Bozzali et al., 2006; Carlson et al., 2007; Davatzikos et al., 2001).”
RABBIT: Rapid Alignment of Brains by Building Intermediate Templates (2009, unknown): “Besides directly measuring the registration accuracy, we also evaluate the performance of RABBIT registration algorithm with respect to its sensitivity in detecting brain atrophy by using voxelbased morphometry (VBM) (Ashburner et al., 2003; Ashburner and Friston, 2000; Davatzikos et al., 2001).”
Deep ensemble learning of sparse regression models for brain disease diagnosis (2017, methodology): “Next, we generated the regional volumetric maps, called RAVENS maps, using a tissue preserving image warping method ( Davatzikos et al., 2001 ).”
Regional Frontal Cortical Volumes Decrease Differentially in Aging: An MRI Study to Compare Volumetric Approaches and Voxel-Based Morphometry (2002, result): “This variability (even after nonlinear warping and smoothing; e.g., Uylings et al., 2000a; Bookstein, 2001; Davatzikos et al., 2001 ) leads to false-negative results (i.e., a failure to detect true effects) and may have influenced our VBM results.”
Voxel-based morphometry of the human brain: Methods and applications (2005, methodology): “RAVENS; [2]) that could be used for the pre-processing of the data, but here we describe the steps that are routinely implemented using the SPM2 software [3].”
A review on neuroimaging‐based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages (2017, unknown): “Density map-based methods Density map-based methods quantify patterns of atrophy by utilizing density maps of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF), which are generated by methods such as voxel-based morphometry (VBM) (Ashburner and Friston, 2000) or regional analysis of”