Online citations, reference lists, and bibliographies.
← Back to Search

Twenty New Digital Brain Phantoms For Creation Of Validation Image Data Bases

B. Aubert-Broche, M. Griffin, G. B. Pike, Alan C. Evans, D. Collins
Published 2006 · Computer Science, Medicine

Save to my Library
Download PDF
Analyze on Scholarcy
Simulations provide a way of generating data where ground truth is known, enabling quantitative testing of image processing methods. In this paper, we present the construction of 20 realistic digital brain phantoms that can be used to simulate medical imaging data. The phantoms are made from 20 normal adults to take into account intersubject anatomical variabilities. Each digital brain phantom was created by registering and averaging four T1, T2, and proton density (PD)-weighted magnetic resonance imaging (MRI) scans from each subject. A fuzzy minimum distance classification was used to classify voxel intensities from T1, T2, and PD average volumes into grey-matter, white matter, cerebro-spinal fluid, and fat. Automatically generated mask volumes were required to separate brain from nonbrain structures and ten fuzzy tissue volumes were created: grey matter, white matter, cerebro-spinal fluid, skull, marrow within the bone, dura, fat, tissue around the fat, muscles, and skin/muscles. A fuzzy vessel class was also obtained from the segmentation of the magnetic resonance angiography scan of the subject. These eleven fuzzy volumes that describe the spatial distribution of anatomical tissues define the digital phantom, where voxel intensity is proportional to the fraction of tissue within the voxel. These fuzzy volumes can be used to drive simulators for different modalities including MRI, PET, or SPECT. These phantoms were used to construct 20 simulated T1-weighted MR scans. To evaluate the realism of these simulations, we propose two approaches to compare them to real data acquired with the same acquisition parameters. The first approach consists of comparing the intensities within the segmented classes in both real and simulated data. In the second approach, a whole brain voxel-wise comparison between simulations and real T1-weighted data is performed. The first comparison underlines that segmented classes appear to properly represent the anatomy on average, and that inside these classes, the simulated and real intensity values are quite similar. The second comparison enables the study of the regional variations with no a priori class. The experiments demonstrate that these variations are small when real data are corrected for intensity nonuniformity
This paper references
Geometric Flows for Segmenting Vasculature in MRI: Theory and Validation
M. Descoteaux (2004)
Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain
N. Tzourio-Mazoyer (2002)
3D statistical neuroanatomical models from 305 MRI volumes
Alan C. Evans (1993)
Reliability of tissue volumes and their spatial distribution for segmented magnetic resonance images
V. Cardenas (2001)
PET-SORTEO: validation and development of database of Simulated PET volumes
A. Reilhac (2005)
Fast robust automated brain extraction
S. Smith (2002)
Automatic 3D Intersubject Registration of MR Volumetric Data in Standardized Talairach Space
D. L. Collins (1994)
BET2-MR-based estimation of brain, skull, and scalp surfaces
M. Jenkinson (2005)
Muliscale Vessel Enhancement Filtering
A. Frangi (1998)
A unified statistical approach for determining significant signals in images of cerebral activation
K. Worsley (1996)
A nonparametric method for automatic correction of intensity nonuniformity in MRI data
J. Sled (1998)
Qualitative and Quantitative Evaluation of Six Algorithms for Correcting Intensity Nonuniformity Effects
J. B. Arnold (2001)
MRI simulation-based evaluation of image-processing and classification methods
R.K.-S. Kwan (1999)
Validation of Partial Tissue Segmentation of Single-Channel Magnetic Resonance Images of the Brain
T. Grabowski (2000)
Automated Separation of Gray and White Matter from MR Images of the Human Brain
H. Schnack (2001)
Automatic Generation of Training Data for Brain Tissue Classification from MRI
C. Cocosco (2002)
Voxel-Based Morphometry—The Methods
J. Ashburner (2000)
Preliminary Experience With The Photon History Generator Module Of A Public-domain Simulation System For Emission Tomography
R. Harrison (1993)
A new improved version of the realistic digital brain phantom
B. Aubert-Broche (2006)

This paper is referenced by
Improved model prediction of glioma growth utilizing tissue-specific boundary effects.
J. Jacobs (2019)
Multi-Cues Regularized Least-Squares applied to Brain MRI Segmentation
C. Basso (2008)
A groupwise super-resolution approach: Application to brain MRI
F. Rousseau (2010)
Non-rigid image registration of brain magnetic resonance images using graph-cuts
Ronald W. K. So (2011)
Effects of Spatial Pattern Scale of Brain Activity on the Sensitivity of DOT, fMRI, EEG and MEG
Katherine L. Perdue (2013)
Clustering of atlas-defined cortical regions based on relaxation times and proton density
B. Aubert-Broche (2009)
A unified Bayesian mixture model framework via spatial information for grayscale image segmentation
Taisong Xiong (2016)
Robust Estimation of Unbalanced Mixture Models on Samples with Outliers
A. Galimzianova (2015)
A cross-center smoothness prior for variational Bayesian brain tissue segmentation
S. Bao (2019)
A scalable method to improve gray matter segmentation at ultra high field MRI
O. F. Gulban (2018)
SPARC: Unified framework for automatic segmentation, probabilistic atlas construction, registration and clustering of brain MR images
A. Ribbens (2010)
Higher SNR PET image prediction using a deep learning model and MRI image.
Chih-Chieh Liu (2019)
FEM-based evaluation of deformable image registration for radiation therapy.
H. Zhong (2007)
Spectral-spatial classification of n-dimensional images in real-time based on segmentation and mathematical morphology on GPUs
P. Barriuso (2015)
Tissue segmentation of the cerebellum from MR images
Keri J Woods (2011)
N4ITK: Improved N3 Bias Correction
N. Tustison (2010)
PET Image Denoising Using a Deep Neural Network Through Fine Tuning
K. Gong (2019)
Unsupervised Clustering Using Diffusion Maps for Local Shape Modelling
Daniel Valdés-Amaro (2009)
Tensor-Factor Analysis
Andrew Stevens (2016)
Inhomogeneity Correction in Magnetic Resonance Images Using Deep Image Priors
Shuo Han (2020)
Removal of CSF pixels on brain MR perfusion images using first several images and Otsu's thresholding technique
Y. Kao (2010)
Statistical shape analysis for bio-structures : local shape modelling, techniques and applications
V. Amaro (2009)
Multimodal imaging and image analysis techniques for neuromodulation.
M. Chakravarty (2012)
Simulation of brain tumors in MR images for evaluation of segmentation efficacy
Marcel Prastawa (2009)
Improving model-based fNIRS analysis using mesh-based anatomical and light-transport models
Anh Phong Tran (2020)
M. Balafar (2013)
An Open Source Multivariate Framework for n-Tissue Segmentation with Evaluation on Public Data
B. Avants (2011)
Quantitative assessment of diffuse optical tomography sensitivity to the cerebral cortex using a whole-head probe.
Katherine L. Perdue (2012)
Midsaggital Plane Detection in Magnetic Resonance Images Using Phase Congruency, Hessian Matrix and Symmetry Information: A Comparative Study
Paulo Guilherme de Lima Freire (2018)
A scalable method to improve gray matter segmentation at ultra high field MRI
O. F. Gulban (2018)
Blood Flow Modeling in a Synthetic Cylindrical Vessel for Validating Methods of Vessel Segmentation in MRA Images
Grzegorz Dwojakowski (2013)
Off-line determination of the optimal number of iterations of the robust anisotropic diffusion filter applied to denoising of brain MR images
R. Ferrari (2012)
See more
Semantic Scholar Logo Some data provided by SemanticScholar