Journal ArticleDOI
Design and construction of a realistic digital brain phantom
D. L. Collins,Alex P. Zijdenbos,V. Kollokian,John G. Sled,Noor Jehan Kabani,Colin J. Holmes,Alan C. Evans +6 more
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TLDR
The authors present a realistic, high-resolution, digital, volumetric phantom of the human brain, which can be used to simulate tomographic images of the head and is the ideal tool to test intermodality registration algorithms.Abstract:
After conception and implementation of any new medical image processing algorithm, validation is an important step to ensure that the procedure fulfils all requirements set forth at the initial design stage. Although the algorithm must be evaluated on real data, a comprehensive validation requires the additional use of simulated data since it is impossible to establish ground truth with in vivo data. Experiments with simulated data permit controlled evaluation over a wide range of conditions (e.g., different levels of noise, contrast, intensity artefacts, or geometric distortion). Such considerations have become increasingly important with the rapid growth of neuroimaging, i.e., computational analysis of brain structure and function using brain scanning methods such as positron emission tomography and magnetic resonance imaging. Since simple objects such as ellipsoids or parallelepipedes do not reflect the complexity of natural brain anatomy, the authors present the design and creation of a realistic, high-resolution, digital, volumetric phantom of the human brain. This three-dimensional digital brain phantom is made up of ten volumetric data sets that define the spatial distribution for different tissues (e.g., grey matter, white matter, muscle, skin, etc.), where voxel intensity is proportional to the fraction of tissue within the voxel. The digital brain phantom can be used to simulate tomographic images of the head. Since the contribution of each tissue type to each voxel in the brain phantom is known, it can be used as the gold standard to test analysis algorithms such as classification procedures which seek to identify the tissue "type" of each image voxel. Furthermore, since the same anatomical phantom may be used to drive simulators for different modalities, it is the ideal tool to test intermodality registration algorithms. The brain phantom and simulated MR images have been made publicly available on the Internet (http://www.bic.mni.mcgill.ca/brainweb).read more
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Journal ArticleDOI
Three-dimensional multimodal brain warping using the Demons algorithm and adaptive intensity corrections
TL;DR: The authors argue that their intensity modeling may be more appropriate than mutual information (MI) in the context of evaluating high-dimensional deformations, as it puts more constraints on the parameters to be estimated and, thus, permits a better search of the parameter space.
Journal ArticleDOI
Robust Rician noise estimation for MR images.
Pierrick Coupé,Pierrick Coupé,José V. Manjón,Elias L. Gedamu,Elias L. Gedamu,Douglas L. Arnold,Douglas L. Arnold,Montserrat Robles,D. Louis Collins,D. Louis Collins +9 more
TL;DR: The main advantage of this object-based method is its robustness to background artefacts such as ghosting, and within the validation on real data, the proposed method obtained very competitive results compared to the methods under study.
Journal ArticleDOI
Automatic segmentation and reconstruction of the cortex from neonatal MRI.
Hui Xue,Latha Srinivasan,Shuzhou Jiang,Mary A. Rutherford,A. David Edwards,Daniel Rueckert,Joseph V. Hajnal +6 more
TL;DR: An automatic segmentation algorithm detecting mislabeled voxels during cortical segmentation and correcting errors caused by partial volume effects is proposed and results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic expectation maximization (EM) scheme.
Journal ArticleDOI
Automatic independent component labeling for artifact removal in fMRI.
Jussi Tohka,Karin Foerde,Karin Foerde,Adam R. Aron,Adam R. Aron,Sabrina M. Tom,Sabrina M. Tom,Arthur W. Toga,Arthur W. Toga,Russell A. Poldrack +9 more
TL;DR: It is concluded that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.
Journal ArticleDOI
Prognostic Significance of Growth Kinetics in Newly Diagnosed Glioblastomas Revealed by Combining Serial Imaging with a Novel Biomathematical Model
Christina Wang,Jason K. Rockhill,Maciej M. Mrugala,Danielle L. Peacock,Albert Lai,Katy Jusenius,Joanna M. Wardlaw,Timothy F. Cloughesy,Alexander M. Spence,Russell C. Rockne,Ellsworth C. Alvord,Kristin R. Swanson +11 more
TL;DR: This is the first report indicating that dynamic insight from routinely obtained pretreatment imaging may be quantitatively useful in characterizing the survival of individual patients with glioblastoma.
References
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Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
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Introduction to artificial neural systems
TL;DR: Jacek M. Zurada is a Professor with the Electrical and Computer Engineering Department at the University of Louisville, Kentucky and has published over 350 journal and conference papers in the areas of neural networks, computational intelligence, data mining, image processing and VLSI circuits.