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
Citations
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Journal ArticleDOI
Self-supervised MRI tissue segmentation by discriminative clustering.
TL;DR: In this article, a self-supervised machine learning approach is proposed for brain lesion segmentation, which is based on a discriminative strategy in a selfsupervised approach.
Journal ArticleDOI
Fast Parallel MR Image Reconstruction via B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA)
TL;DR: The proposed majorize-minimize methods (called BARISTA) converge faster than state-of-the-art variable splitting algorithms when combined with momentum acceleration and adaptive momentum restarting and the tuning parameters associated with the proposed methods are unitless convergence tolerances that are easier to choose than the constraint penalty parameters required by variable splitting methods.
Book ChapterDOI
MRI Denoising Using Deep Learning
José V. Manjón,Pierrick Coupé +1 more
TL;DR: A new method for MRI denoising that combines recent advances in deep learning with classical approaches for noise reduction is presented that follows a two-stage strategy.
Journal ArticleDOI
MRI Tissue Classification Using High-Resolution Bayesian Hidden Markov Normal Mixture Models
TL;DR: The subvoxel approach provides more accurate tissue classification and also allows more effective estimation of the proportion of major tissue types present in each voxel for both simulated and real datasets.
Journal ArticleDOI
Non-Stationary Rician Noise Estimation in Parallel MRI Using a Single Image: A Variance-Stabilizing Approach
TL;DR: A new automatic noise estimation technique for non-stationary Rician noise that overcomes the aforementioned drawbacks and is compared to the main state-of-the-art methods in synthetic and real scenarios.
References
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Co-planar stereotaxic atlas of the human brain : 3-dimensional proportional system : an approach to cerebral imaging
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A nonparametric method for automatic correction of intensity nonuniformity in MRI data
TL;DR: A novel approach to correcting for intensity nonuniformity in magnetic resonance (MR) data is described that achieves high performance without requiring a model of the tissue classes present, and is applied at an early stage in an automated data analysis, before a tissue model is available.
Journal ArticleDOI
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
TL;DR: A fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques and therefore does not suffer the drawbacks involved in user intervention.
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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.