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
Neuroanatomical database of normal Japanese brains
TL;DR: There exists cross-generational changes in brain shape, that is, the young generation has a shorter and wider brain than the older generation, and the effect of aging on the volume of gray matter and white matter is determined by voxel based morphometry.
Book ChapterDOI
State of the Art of Level Set Methods in Segmentation and Registration of Medical Imaging Modalities
TL;DR: This work states that in situations where the initial model and desired object boundary differ greatly in size and shape, the model must be reparameterized dynamically to faithfully recover the object boundary, parametric deformable models have two main limitations.
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Associations of age, gender and body mass with 1H MR-observed brain metabolites and tissue distributions
TL;DR: There is sufficient evidence to warrant the inclusion of body weight as a subject selection parameter, secondary to age, and as a factor in data analysis for MRS studies of some brain regions.
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M3Net: A multi-model, multi-size, and multi-view deep neural network for brain magnetic resonance image segmentation
Jie Wei,Yong Xia,Yanning Zhang +2 more
TL;DR: A multi-model, multi-size and multi-view deep neural network (M3Net) for brain MR image segmentation, which uses three identical modules to segment transaxial, coronal, and sagittal MR slices, respectively.
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Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network
TL;DR: Experimental results show that the proposed CNN-based anisotropic MR image reconstruction method outperforms classical interpolation methods, non-local means method (NLM), and sparse coding based algorithm in terms of peak signal-to-noise-ratio, structural similarity image index, intensity profile, and small structures.
References
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Journal ArticleDOI
Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space
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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.