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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Proceedings ArticleDOI
A combined feature ensemble based mutual information scheme for robust inter-modal, inter-protocol image registration
TL;DR: Improvement in registration accuracy is demonstrated by using the COFEMI scheme over the traditional intensity based-Mi scheme in registering prostate whole mount histological sections with corresponding magnetic resonance imaging (MRI) slices and phantom brain T1 and T2 MRI studies, which were adversely affected by imaging artifacts.
Proceedings ArticleDOI
FCD segmentation using texture asymmetry of MR-T1 images of the brain
TL;DR: A new FCD segmentation technique based on analysis of texture asymmetry is presented, which does not rely on template-based segmentation and is applicable to patients of any age, regardless of anatomic variations.
Journal ArticleDOI
Test–retest reliability of the novel 5-HT1B receptor PET radioligand [11C]P943
Aybala Saricicek,Aybala Saricicek,Jason I. Chen,Beata Planeta,Barbara M. Ruf,Kalyani Subramanyam,Kathleen Maloney,David Matuskey,David Labaree,Lorenz Deserno,Lorenz Deserno,Alexander Neumeister,Alexander Neumeister,John H. Krystal,Jean-Dominique Gallezot,Yiyun Huang,Richard E. Carson,Zubin Bhagwagar,Zubin Bhagwagar +18 more
TL;DR: Reliable measures of 5-HT1B receptor binding can be obtained using the novel PET radioligand [11C]P943 and the power analyses showed a greater number of subjects were required using MA1 BPF compared with other outcome measures for both within-subject and between-subject study designs.
Journal ArticleDOI
Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies
TL;DR: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric image with very similar noise properties to those of MC images, in a fraction of the time.
Book ChapterDOI
A fast and automatic method to correct intensity inhomogeneity in MR brain images
TL;DR: This paper presents a method to improve the semi-automatic method for intensity inhomogeneity correction by Dawant et al through introducing a fully automatic approach to reference points generation, which is based on order statistics and integrates information from the fine to coarse scale representations of the input image.
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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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.