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Medical Imaging, IEEE Transactions on

Issue 4 • Date April 2007

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Displaying Results 1 - 25 of 25
  • Table of contents

    Publication Year: 2007 , Page(s): C1 - C4
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  • IEEE Transactions on Medical Imaging publication information

    Publication Year: 2007 , Page(s): C2
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  • Guest editorial: Special Issue on Computational Neuroanatomy

    Publication Year: 2007 , Page(s): 425 - 426
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  • Brain Functional Localization: A Survey of Image Registration Techniques

    Publication Year: 2007 , Page(s): 427 - 451
    Cited by:  Papers (44)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2818 KB) |  | HTML iconHTML  

    Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem View full abstract»

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  • A Deformable Registration Method for Automated Morphometry of MRI Brain Images in Neuropsychiatric Research

    Publication Year: 2007 , Page(s): 452 - 461
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2409 KB) |  | HTML iconHTML  

    Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching magnetic resonance imaging (MRI) brain image data obtained from various subjects and with various imaging conditions are solved here. Registration is driven by local forces derived from multimodal point similarity measures which are estimated with the use of joint intensity histogram and tissue probability maps. A spatial deformation model imitating principles of continuum mechanics is used. Five similarity measures are tested in an experiment with image data obtained from the Simulated Brain Database and a quantitative evaluation of the algorithm is presented. Results of application of the method in automated spatial detection of anatomical abnormalities in first-episode schizophrenia are presented View full abstract»

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  • Large Deformation Diffeomorphism and Momentum Based Hippocampal Shape Discrimination in Dementia of the Alzheimer type

    Publication Year: 2007 , Page(s): 462 - 470
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (742 KB) |  | HTML iconHTML  

    In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial momentum leads to analysis of anatomical shape and form in target images without being restricted to small-deformation assumption in the analysis of linear displacements. We apply this approach to a study of dementia of the Alzheimer type (DAT). The left hippocampus in the DAT group shows significant shape abnormality while the right hippocampus shows similar pattern of abnormality. Further, PCA of the initial momentum leads to correct classification of 12 out of 18 DAT subjects and 22 out of 26 control subjects View full abstract»

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  • Simultaneous Registration and Parcellation of Bilateral Hippocampal Surface Pairs for Local Asymmetry Quantification

    Publication Year: 2007 , Page(s): 471 - 478
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (571 KB) |  | HTML iconHTML  

    In clinical applications where structural asymmetries between homologous shapes have been correlated with pathology, the questions of definition and quantification of "asymmetry" arise naturally. When not only the degree but the position of deformity is thought relevant, asymmetry localization must also be addressed. Asymmetries between paired shapes have already been formulated in terms of (nonrigid) diffeomorphisms between the shapes. For the infinity of such maps possible for a given pair, we define optimality as the minimization of deviation from isometry under the constraint of piecewise deformation homogeneity. We propose a novel variational formulation for segmenting asymmetric regions from surface pairs based on the minimization of a functional of both the deformation map and the segmentation boundary, which defines the regions within which the homogeneity constraint is to be enforced. The functional minimization is achieved via a quasi-simultaneous evolution of the map and the segmenting curve, conducted on and between two-dimensional surface parametric domains. We present examples using both synthetic data and pairs of left and right hippocampal structures and demonstrate the relevance of the extracted features through a clinical epilepsy classification analysis View full abstract»

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  • Atlas Renormalization for Improved Brain MR Image Segmentation Across Scanner Platforms

    Publication Year: 2007 , Page(s): 479 - 486
    Cited by:  Papers (12)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1018 KB) |  | HTML iconHTML  

    Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based whole brain segmentation method by introducing an intensity renormalization procedure that automatically adjusts the prior atlas intensity model to new input data. Validation using manually labeled test datasets has shown that the new procedure improves the segmentation accuracy (as measured by the Dice coefficient) by 10% or more for several structures including hippocampus, amygdala, caudate, and pallidum. The results verify that this new procedure reduces the sensitivity of the whole brain segmentation method to changes in scanner platforms and improves its accuracy and robustness, which can thus facilitate multicenter or multisite neuroanatomical imaging studies View full abstract»

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  • Topology-Preserving Tissue Classification of Magnetic Resonance Brain Images

    Publication Year: 2007 , Page(s): 487 - 496
    Cited by:  Papers (15)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2406 KB) |  | HTML iconHTML  

    This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce object-level relationships with little constraint over the geometry. When applied to the problem of brain segmentation, it directly provides a cortical surface with spherical topology while segmenting the main cerebral structures. Validation on simulated and real images characterises the performance of the algorithm with regard to noise, inhomogeneities, and anatomical variations View full abstract»

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  • A Statistical Parts-Based Model of Anatomical Variability

    Publication Year: 2007 , Page(s): 497 - 508
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2655 KB) |  | HTML iconHTML  

    In this paper, we present a statistical parts-based model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence frequency are quantified statistically. The parts-based approach explicitly addresses the case where one-to-one correspondence does not exist between all subjects in a population due to anatomical differences, as model parts are not required to appear in all subjects. The model is constructed through a fully automatic machine learning algorithm, identifying image patterns that appear with statistical regularity in a large collection of subject images. Parts are represented by generic scale-invariant features, and the model can, therefore, be applied to a wide variety of image domains. Experimentation based on 2-D MR slices shows that a PBM learned from a set of 102 subjects can be robustly fit to 50 new subjects with accuracy comparable to 3 human raters. Additionally, it is shown that unlike global models such as the AAM, PBM fitting is stable in the presence of unexpected, local perturbation View full abstract»

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  • Automatic Segmentation of the Caudate Nucleus From Human Brain MR Images

    Publication Year: 2007 , Page(s): 509 - 517
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1823 KB) |  | HTML iconHTML  

    We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of the algorithm was tested with 55 high-resolution T1-weighted magnetic resonance imaging (MRI) datasets, and segmentation results were overlaid onto the original image data for visual inspection. We further evaluated the algorithm by comparing automated segmentation results to a "gold standard" established by human experts for these 55 MR datasets. Quantitative comparison showed a high intraclass correlation between the algorithm and expert as well as high spatial overlap between the regions-of-interest (ROIs) generated from the two methods. The mean spatial overlap plusmn standard deviation (defined by the intersection of the 2 ROIs divided by the union of the 2 ROIs) was equal to 0.873 plusmn 0.0234. The algorithm has been incorporated into a public domain software program written in Java and, thus, has the potential to be of broad benefit to neuroimaging investigators interested in basal ganglia anatomy and function View full abstract»

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  • Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops

    Publication Year: 2007 , Page(s): 518 - 529
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (892 KB) |  | HTML iconHTML  

    In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator View full abstract»

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  • A Geometric Method for Automatic Extraction of Sulcal Fundi

    Publication Year: 2007 , Page(s): 530 - 540
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1659 KB) |  | HTML iconHTML  

    Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangular mesh representing the cortical surface. The input to our algorithm is a triangular mesh representation of an extracted cortical surface as computed by one of several available software packages for performing automated and semi-automated cortical surface extraction. Given this input we first compute a geometric depth measure for each triangle on the cortical surface mesh, and based on this information we extract sulcal regions by checking for connected regions exceeding a depth threshold. We then identify endpoints of each region and delineate the fundus by thinning the connected region while keeping the endpoints fixed. The curves, thus, defined are regularized using weighted splines on the surface mesh to yield high-quality representations of the sulcal fundi. We present the geometric framework and validate it with real data from human brains. Comparisons with expert-labeled sulcal fundi are part of this validation process View full abstract»

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  • Automated Extraction of the Cortical Sulci Based on a Supervised Learning Approach

    Publication Year: 2007 , Page(s): 541 - 552
    Cited by:  Papers (8)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3641 KB) |  | HTML iconHTML  

    It is important to detect and extract the major cortical sulci from brain images, but manually annotating these sulci is a time-consuming task and requires the labeler to follow complex protocols , . This paper proposes a learning-based algorithm for automated extraction of the major cortical sulci from magnetic resonance imaging (MRI) volumes and cortical surfaces. Unlike alternative methods for detecting the major cortical sulci, which use a small number of predefined rules based on properties of the cortical surface such as the mean curvature, our approach learns a discriminative model using the probabilistic boosting tree algorithm (PBT) . PBT is a supervised learning approach which selects and combines hundreds of features at different scales, such as curvatures, gradients and shape index. Our method can be applied to either MRI volumes or cortical surfaces. It first outputs a probability map which indicates how likely each voxel lies on a major sulcal curve. Next, it applies dynamic programming to extract the best curve based on the probability map and a shape prior. The algorithm has almost no parameters to tune for extracting different major sulci. It is very fast (it runs in under 1 min per sulcus including the time to compute the discriminative models) due to efficient implementation of the features (e.g., using the integral volume to rapidly compute the responses of 3-D Haar filters). Because the algorithm can be applied to MRI volumes directly, there is no need to perform preprocessing such as tissue segmentation or mapping to a canonical space. The learning aspect of our approach makes the system very flexible and general. For illustration, we use volumes of the right hemisphere with several major cortical sulci manually labeled. The algorithm is tested on two groups of data, including some brains from patients with Williams Syndrome, and the results are very encouraging View full abstract»

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  • Classification Based on Cortical Folding Patterns

    Publication Year: 2007 , Page(s): 553 - 565
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1383 KB) |  | HTML iconHTML  

    We describe here a classification system based on automatically identified cortical sulci. Multivariate recognition methods are required for the detection of complex brain patterns with a spatial distribution. However, such methods may face the well-known issue of the curse of dimensionality-the risk of overfitting the training dataset in high-dimensional space. We overcame this problem, using a classifier pipeline with one- or two-stage of descriptor selection based on machine-learning methods, followed by a support vector machine classifier or linear discriminant analysis. We compared alternative designs of the pipeline on two different datasets built from the same database corresponding to 151 brains. The first dataset dealt with cortex asymmetry and the second dealt with the effect of the subject's sex. Our system successfully (98%) distinguished between the left and right hemispheres on the basis of sulcal shape (size, depth, etc.). The sex of the subject could be determined with a success rate of 85%. These results highlight the attractiveness of multivariate recognition models combined with appropriate descriptor selection. The sulci selected by the pipeline are consistent with previous whole-brain studies on sex effects and hemispheric asymmetries View full abstract»

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  • Weighted Fourier Series Representation and Its Application to Quantifying the Amount of Gray Matter

    Publication Year: 2007 , Page(s): 566 - 581
    Cited by:  Papers (26)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2524 KB) |  | HTML iconHTML  

    We present a novel weighted Fourier series (WFS) representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a self-adjoint partial differential equation and the traditional spherical harmonic (SPHARM) representation. To reduce steep computational requirements, a new iterative residual fitting (IRF) algorithm is developed. Its computational and numerical implementation issues are discussed in detail. The computer codes are also available at http://www.stat.wisc.edu/ ~mchung/softwares/weighted-SPHARM/weighted-SPHARM.html . As an illustration, the WFS is applied in quantifying the amount of gray matter in a group of high functioning autistic subjects. Within the WFS framework, cortical thickness and gray matter density are computed and compared View full abstract»

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  • Cortical Surface Shape Analysis Based on Spherical Wavelets

    Publication Year: 2007 , Page(s): 582 - 597
    Cited by:  Papers (9)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1211 KB) |  | HTML iconHTML  

    In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transformation can characterize the underlying functions in a local fashion in both space and frequency, in contrast to spherical harmonics that have a global basis set. We perform principal component analysis (PCA) on these wavelet shape features to study patterns of shape variation within normal population from coarse to fine resolution. In addition, we study the development of cortical folding in newborns using the Gompertz model in the wavelet domain, which allows us to characterize the order of development of large-scale and finer folding patterns independently. Given a limited amount of training data, we use a regularization framework to estimate the parameters of the Gompertz model to improve the prediction performance on new data. We develop an efficient method to estimate this regularized Gompertz model based on the Broyden-Fletcher-Goldfarb-Shannon (BFGS) approximation. Promising results are presented using both PCA and the folding development model in the wavelet domain. The cortical folding development model provides quantitative anatomic information regarding macroscopic cortical folding development and may be of potential use as a biomarker for early diagnosis of neurologic deficits in newborns View full abstract»

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  • Multiscale 3-D Shape Representation and Segmentation Using Spherical Wavelets

    Publication Year: 2007 , Page(s): 598 - 618
    Cited by:  Papers (14)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2051 KB) |  | HTML iconHTML  

    This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed shape representation can be optimized to compactly encode shape variations in a population at the needed scale and spatial locations, enabling the construction of more descriptive, nonglobal, nonuniform shape probability priors to be included in the segmentation and shape analysis framework. In particular, this representation addresses the shortcomings of techniques that learn a global shape prior at a single scale of analysis and cannot represent fine, local variations in a population of shapes in the presence of a limited dataset. Specifically, our technique defines a multiscale parametric model of surfaces belonging to the same population using a compact set of spherical wavelets targeted to that population. We further refine the shape representation by separating into groups wavelet coefficients that describe independent global and/or local biological variations in the population, using spectral graph partitioning. We then learn a prior probability distribution induced over each group to explicitly encode these variations at different scales and spatial locations. Based on this representation, we derive a parametric active surface evolution using the multiscale prior coefficients as parameters for our optimization procedure to naturally include the prior for segmentation. Additionally, the optimization method can be applied in a coarse-to-fine manner. We apply our algorithm to two different brain structures, the caudate nucleus and the hippocampus, of interest in the study of schizophrenia. We show: 1) a reconstruction task of a test set to validate the expressiveness of - - our multiscale prior and 2) a segmentation task. In the reconstruction task, our results show that for a given training set size, our algorithm significantly improves the approximation of shapes in a testing set over the Point Distribution Model, which tends to oversmooth data. In the segmentation task, our validation shows our algorithm is computationally efficient and outperforms the Active Shape Model algorithm, by capturing finer shape details View full abstract»

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  • Anatomical Equivalence Class: A Morphological Analysis Framework Using a Lossless Shape Descriptor

    Publication Year: 2007 , Page(s): 619 - 631
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2975 KB) |  | HTML iconHTML  

    Methods of computational anatomy are typically based on a spatial transformation that maps a template to an individual anatomy and vice versa. However, important morphological characteristics are frequently not captured by this transformation, thereby leading to lossy representations. We extend this formulation by incorporating residual anatomical information, i.e., information that is not captured by the shape transformation but is necessary in order to fully and exactly reconstruct the anatomy under measurement. We, therefore, arrive at a lossless morphological representation. By virtue of being lossless, this representation allows us to represent the same anatomy by an infinite number of pairs [transformation, residual], since different residuals correspond to different transformations. We treat these pairs as members of an anatomical equivalence class (AEC), which we approximate using principal component analysis. We show that projection onto the orthogonal to the AEC subspace produces measurements that allow us to better detect morphological abnormalities by eliminating variation in the data that is irrelevant and confounds underlying subtle morphological characteristics. Finally, we show that higher classification rates between a group of normal brains and a group of brains with localized atrophy are obtained if we use nonmetric distances between AECs instead of conventional Euclidean distances between individual morphological measurements. The results confirm that this representation can improve the results compared to conventional analysis, but also highlight limitations of the current approach and point to directions of further development of this general morphological analysis framework View full abstract»

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  • 4th IEEE-EMBS International Summer School and Symposium on Medical Devices and Biosensors (ISSS-MDBS)

    Publication Year: 2007 , Page(s): 632
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  • Special issue on functional imaging of the heart

    Publication Year: 2007 , Page(s): 633
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  • IEEE Transactions on Medical Imaging applicants sought for Editor-In-Chief

    Publication Year: 2007 , Page(s): 634
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  • Order form for reprints

    Publication Year: 2007 , Page(s): 635
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  • Does your research need a jump-start? The IEEE Engineering in Medicine and Biology Society Electronic Resource

    Publication Year: 2007 , Page(s): 636
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  • IEEE Transactions on Medical Imaging Information for authors

    Publication Year: 2007 , Page(s): C3
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Aims & Scope

IEEE Transactions on Medical Imaging (T-MI) encourages the submission of manuscripts on imaging of body structures, morphology and function, and imaging of microscopic biological entities. The journal publishes original contributions on medical imaging achieved by various modalities, such as ultrasound, X-rays (including CT) magnetic resonance, radionuclides, microwaves, and light, as well as medical image processing and analysis, visualization, pattern recognition, and related methods. Studies involving highly technical perspectives are most welcome. The journal focuses on a unified common ground where instrumentation, systems, components, hardware and software, mathematics and physics contribute to the studies.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Michael Insana
Beckman Institute for Advanced Science and Technology
Department of Bioengineering
University of Illinois at Urbana-Champaign
Urbana, IL 61801 USA
m.f.i@ieee.org