Lilla Zöllei - Academia.edu (original) (raw)

Papers by Lilla Zöllei

Research paper thumbnail of Combined Volumetric and Surface Registration

IEEE Transactions on Medical Imaging, 2009

In this paper, we propose a novel method for the registration of volumetric images of the brain t... more In this paper, we propose a novel method for the registration of volumetric images of the brain that optimizes the alignment of both cortical and subcortical structures. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and diffused into the volume using the Navier operator of elasticity, resulting in a volumetric warp that aligns cortical

Research paper thumbnail of Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images

NeuroImage, 2015

Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect a... more Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6years of life.

Research paper thumbnail of On the optimality of mutual information as an image registration objective function

2009 16th IEEE International Conference on Image Processing (ICIP), 2009

We model images and the anatomy that they are derived from as stationary and jointly ergodic rand... more We model images and the anatomy that they are derived from as stationary and jointly ergodic random processes. Using an empirically-observed property of anatomy, and data processing inequality arguments, we arrive at optimality criteria for mutual information in the ensemble domain. Using ergodicity, we transfer the criteria to single pairs of images in the spatial domain, where it applies to the popular mutual information-based registration approach that is used in practice.

Research paper thumbnail of Multi-modal Image Registration Using Dirichlet-Encoded Prior Information

Lecture Notes in Computer Science, 2006

We present a new objective function for the registration of multi-modal medical images. Our novel... more We present a new objective function for the registration of multi-modal medical images. Our novel similarity metric incorporates both knowledge about the current observations and information gained from previous registration results and combines the relative influence of these two types of information in a principled way. We show that in the absence of prior information, the method reduces approximately to

Research paper thumbnail of Efficient Population Registration of 3D Data

Lecture Notes in Computer Science, 2005

We present a population registration framework that acts on large collections or populations of d... more We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradientbased stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.

Research paper thumbnail of Anatomical priors for global probabilistic diffusion tractography

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

Abstract We investigate the use of anatomical priors in a Bayesian framework for diffusion tracto... more Abstract We investigate the use of anatomical priors in a Bayesian framework for diffusion tractography. We compare priors that utilize different types of information on the white-matter pathways to be reconstructed. This information includes manually labeled paths from a set ...

Research paper thumbnail of A variational framework for joint segmentation and registration

Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 2001

... Anthony Yezzi*, Lilla Zolleif, and Tina Kapurt *School of Electrical and Computer Engineering... more ... Anthony Yezzi*, Lilla Zolleif, and Tina Kapurt *School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA +Artificial Intelligence ... In the 2D exper-iments, corresponding slices between the MR and the CT were chosen manually, and used as input ...

Research paper thumbnail of Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution

Proceedings / sponsored by IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence. Workshop on Mathematical Methods in Biomedical Image Analysis, Jan 15, 2008

In this work, we describe a white matter trajectory clustering algorithm that allows for incorpor... more In this work, we describe a white matter trajectory clustering algorithm that allows for incorporating and appropriately weighting anatomical information. The influence of the anatomical prior reflects confidence in its accuracy and relevance. It can either be defined by the user or it can be inferred automatically. After a detailed description of our novel clustering framework, we demonstrate its properties through a set of preliminary experiments.

Research paper thumbnail of Feature-Based Alignment of Volumetric Multi-modal Images

Lecture Notes in Computer Science, 2013

This paper proposes a method for aligning image volumes acquired from different imaging modalitie... more This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.

Research paper thumbnail of A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range

Frontiers in Human Neuroscience, 2015

We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailo... more We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.

Research paper thumbnail of Exploratory Identification of Cardiac Noise in fMRI Images

Lecture Notes in Computer Science, 2003

A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI ima... more A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI images is presented. Highly autocorrelated, independent components of the input time series are extracted by applying Canonical Correlation Analysis in the time domain. A close correspondence between some of these components and cardiac noise contributions is established. Our analysis is carried out without using any external monitoring of the subject or any modification applied to the standard image acquisition protocol. Using the results as a priori information about the presence of corrupting cardiac noise, several approaches are suggested that could improve the performance of activation detection algorithms on non-rest-case datasets.

Research paper thumbnail of A unified statistical and information theoretic framework for multi-modal image registration

Information processing in medical imaging : proceedings of the ... conference, 2003

We formulate and interpret several registration methods in the context of a unified statistical a... more We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Research paper thumbnail of A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008

We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber t... more We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.

Research paper thumbnail of Geometry Driven Volumetric Registration

Lecture Notes in Computer Science, 2007

In this paper, we propose a novel method for the registration of volumetric images of the brain t... more In this paper, we propose a novel method for the registration of volumetric images of the brain that attempts to maximize the overlap of cortical folds. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and is diffused throughout the volume using the Navier operator of elasticity. The result is a volumetric warp that aligns the folding patterns.

Research paper thumbnail of Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy

Frontiers in neuroinformatics, 2011

We have developed a method for automated probabilistic reconstruction of a set of major white-mat... more We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy trai...

Research paper thumbnail of A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration

Lecture Notes in Computer Science, 2007

We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that em... more We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.

Research paper thumbnail of Transcriptional landscape of the prenatal human brain

Research paper thumbnail of Improved tractography alignment using combined volumetric and surface registration

NeuroImage, 2010

Previously we introduced an automated high-dimensional non-linear registration framework, CVS, th... more Previously we introduced an automated high-dimensional non-linear registration framework, CVS, that combines volumetric and surface-based alignment to achieve robust and accurate correspondence in both cortical and sub-cortical regions (Postelnicu et al., 2009). In this paper we show that using CVS to compute cross-subject alignment from anatomical images, then applying the previously computed alignment to diffusion weighted MRI images, outperforms state-of-the-art techniques for computing cross-subject alignment directly from the DWI data itself. Specifically, we show that CVS outperforms the alignment component of TBSS in terms of degree-of-alignment of manually labeled tract models for the uncinate fasciculus, the inferior longitudinal fasciculus and the corticospinal tract. In addition, we compare linear alignment using FLIRT based on either fractional anisotropy or anatomical volumes across-subjects, and find a comparable effect. Together these results imply a clear advantage to aligning anatomy as opposed to lower resolution DWI data even when the final goal is diffusion analysis.

Research paper thumbnail of Diffusion-weighted Information Used in a Combined Volumetric and Surface-based Brain Registration Framework

Research paper thumbnail of Radial and tangential neuronal migration pathways in the human fetal brain: Anatomically distinct patterns of diffusion MRI coherence

NeuroImage, 2013

Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by... more Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by highly orchestrated cellular migration. A greater appreciation of the processes involved in human fetal corticogenesis is vital to gaining an understanding of how developmental disturbances originating in gestation could establish a variety of complex neuropathology manifesting in childhood, or even in adult life. Magnetic resonance imaging modalities offer a unique insight into anatomical structure, and increasingly infer information regarding underlying microstructure in the human brain. In this study we applied a combination of high-resolution structural and diffusion-weighted magnetic resonance imaging to a unique cohort of three post-mortem fetal brain specimens, aged between 19 and 22 post-conceptual weeks. Specifically, we sought to assess patterns of diffusion coherence associated with subcortical neuroproliferative structures: the pallial ventricular/subventricular zone and subpallial ganglionic eminence. Two distinct three-dimensional patterns of diffusion coherence were evident: a clear radial pattern originating in ventricular/subventricular zone, and a tangentio-radial patterns originating in ganglionic eminence. These patterns appeared to regress in a caudo-rostral and lateral-ventral to medial-dorsal direction across the short period of fetal development under study. Our findings demonstrate for the first time distinct patterns of diffusion coherence associated with known anatomical proliferative structures. The radial pattern associated with dorsopallial ventricular/subventricular zone and the tangentio-radial pattern associated with subpallial ganglionic eminence are consistent with reports of radial-glial mediated neuronal migration pathways identified during human corticogenesis, supported by our prior studies of comparative fetal diffusion MRI and histology. The ability to assess such pathways in the fetal brain using MR imaging offers a unique insight into three-dimensional trajectories beyond those visualized using traditional histological techniques. Our results suggest that ex-vivo fetal MRI is a potentially useful modality in understanding normal human development and various disease processes whose etiology may originate in aberrant fetal neuronal migration.

Research paper thumbnail of Combined Volumetric and Surface Registration

IEEE Transactions on Medical Imaging, 2009

In this paper, we propose a novel method for the registration of volumetric images of the brain t... more In this paper, we propose a novel method for the registration of volumetric images of the brain that optimizes the alignment of both cortical and subcortical structures. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and diffused into the volume using the Navier operator of elasticity, resulting in a volumetric warp that aligns cortical

Research paper thumbnail of Brain extraction in pediatric ADC maps, toward characterizing neuro-development in multi-platform and multi-institution clinical images

NeuroImage, 2015

Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect a... more Apparent Diffusion Coefficient (ADC) maps can be used to characterize myelination and to detect abnormalities in the developing brain. However, given the normal variation in regional ADC with myelination, detection of abnormalities is difficult when based on visual assessment. Quantitative and automated analysis of pediatric ADC maps is thus desired but requires accurate brain extraction as the first step. Currently, most existing brain extraction methods are optimized for structural T1-weighted MR images of fully myelinated brains. Due to differences in age and image contrast, these approaches do not translate well to pediatric ADC maps. To address this problem, we present a multi-atlas brain extraction framework that has 1) specificity: designed and optimized specifically for pediatric ADC maps; 2) generality: applicable to multi-platform and multi-institution data, and to subjects at various neuro-developmental stages across the first 6years of life; 3) accuracy: highly accurate compared to expert annotations; and 4) consistency: consistently accurate regardless of sources of data and ages of subjects. We show how we achieve these goals, via optimizing major components in a multi-atlas brain extraction framework, and via developing and evaluating new criteria for its atlas ranking component. Moreover, we demonstrate that these goals can be achieved with a fixed set of atlases and a fixed set of parameters, which opens doors for our optimized framework to be used in large-scale and multi-institution neuro-developmental and clinical studies. In a pilot study, we use this framework in a dataset containing scanner-generated ADC maps from 308 pediatric patients collected during the course of routine clinical care. Our framework leads to successful quantifications of the changes in whole-brain volumes and mean ADC values across the first 6years of life.

Research paper thumbnail of On the optimality of mutual information as an image registration objective function

2009 16th IEEE International Conference on Image Processing (ICIP), 2009

We model images and the anatomy that they are derived from as stationary and jointly ergodic rand... more We model images and the anatomy that they are derived from as stationary and jointly ergodic random processes. Using an empirically-observed property of anatomy, and data processing inequality arguments, we arrive at optimality criteria for mutual information in the ensemble domain. Using ergodicity, we transfer the criteria to single pairs of images in the spatial domain, where it applies to the popular mutual information-based registration approach that is used in practice.

Research paper thumbnail of Multi-modal Image Registration Using Dirichlet-Encoded Prior Information

Lecture Notes in Computer Science, 2006

We present a new objective function for the registration of multi-modal medical images. Our novel... more We present a new objective function for the registration of multi-modal medical images. Our novel similarity metric incorporates both knowledge about the current observations and information gained from previous registration results and combines the relative influence of these two types of information in a principled way. We show that in the absence of prior information, the method reduces approximately to

Research paper thumbnail of Efficient Population Registration of 3D Data

Lecture Notes in Computer Science, 2005

We present a population registration framework that acts on large collections or populations of d... more We present a population registration framework that acts on large collections or populations of data volumes. The data alignment procedure runs in a simultaneous fashion, with every member of the population approaching the central tendency of the collection at the same time. Such a mechanism eliminates the need for selecting a particular reference frame a priori, resulting in a non-biased estimate of a digital atlas. Our algorithm adopts an affine congealing framework with an information theoretic objective function and is optimized via a gradientbased stochastic approximation process embedded in a multi-resolution setting. We present experimental results on both synthetic and real images.

Research paper thumbnail of Anatomical priors for global probabilistic diffusion tractography

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

Abstract We investigate the use of anatomical priors in a Bayesian framework for diffusion tracto... more Abstract We investigate the use of anatomical priors in a Bayesian framework for diffusion tractography. We compare priors that utilize different types of information on the white-matter pathways to be reconstructed. This information includes manually labeled paths from a set ...

Research paper thumbnail of A variational framework for joint segmentation and registration

Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 2001

... Anthony Yezzi*, Lilla Zolleif, and Tina Kapurt *School of Electrical and Computer Engineering... more ... Anthony Yezzi*, Lilla Zolleif, and Tina Kapurt *School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA +Artificial Intelligence ... In the 2D exper-iments, corresponding slices between the MR and the CT were chosen manually, and used as input ...

Research paper thumbnail of Modeling of Anatomical Information in Clustering of White Matter Fiber Trajectories Using Dirichlet Distribution

Proceedings / sponsored by IEEE Computer Society Technical Committee on Pattern Analysis and Machine Intelligence. Workshop on Mathematical Methods in Biomedical Image Analysis, Jan 15, 2008

In this work, we describe a white matter trajectory clustering algorithm that allows for incorpor... more In this work, we describe a white matter trajectory clustering algorithm that allows for incorporating and appropriately weighting anatomical information. The influence of the anatomical prior reflects confidence in its accuracy and relevance. It can either be defined by the user or it can be inferred automatically. After a detailed description of our novel clustering framework, we demonstrate its properties through a set of preliminary experiments.

Research paper thumbnail of Feature-Based Alignment of Volumetric Multi-modal Images

Lecture Notes in Computer Science, 2013

This paper proposes a method for aligning image volumes acquired from different imaging modalitie... more This paper proposes a method for aligning image volumes acquired from different imaging modalities (e.g. MR, CT) based on 3D scale-invariant image features. A novel method for encoding invariant feature geometry and appearance is developed, based on the assumption of locally linear intensity relationships, providing a solution to poor repeatability of feature detection in different image modalities. The encoding method is incorporated into a probabilistic feature-based model for multi-modal image alignment. The model parameters are estimated via a group-wise alignment algorithm, that iteratively alternates between estimating a feature-based model from feature data, then realigning feature data to the model, converging to a stable alignment solution with few pre-processing or pre-alignment requirements. The resulting model can be used to align multi-modal image data with the benefits of invariant feature correspondence: globally optimal solutions, high efficiency and low memory usage. The method is tested on the difficult RIRE data set of CT, T1, T2, PD and MP-RAGE brain images of subjects exhibiting significant inter-subject variability due to pathology.

Research paper thumbnail of A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range

Frontiers in Human Neuroscience, 2015

We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailo... more We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.

Research paper thumbnail of Exploratory Identification of Cardiac Noise in fMRI Images

Lecture Notes in Computer Science, 2003

A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI ima... more A fast exploratory framework for extracting cardiac noise signals contained in rest-case fMRI images is presented. Highly autocorrelated, independent components of the input time series are extracted by applying Canonical Correlation Analysis in the time domain. A close correspondence between some of these components and cardiac noise contributions is established. Our analysis is carried out without using any external monitoring of the subject or any modification applied to the standard image acquisition protocol. Using the results as a priori information about the presence of corrupting cardiac noise, several approaches are suggested that could improve the performance of activation detection algorithms on non-rest-case datasets.

Research paper thumbnail of A unified statistical and information theoretic framework for multi-modal image registration

Information processing in medical imaging : proceedings of the ... conference, 2003

We formulate and interpret several registration methods in the context of a unified statistical a... more We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.

Research paper thumbnail of A mathematical framework for incorporating anatomical knowledge in DT-MRI analysis

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2008

We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber t... more We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.

Research paper thumbnail of Geometry Driven Volumetric Registration

Lecture Notes in Computer Science, 2007

In this paper, we propose a novel method for the registration of volumetric images of the brain t... more In this paper, we propose a novel method for the registration of volumetric images of the brain that attempts to maximize the overlap of cortical folds. In order to achieve this, relevant geometrical information is extracted from a surface-based morph and is diffused throughout the volume using the Navier operator of elasticity. The result is a volumetric warp that aligns the folding patterns.

Research paper thumbnail of Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy

Frontiers in neuroinformatics, 2011

We have developed a method for automated probabilistic reconstruction of a set of major white-mat... more We have developed a method for automated probabilistic reconstruction of a set of major white-matter pathways from diffusion-weighted MR images. Our method is called TRACULA (TRActs Constrained by UnderLying Anatomy) and utilizes prior information on the anatomy of the pathways from a set of training subjects. By incorporating this prior knowledge in the reconstruction procedure, our method obviates the need for manual interaction with the tract solutions at a later stage and thus facilitates the application of tractography to large studies. In this paper we illustrate the application of the method on data from a schizophrenia study and investigate whether the inclusion of both patients and healthy subjects in the training set affects our ability to reconstruct the pathways reliably. We show that, since our method does not constrain the exact spatial location or shape of the pathways but only their trajectory relative to the surrounding anatomical structures, a set a of healthy trai...

Research paper thumbnail of A Marginalized MAP Approach and EM Optimization for Pair-Wise Registration

Lecture Notes in Computer Science, 2007

We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that em... more We formalize the pair-wise registration problem in a maximum a posteriori (MAP) framework that employs a multinomial model of joint intensities with parameters for which we only have a prior distribution. To obtain an MAP estimate of the aligning transformation alone, we treat the multinomial parameters as nuisance parameters, and marginalize them out. If the prior on those is uninformative, the marginalization leads to registration by minimization of joint entropy. With an informative prior, the marginalization leads to minimization of the entropy of the data pooled with pseudo observations from the prior. In addition, we show that the marginalized objective function can be optimized by the Expectation-Maximization (EM) algorithm, which yields a simple and effective iteration for solving entropy-based registration problems. Experimentally, we demonstrate the effectiveness of the resulting EM iteration for rapidly solving a challenging intra-operative registration problem.

Research paper thumbnail of Transcriptional landscape of the prenatal human brain

Research paper thumbnail of Improved tractography alignment using combined volumetric and surface registration

NeuroImage, 2010

Previously we introduced an automated high-dimensional non-linear registration framework, CVS, th... more Previously we introduced an automated high-dimensional non-linear registration framework, CVS, that combines volumetric and surface-based alignment to achieve robust and accurate correspondence in both cortical and sub-cortical regions (Postelnicu et al., 2009). In this paper we show that using CVS to compute cross-subject alignment from anatomical images, then applying the previously computed alignment to diffusion weighted MRI images, outperforms state-of-the-art techniques for computing cross-subject alignment directly from the DWI data itself. Specifically, we show that CVS outperforms the alignment component of TBSS in terms of degree-of-alignment of manually labeled tract models for the uncinate fasciculus, the inferior longitudinal fasciculus and the corticospinal tract. In addition, we compare linear alignment using FLIRT based on either fractional anisotropy or anatomical volumes across-subjects, and find a comparable effect. Together these results imply a clear advantage to aligning anatomy as opposed to lower resolution DWI data even when the final goal is diffusion analysis.

Research paper thumbnail of Diffusion-weighted Information Used in a Combined Volumetric and Surface-based Brain Registration Framework

Research paper thumbnail of Radial and tangential neuronal migration pathways in the human fetal brain: Anatomically distinct patterns of diffusion MRI coherence

NeuroImage, 2013

Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by... more Corticogenesis is underpinned by a complex process of subcortical neuroproliferation, followed by highly orchestrated cellular migration. A greater appreciation of the processes involved in human fetal corticogenesis is vital to gaining an understanding of how developmental disturbances originating in gestation could establish a variety of complex neuropathology manifesting in childhood, or even in adult life. Magnetic resonance imaging modalities offer a unique insight into anatomical structure, and increasingly infer information regarding underlying microstructure in the human brain. In this study we applied a combination of high-resolution structural and diffusion-weighted magnetic resonance imaging to a unique cohort of three post-mortem fetal brain specimens, aged between 19 and 22 post-conceptual weeks. Specifically, we sought to assess patterns of diffusion coherence associated with subcortical neuroproliferative structures: the pallial ventricular/subventricular zone and subpallial ganglionic eminence. Two distinct three-dimensional patterns of diffusion coherence were evident: a clear radial pattern originating in ventricular/subventricular zone, and a tangentio-radial patterns originating in ganglionic eminence. These patterns appeared to regress in a caudo-rostral and lateral-ventral to medial-dorsal direction across the short period of fetal development under study. Our findings demonstrate for the first time distinct patterns of diffusion coherence associated with known anatomical proliferative structures. The radial pattern associated with dorsopallial ventricular/subventricular zone and the tangentio-radial pattern associated with subpallial ganglionic eminence are consistent with reports of radial-glial mediated neuronal migration pathways identified during human corticogenesis, supported by our prior studies of comparative fetal diffusion MRI and histology. The ability to assess such pathways in the fetal brain using MR imaging offers a unique insight into three-dimensional trajectories beyond those visualized using traditional histological techniques. Our results suggest that ex-vivo fetal MRI is a potentially useful modality in understanding normal human development and various disease processes whose etiology may originate in aberrant fetal neuronal migration.