The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Bruce Fischl - One of the best experts on this subject based on the ideXlab platform.
-
Cortical Surface registration using unsupervised learning
NeuroImage, 2020Co-Authors: Jieyu Cheng, Bruce Fischl, Adrian V Dalca, Lilla Zollei, Alzheimers Disease Neuroimaging InitiativeAbstract:Non-rigid Cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of Surface properties and perform registration by aligning Cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to Surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for Cortical Surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including Cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
-
advantages of Cortical Surface reconstruction using submillimeter 7 t memprage
NeuroImage, 2018Co-Authors: N Zaretskaya, Bruce Fischl, Ville Renvall, Martin Reuter, Jonathan R. PolimeniAbstract:Abstract Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the Cortical Surface reconstruction and Cortical thickness estimation. In this study we adapted the FreeSurfer Cortical Surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in Surface placement between meshes generated from (0.75 mm)3 isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm3 voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray–white Surface is found closer to the white matter, especially in Cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray–CSF Surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring Cortical thickness.
-
Cortical Surface based analysis reduces bias and variance in kinetic modeling of brain pet data
NeuroImage, 2014Co-Authors: Douglas N. Greve, Bruce Fischl, Claus Svarer, Patrick M Fisher, Ling Feng, Adam E Hansen, William Frans Christian Baare, Bruce R Rosen, Gitte M KnudsenAbstract:Abstract Exploratory (i.e., voxelwise) spatial methods are commonly used in neuroimaging to identify areas that show an effect when a region-of-interest (ROI) analysis cannot be performed because no strong a priori anatomical hypothesis exists. However, noise at a single voxel is much higher than noise in a ROI making noise management critical to successful exploratory analysis. This work explores how preprocessing choices affect the bias and variability of voxelwise kinetic modeling analysis of brain positron emission tomography (PET) data. These choices include the use of volume- or Cortical Surface-based smoothing, level of smoothing, use of voxelwise partial volume correction (PVC), and PVC masking threshold. PVC was implemented using the Muller-Gartner method with the masking out of voxels with low gray matter (GM) partial volume fraction. Dynamic PET scans of an antagonist serotonin-4 receptor radioligand ([ 11 C]SB207145) were collected on sixteen healthy subjects using a Siemens HRRT PET scanner. Kinetic modeling was used to compute maps of non-displaceable binding potential (BP ND ) after preprocessing. The results showed a complicated interaction between smoothing, PVC, and masking on BP ND estimates. Volume-based smoothing resulted in large bias and intersubject variance because it smears signal across tissue types. In some cases, PVC with volume smoothing paradoxically caused the estimated BP ND to be less than when no PVC was used at all. When applied in the absence of PVC, Cortical Surface-based smoothing resulted in dramatically less bias and the least variance of the methods tested for smoothing levels 5 mm and higher. When used in combination with PVC, Surface-based smoothing minimized the bias without significantly increasing the variance. Surface-based smoothing resulted in 2–4 times less intersubject variance than when volume smoothing was used. This translates into more than 4 times fewer subjects needed in a group analysis to achieve similarly powered statistical tests. Surface-based smoothing has less bias and variance because it respects Cortical geometry by smoothing the PET data only along the Cortical ribbon and so does not contaminate the GM signal with that of white matter and cerebrospinal fluid. The use of Surface-based analysis in PET should result in substantial improvements in the reliability and detectability of effects in exploratory PET analysis, with or without PVC.
-
quantitative comparison of Cortical Surface reconstructions from mp2rage and multi echo mprage data at 3 and 7 t
NeuroImage, 2014Co-Authors: Kyoko Fujimoto, Bruce Fischl, Jonathan R. Polimeni, Martin Reuter, Andre Van Der Kouwe, Tobias Kober, Thomas Benner, Lawrence L WaldAbstract:The Magnetization-Prepared 2 Rapid Acquisition Gradient Echo (MP2RAGE) method achieves spatially uniform contrast across the entire brain between gray matter and surrounding white matter tissue and cerebrospinal fluid by rapidly acquiring data at two points during an inversion recovery, and then combining the two volumes so as to cancel out sources of intensity and contrast bias, making it useful for neuroimaging studies at ultrahigh field strengths (≥7T). To quantify the effectiveness of the MP2RAGE method for quantitative morphometric neuroimaging, we performed tissue segmentation and cerebral Cortical Surface reconstruction of the MP2RAGE data and compared the results with those generated from conventional multi-echo MPRAGE (MEMPRAGE) data across a group of healthy subjects. To do so, we developed a preprocessing scheme for the MP2RAGE image data to allow for automatic Cortical segmentation and Surface reconstruction using FreeSurfer and analysis methods to compare the positioning of the Surface meshes. Using image volumes with 1mm isotropic voxels we found a scan-rescan reproducibility of Cortical thickness estimates to be 0.15 mm (or 6%) for the MEMPRAGE data and a slightly lower reproducibility of 0.19 mm (or 8%) for the MP2RAGE data. We also found that the thickness estimates were systematically smaller in the MP2RAGE data, and that both the interior and exterior Cortical boundaries estimated from the MP2RAGE data were consistently positioned within the corresponding boundaries estimated from the MEMPRAGE data. Therefore several measureable differences exist in the appearance of Cortical gray matter and its effect on automatic segmentation methods that must be considered when choosing an acquisition or segmentation method for studies requiring Cortical Surface reconstructions. We propose potential extensions to the MP2RAGE method that may help to reduce or eliminate these discrepancies.
-
Measuring and comparing brain Cortical Surface area and other areal quantities.
NeuroImage, 2012Co-Authors: Anderson M. Winkler, Bruce Fischl, Mert R. Sabuncu, B.t. Thomas Yeo, Douglas N. Greve, Peter Kochunov, Thomas E. Nichols, John Blangero, David C. GlahnAbstract:Structural analysis of MRI data on the Cortical Surface usually focuses on Cortical thickness. Cortical Surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the Surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the Cortical Surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the cortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of Surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications.
Anders M Dale - One of the best experts on this subject based on the ideXlab platform.
-
brain structure mediates the association between height and cognitive ability
Brain Structure & Function, 2018Co-Authors: Eero Vuoksimaa, Matthew S Panizzon, Christine Fennemanotestine, Donald J Hagler, Michael J Lyons, Carol E Franz, Anders M Dale, William S KremenAbstract:Height and general cognitive ability are positively associated, but the underlying mechanisms of this relationship are not well understood. Both height and general cognitive ability are positively associated with brain size. Still, the neural substrate of the height-cognitive ability association is unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to investigate whether the association between height and cognitive ability is mediated by Cortical size. In addition to Cortical volume, we used genetically, ontogenetically and phylogenetically distinct Cortical metrics of total Cortical Surface area and mean Cortical thickness. Height was positively associated with general cognitive ability and total Cortical volume and Cortical Surface area, but not with mean Cortical thickness. Mediation models indicated that the well-replicated height-general cognitive ability association is accounted for by individual differences in total Cortical volume and Cortical Surface area (highly heritable metrics related to global brain size), and that the genetic association between Cortical Surface area and general cognitive ability underlies the phenotypic height-general cognitive ability relationship.
-
Cortical morphology of the pars opercularis and its relationship to motor inhibitory performance in a longitudinal developing cohort
Brain Structure & Function, 2018Co-Authors: Lauren B Curley, Erik Newman, Timothy T Brown, Natacha Akshoomoff, Chase Reuter, Wesley K. Thompson, Donald J Hagler, Anders M Dale, Terry L. JerniganAbstract:This study investigates the relationship between variability in Cortical Surface area and thickness of the pars opercularis of the inferior frontal gyrus and motor-inhibitory performance on a stop-signal task in a longitudinal, typically developing cohort of children and adolescents. Linear mixed-effects models were used to investigate the hypotheses that (1) Cortical thinning and (2) a relatively larger Cortical Surface area of the bilateral pars opercularis of the inferior frontal gyrus would predict better performance on the stop-signal task in a cohort of 110 children and adolescents 4-13 years of age, with one to four observations (totaling 232 observations). Cortical thickness of the bilateral opercular region was not related to inhibitory performance. However, independent of age, gender, and total Cortical Surface area, relatively larger Cortical Surface area of the bilateral opercular region of the inferior frontal gyrus was associated with better motor-inhibitory performance. Follow-up analyses showed a significant effect of Surface area of the right pars opercularis, but no evidence for an effect of area of left pars opercularis, on motor-inhibitory performance. These findings are consistent with the previous work in adults showing that Cortical morphology of the pars opercularis is related to inhibitory functioning. It also expands upon this literature by showing that, in contrast to earlier work highlighting the importance of Cortical thickness of this region in adults, relative Cortical Surface area of the pars opercularis may be related to developing motor-inhibitory functions during childhood and adolescence. Relationships between Cortical phenotypes and individual differences in behavioral measures may vary across the lifespan.
-
Cortical Surface based analysis i segmentation and Surface reconstruction
NeuroImage, 1999Co-Authors: Anders M Dale, Bruce Fischl, Martin I. SerenoAbstract:Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of Cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the Cortical Surface. In order to study such Cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the Cortical Surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the Cortical Surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the Cortical Surface are described in a companion paper. These procedures allow for the routine use of Cortical Surface-based analysis and visualization methods in functional brain imaging. r 1999 Academic Press
-
Cortical Surface based analysis ii inflation flattening and a Surface based coordinate system
NeuroImage, 1999Co-Authors: Bruce Fischl, Martin I. Sereno, Anders M DaleAbstract:The Surface of the human cerebral cortex is a highly folded sheet with the majority of its Surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the Cortical Surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable Surface such as a sphere for the purpose of establishing a Surface-based coordinate system. r 1999 Academic Press
-
High-resolution intersubject averaging and a coordinate system for the Cortical Surface.
Human brain mapping, 1999Co-Authors: Bruce Fischl, Martin I. Sereno, Roger B. H. Tootell, Anders M DaleAbstract:The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the Cortical Surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the Cortical Surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing Cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D Cortical sheet. In order to address the need for a more natural Surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical Surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
Martin I. Sereno - One of the best experts on this subject based on the ideXlab platform.
-
smoothing and cluster thresholding for Cortical Surface based group analysis of fmri data
NeuroImage, 2006Co-Authors: Donald J Hagler, Ayse Pinar Saygin, Martin I. SerenoAbstract:Cortical Surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with Surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a Surface-based version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the Cortical Surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a Cortical Surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing Surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.
-
Cortical Surface based analysis i segmentation and Surface reconstruction
NeuroImage, 1999Co-Authors: Anders M Dale, Bruce Fischl, Martin I. SerenoAbstract:Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of Cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the Cortical Surface. In order to study such Cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the Cortical Surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the Cortical Surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the Cortical Surface are described in a companion paper. These procedures allow for the routine use of Cortical Surface-based analysis and visualization methods in functional brain imaging. r 1999 Academic Press
-
Cortical Surface based analysis ii inflation flattening and a Surface based coordinate system
NeuroImage, 1999Co-Authors: Bruce Fischl, Martin I. Sereno, Anders M DaleAbstract:The Surface of the human cerebral cortex is a highly folded sheet with the majority of its Surface area buried within folds. As such, it is a difficult domain for computational as well as visualization purposes. We have therefore designed a set of procedures for modifying the representation of the Cortical Surface to (i) inflate it so that activity buried inside sulci may be visualized, (ii) cut and flatten an entire hemisphere, and (iii) transform a hemisphere into a simple parameterizable Surface such as a sphere for the purpose of establishing a Surface-based coordinate system. r 1999 Academic Press
-
High-resolution intersubject averaging and a coordinate system for the Cortical Surface.
Human brain mapping, 1999Co-Authors: Bruce Fischl, Martin I. Sereno, Roger B. H. Tootell, Anders M DaleAbstract:The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the Cortical Surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the Cortical Surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing Cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D Cortical sheet. In order to address the need for a more natural Surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical Surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain.
-
high resolution intersubject averaging and a coordinate system for the Cortical Surface
Human Brain Mapping, 1999Co-Authors: Bruce Fischl, Martin I. Sereno, Roger B. H. Tootell, Anders M DaleAbstract:The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the Cortical Surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the Cortical Surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing Cortical features are based on 3-D stereotaxic coordinates rather than on position relative to the 2-D Cortical sheet. In order to address the need for a more natural Surface-based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical Surface-based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain. Hum. Brain Mapping 8:272-284, 1999. r 1999 Wiley-Liss, Inc.
Antonios Makropoulos - One of the best experts on this subject based on the ideXlab platform.
-
geometric deep learning for post menstrual age prediction based on the neonatal white matter Cortical Surface
UNSURE GRAIL@MICCAI, 2020Co-Authors: Vitalis Vosylius, Antonios Makropoulos, Andreas Schuh, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Daniel RueckertAbstract:Accurate estimation of the age in neonates is useful for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter Cortical Surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the Cortical Surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727 scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.
-
geometric deep learning for post menstrual age prediction based on the neonatal white matter Cortical Surface
arXiv: Computer Vision and Pattern Recognition, 2020Co-Authors: Vitalis Vosylius, Antonios Makropoulos, Andreas Schuh, Andy Wang, Cemlyn Waters, Alexey Zakharov, Francis Ward, Loic Le Folgoc, John Cupitt, Daniel RueckertAbstract:Accurate estimation of the age in neonates is essential for measuring neurodevelopmental, medical, and growth outcomes. In this paper, we propose a novel approach to predict the post-menstrual age (PA) at scan, using techniques from geometric deep learning, based on the neonatal white matter Cortical Surface. We utilize and compare multiple specialized neural network architectures that predict the age using different geometric representations of the Cortical Surface; we compare MeshCNN, Pointnet++, GraphCNN, and a volumetric benchmark. The dataset is part of the Developing Human Connectome Project (dHCP), and is a cohort of healthy and premature neonates. We evaluate our approach on 650 subjects (727scans) with PA ranging from 27 to 45 weeks. Our results show accurate prediction of the estimated PA, with mean error less than one week.
-
construction of a neonatal Cortical Surface atlas using multimodal Surface matching in the developing human connectome project
NeuroImage, 2018Co-Authors: Jelena Bozek, Antonios Makropoulos, Andreas Schuh, Robert Wright, Sean P. Fitzgibbon, Matthew F Glasser, Timothy S Coalson, Jonathan Omuircheartaigh, Jana Hutter, Anthony N PriceAbstract:We propose a method for constructing a spatio-temporal Cortical Surface atlas of neonatal brains aged between 36 and 44 weeks of post-menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed Surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using Cortical folding for driving the alignment. Templates have been generated for the anatomical Cortical Surface and for the Cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and Cortical regions. To achieve this, Cortical Surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de-drifting the template. We used temporal adaptive kernel regression to produce age-dependant atlases for 9 weeks (36-44 weeks PMA). The generated templates capture expected patterns of Cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age.
-
the developing human connectome project a minimal processing pipeline for neonatal Cortical Surface reconstruction
NeuroImage, 2018Co-Authors: Antonios Makropoulos, Emma C. Robinson, Andreas Schuh, Robert Wright, Sean P. Fitzgibbon, Jelena Bozek, Serena J. Counsell, Johannes K. Steinweg, Katy VecchiatoAbstract:The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises Cortical Surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for Cortical and sub-Cortical volume segmentation, Cortical Surface extraction, and Cortical Surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating Cortical Surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these Surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
-
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction
NeuroImage, 2018Co-Authors: Antonios Makropoulos, Emma C. Robinson, Andreas Schuh, Robert Wright, Sean P. Fitzgibbon, Jelena Bozek, Serena J. Counsell, Johannes K. Steinweg, Jonathan Passerat-palmbach, Gregor LenzAbstract:The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project and pioneered by FreeSurfer, the project utilises Cortical Surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for Cortical and sub-Cortical volume segmentation, Cortical Surface extraction and Cortical Surface inflation of neonatal subjects, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be fully automatically processed; generating Cortical Surface models that are topologically and anatomically correct. Downstream, these Surfaces will enhance comparisons of functional and diffusion MRI datasets, and support the modelling of emerging patterns of brain connectivity.
Jonathan R. Polimeni - One of the best experts on this subject based on the ideXlab platform.
-
improved Cortical Surface reconstruction using sub millimeter resolution mprage by image denoising
NeuroImage, 2021Co-Authors: Qiyuan Tian, N Zaretskaya, Qiuyun Fan, Chanon Ngamsombat, Berkin Bilgic, Jonathan R. Polimeni, Susie Y. HuangAbstract:Abstract Automatic cerebral Cortical Surface reconstruction is a useful tool for Cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of Cortical Surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the Cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of Cortical Surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the Cortical Surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter Surface placement, gray matter–cerebrospinal fluid Surface placement and Cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the Cortical Surface placement accuracy. The scan-rescan variability of the Cortical Surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved Cortical Surface reconstruction at sub-millimeter resolution.
-
improved Cortical Surface reconstruction using sub millimeter resolution mprage by image denoising
NeuroImage, 2021Co-Authors: Qiyuan Tian, N Zaretskaya, Qiuyun Fan, Chanon Ngamsombat, Berkin Bilgic, Jonathan R. Polimeni, Susie Y. HuangAbstract:Abstract Automatic cerebral Cortical Surface reconstruction is a useful tool for Cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-millimeter isotropic resolution for improved accuracy of Cortical Surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the Cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of Cortical Surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the Cortical Surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ∼0.016, high whole-brain averaged peak signal-to-noise ratio of ∼33.5 dB and structural similarity index of 0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray–white Surface placement, gray–CSF Surface placement and Cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies are approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance is equivalent to averaging ∼2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the Cortical Surface placement accuracy. The scan-rescan precision of the Cortical Surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved Cortical Surface reconstruction sub-millimeter resolution.
-
Improved Cortical Surface reconstruction using sub-millimeter resolution MPRAGE by image denoising
2020Co-Authors: Qiyuan Tian, N Zaretskaya, Qiuyun Fan, Chanon Ngamsombat, Berkin Bilgic, Jonathan R. Polimeni, Susie Y. HuangAbstract:AbstractAutomatic cerebral Cortical Surface reconstruction is a useful tool for Cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-millimeter isotropic resolution for improved accuracy of Cortical Surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the Cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of Cortical Surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the Cortical Surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ∼0.016, high whole-brain averaged peak signal-to-noise ratio of ∼33.5 dB and structural similarity index of 0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray–white Surface placement, gray–CSF Surface placement and Cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. The denoising performance is equivalent to averaging ∼2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the Cortical Surface placement accuracy. The scan-rescan precision of the Cortical Surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved Cortical Surface reconstruction sub-millimeter resolution.
-
advantages of Cortical Surface reconstruction using submillimeter 7 t memprage
NeuroImage, 2018Co-Authors: N Zaretskaya, Bruce Fischl, Ville Renvall, Martin Reuter, Jonathan R. PolimeniAbstract:Abstract Recent advances in MR technology have enabled increased spatial resolution for routine functional and anatomical imaging, which has created demand for software tools that are able to process these data. The availability of high-resolution data also raises the question of whether higher resolution leads to substantial gains in accuracy of quantitative morphometric neuroimaging procedures, in particular the Cortical Surface reconstruction and Cortical thickness estimation. In this study we adapted the FreeSurfer Cortical Surface reconstruction pipeline to process structural data at native submillimeter resolution. We then quantified the differences in Surface placement between meshes generated from (0.75 mm)3 isotropic resolution data acquired in 39 volunteers and the same data downsampled to the conventional 1 mm3 voxel size. We find that when processed at native resolution, cortex is estimated to be thinner in most areas, but thicker around the Cingulate and the Calcarine sulci as well as in the posterior bank of the Central sulcus. Thickness differences are driven by two kinds of effects. First, the gray–white Surface is found closer to the white matter, especially in Cortical areas with high myelin content, and thus low contrast, such as the Calcarine and the Central sulci, causing local increases in thickness estimates. Second, the gray–CSF Surface is placed more interiorly, especially in the deep sulci, contributing to local decreases in thickness estimates. We suggest that both effects are due to reduced partial volume effects at higher spatial resolution. Submillimeter voxel sizes can therefore provide improved accuracy for measuring Cortical thickness.
-
automatic Cortical Surface reconstruction of high resolution t1 echo planar imaging data
NeuroImage, 2016Co-Authors: Jonathan R. Polimeni, Thomas Witzel, Ville Renvall, Lawrence L WaldAbstract:Echo planar imaging (EPI) is the method of choice for the majority of functional magnetic resonance imaging (fMRI), yet EPI is prone to geometric distortions and thus misaligns with conventional anatomical reference data. The poor geometric correspondence between functional and anatomical data can lead to severe misplacements and corruption of detected activation patterns. However, recent advances in imaging technology have provided EPI data with increasing quality and resolution. Here we present a framework for deriving Cortical Surface reconstructions directly from high-resolution EPI-based reference images that provide anatomical models exactly geometric distortion-matched to the functional data. Anatomical EPI data with 1mm isotropic voxel size were acquired using a fast multiple inversion recovery time EPI sequence (MI-EPI) at 7T, from which quantitative T1 maps were calculated. Using these T1 maps, volumetric data mimicking the tissue contrast of standard anatomical data were synthesized using the Bloch equations, and these T1-weighted data were automatically processed using FreeSurfer. The spatial alignment between T2(⁎)-weighted EPI data and the synthetic T1-weighted anatomical MI-EPI-based images was improved compared to the conventional anatomical reference. In particular, the alignment near the regions vulnerable to distortion due to magnetic susceptibility differences was improved, and sampling of the adjacent tissue classes outside of the cortex was reduced when using Cortical Surface reconstructions derived directly from the MI-EPI reference. The MI-EPI method therefore produces high-quality anatomical data that can be automatically segmented with standard software, providing Cortical Surface reconstructions that are geometrically matched to the BOLD fMRI data.
