Reconstruction of the human cerebral cortex robust to white matter lesions: method and validation

Neural deformation fields for template-based reconstruction of cortical surfaces from MRI

The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods separate the surface registration from the surface extraction, which is computationally inefficient and prone to distortions. We introduce Vox2Cortex-Flow (V2C-Flow), a deep mesh-deformation technique that learns a deformation field from a brain template to the cortical surfaces of an MRI scan. To this end, we present a geometric neural network that models the deformation-describing ordinary differential equation in a continuous manner. The network architecture comprises convolutional and graph-convolutional layers, which allows it to work with images and meshes at the same time. V2C-Flow is not only very fast, requiring less than two seconds to infer all four cortical surfaces, but also establishes vertex-wise correspondences to the template during reconstruction. In addition, V2C-Flow is the first approach for cortex reconstruction that models white matter and pial surfaces jointly, therefore avoiding intersections between them. Our comprehensive experiments on internal and external test data demonstrate that V2C-Flow results in cortical surfaces that are state-of-the-art in terms of accuracy. Moreover, we show that the established correspondences are more consistent than in FreeSurfer and that they can directly be utilized for cortex parcellation and group analyses of cortical thickness.

Structural brain differences in essential tremor and Parkinson's disease deep brain stimulation patients

BACKGROUND

Essential tremor (ET) and Parkinson's disease (PD) are the most common tremor disorders and are common indications for deep brain stimulation (DBS). In some patients, PD and ET symptoms overlap and diagnosis can be challenging based on clinical criteria alone. The objective of this study was to identify structural brain differences between PD and ET DBS patients to help differentiate these disorders and improve our understanding of the different brain regions involved in these pathologic processes.

METHODS

We included ET and PD patients scheduled to undergo DBS surgery in this observational study. Patients underwent 3T brain MRI while under general anesthesia as part of their procedure. Cortical thicknesses and subcortical volumes were quantified from T1-weighted images using automated multi-atlas segmentation. We used logistic regression analysis to identify brain regions associated with diagnosis of ET or PD.

RESULTS

149 ET and 265 PD patients were included. Smaller volumes in the pallidum and thalamus and reduced thickness in the anterior orbital gyrus, lateral orbital gyrus, and medial precentral gyrus were associated with greater odds of ET diagnosis. Conversely, reduced volumes in the caudate, amygdala, putamen, and basal forebrain, and reduced thickness in the orbital part of the inferior frontal gyrus, supramarginal gyrus, and posterior cingulate were associated with greater odds of PD diagnosis.

CONCLUSIONS

These findings identify structural brain differences between PD and ET patients. These results expand our understanding of the different brain regions involved in these disorders and suggest that structural MRI may help to differentiate patients with these two disorders.

Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite

Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.

Synthetic Atrophy for Longitudinal Cortical Surface Analyses

In the fields of longitudinal cortical segmentation and surface-based cortical thickness (CT) measurement, difficulty in assessing accuracy remains a substantial limitation due to the inability of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for these purposes, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal ground truth dataset specifically designed to address this gap in surface-based accuracy validation techniques. Across the entire brain, our method can induce up to between 0.8 and 2.5 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that cortical segmentations of synthetically atrophied images exhibit similar segmentation error to those obtained from images of naturally atrophied brains. Importantly, our method relies exclusively on publicly available software and datasets.

Initial evidence of abnormal brain plasticity in anorexia nervosa: an ultra-high field study

Anorexia Nervosa has been associated with white matter abnormalities implicating subcortical abnormal myelination. Extending these findings to intracortical myelin has been challenging but ultra-high field neuroimaging offers new methodological opportunities. To test the integrity of intracortical myelin in AN we used 7 T neuroimaging to acquire T1-weighted images optimized for intracortical myelin from seven females with AN (age range: 18-33) and 11 healthy females (age range: 23-32). Intracortical T₁ values (inverse index of myelin concentration) were extracted from 148 cortical regions at ten depth-levels across the cortical ribbon. Across all cortical regions, these levels were averaged to generate estimates of total intracortical myelin concentration and were clustered using principal component analyses into two clusters; the outer cluster comprised T₁ values across depth-levels ranging from the CSF boundary to the middle of the cortical regions and the inner cluster comprised T₁ values across depth-levels ranging from the middle of the cortical regions to the gray/white matter boundary. Individuals with AN exhibited higher T₁ values (i.e., decreased intracortical myelin concentration) in all three metrics. It remains to be established if these abnormalities result from undernutrition or specific lipid nutritional imbalances, or are trait markers; and whether they may contribute to neurobiological deficits seen in AN.

DeepLesionBrain: Towards a broader deep-learning generalization for multiple sclerosis lesion segmentation

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However, state-of-the-art approaches trained to perform well on highly-controlled datasets fail to generalize on clinical data from unseen datasets. Instead of proposing another improvement of the segmentation accuracy, we propose a novel method robust to domain shift and performing well on unseen datasets, called DeepLesionBrain (DLB). This generalization property results from three main contributions. First, DLB is based on a large group of compact 3D CNNs. This spatially distributed strategy aims to produce a robust prediction despite the risk of generalization failure of some individual networks. Second, we propose a hierarchical specialization learning (HSL) by pre-training a generic network over the whole brain, before using its weights as initialization to locally specialized networks. By this end, DLB learns both generic features extracted at global image level and specific features extracted at local image level. Finally, DLB includes a new image quality data augmentation to reduce dependency to training data specificity (e.g., acquisition protocol). DLB generalization was validated in cross-dataset experiments on MSSEG'16, ISBI challenge, and in-house datasets. During experiments, DLB showed higher segmentation accuracy, better segmentation consistency and greater generalization performance compared to state-of-the-art methods. Therefore, DLB offers a robust framework well-suited for clinical practice.

Cortical Morphology in Autism: Findings from a Cortical Shape-Adaptive Approach to Local Gyrification Indexing

It has been challenging to elucidate the differences in brain structure that underlie behavioral features of autism. Prior studies have begun to identify patterns of changes in autism across multiple structural indices, including cortical thickness, local gyrification, and sulcal depth. However, common approaches to local gyrification indexing used in prior studies have been limited by low spatial resolution relative to functional brain topography. In this study, we analyze the aforementioned structural indices, utilizing a new method of local gyrification indexing that quantifies this index adaptively in relation to specific sulci/gyri, improving interpretation with respect to functional organization. Our sample included n = 115 autistic and n = 254 neurotypical participants aged 5-54, and we investigated structural patterns by group, age, and autism-related behaviors. Differing structural patterns by group emerged in many regions, with age moderating group differences particularly in frontal and limbic regions. There were also several regions, particularly in sensory areas, in which one or more of the structural indices of interest either positively or negatively covaried with autism-related behaviors. Given the advantages of this approach, future studies may benefit from its application in hypothesis-driven examinations of specific brain regions and/or longitudinal studies to assess brain development in autism.

Synthetic atrophy for longitudinal surface-based cortical thickness measurement

Difficulty in validating accuracy remains a substantial setback in the field of surface-based cortical thickness (CT) measurement due to the lack of experimental validation against ground truth. Although methods have been developed to create synthetic datasets for this purpose, none provide a robust mechanism for measuring exact thickness changes with surface-based approaches. This work presents a registration-based technique for inducing synthetic cortical atrophy to create a longitudinal, ground truth dataset specifically designed for accuracy validation of surface-based CT measurements. Across the entire brain, we show our method can induce up to between 0.6 and 2.6 mm of localized cortical atrophy in a given gyrus depending on the region's original thickness. By calculating the image deformation to induce this atrophy at 400% of the original resolution in each direction, we can induce a sub-voxel resolution amount of atrophy while minimizing partial volume effects. We also show that our method can be extended beyond application to CT measurements for the accuracy validation of longitudinal cortical segmentation and surface reconstruction pipelines when measuring accuracy against cortical landmarks. Importantly, our method relies exclusively on publicly available software and datasets.

Evaluating White Matter Lesion Segmentations with Refined Sørensen-Dice Analysis

The Sørensen-Dice index (SDI) is a widely used measure for evaluating medical image segmentation algorithms. It offers a standardized measure of segmentation accuracy which has proven useful. However, it offers diminishing insight when the number of objects is unknown, such as in white matter lesion segmentation of multiple sclerosis (MS) patients. We present a refinement for finer grained parsing of SDI results in situations where the number of objects is unknown. We explore these ideas with two case studies showing what can be learned from our two presented studies. Our first study explores an inter-rater comparison, showing that smaller lesions cannot be reliably identified. In our second case study, we demonstrate fusing multiple MS lesion segmentation algorithms based on the insights into the algorithms provided by our analysis to generate a segmentation that exhibits improved performance. This work demonstrates the wealth of information that can be learned from refined analysis of medical image segmentations.

The Impact of MRI T1 Hypointense Brain Lesions on Cerebral Deep Gray Matter Volume Measures in Multiple Sclerosis

BACKGROUND AND PURPOSE

Deep gray matter (DGM) atrophy has been shown at early stages of multiple sclerosis (MS) and reported as an informative marker of cognitive dysfunction and clinical progression. Therefore, accurate measurement of DGM structure volume is a key priority in MS research. Findings from prior studies have shown that hypointense T1 lesions may impact the accuracy of global brain volume measures; however, literature on the effects of hypointense T1 lesions on DGM structure volumes is sparse.

METHODS

We explored the effects of hypointense T1 lesions on data from 54 relapsing remitting MS patients. Lesions were segmented both manually and with a freely available automatic lesion segmentation/in-painting algorithm (Lesion Segmentation Tool-LST). Volumes of 14 DGM structures were calculated from non-in-painted and in-painted images and compared via paired t-tests, intraclass correlation coefficient, and Dice similarity coefficient.

RESULTS

There were no significant differences in DGM structural volumes between non-in-painted and in-painted images. Automatic lesion-segmentation/in-painting tool provided similar results to manual segmentation/in-painting.

CONCLUSIONS

Our results suggest that lesion in-painting has a negligible impact on DGM structure volume measurement although some regions are more vulnerable to the impact of lesions than others. Furthermore, manual lesion segmentation/in-painting can be replaced by an automatic segmentation/in-painting process.

Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis

BACKGROUND

The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear.

OBJECTIVE

To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS.

METHODS

A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category.

RESULTS

A total of 86 DMT epochs (DMT-H: n = 32; DMT-L: n = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (-0.15% vs -0.81%; p = 0.001) and putaminal (-0.27% vs -0.73%; p = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume.

CONCLUSION

DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS.

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Depth-dependent intracortical myelin organization in the living human brain determined by in vivo ultra-high field magnetic resonance imaging

BACKGROUND

Intracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI.

METHODS

Twenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T₁-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T₁ values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T₁ values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls.

RESULTS

Intracortical T₁ values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T₁ values and non-linearity indices (P < 10^-5). Both T₁ values (P < 10^-5) and non-linearity indices (P < 10^-15) were associated with cortical thickness. Higher myelin concentration but only in the deepest cortical levels was associated with increased subcortical fractional anisotropy (P = 0.05).

CONCLUSIONS

We demonstrate the usefulness of an automatic, whole-brain method to perform depth-dependent examination of intracortical myelin organization. The extracted metrics, T₁ values and the non-linearity index, have characteristic patterns across cortical regions, and are associated with thickness and underlying white matter microstructure.

Bayesian framework inspired no-reference region-of-interest quality measure for brain MRI images

We describe a postacquisition, attribute-based quality assessment method for brain magnetic resonance imaging (MRI) images. It is based on the application of Bayes theory to the relationship between entropy and image quality attributes. The entropy feature image of a slice is segmented into low- and high-entropy regions. For each entropy region, there are three separate observations of contrast, standard deviation, and sharpness quality attributes. A quality index for a quality attribute is the posterior probability of an entropy region given any corresponding region in a feature image where quality attribute is observed. Prior belief in each entropy region is determined from normalized total clique potential (TCP) energy of the slice. For TCP below the predefined threshold, the prior probability for a region is determined by deviation of its percentage composition in the slice from a standard normal distribution built from 250 MRI volume data provided by Alzheimer's Disease Neuroimaging Initiative. For TCP above the threshold, the prior is computed using a mathematical model that describes the TCP-noise level relationship in brain MRI images. Our proposed method assesses the image quality of each entropy region and the global image. Experimental results demonstrate good correlation with subjective opinions of radiologists for different types and levels of quality distortions.

Longitudinal multiple sclerosis lesion segmentation data resource

The data presented in this article is related to the research article entitled “Longitudinal multiple sclerosis lesion segmentation: Resource and challenge” (Carass et al., 2017) [1]. In conjunction with the 2015 International Symposium on Biomedical Imaging, we organized a longitudinal multiple sclerosis (MS) lesion segmentation challenge providing training and test data to registered participants. The training data consists of five subjects with a mean of 4.4 (±0.55) time-points, and test data of fourteen subjects with a mean of 4.4 (±0.67) time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. The training data including multi-modal scans and manually delineated lesion masks is available for download.1 In addition, the testing data is also being made available in conjunction with a website for evaluating the automated analysis of the testing data.

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4.4 time-points, and test data of fourteen subjects with a mean of 4.4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website2 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

Random forest regression for magnetic resonance image synthesis

By choosing different pulse sequences and their parameters, magnetic resonance imaging (MRI) can generate a large variety of tissue contrasts. This very flexibility, however, can yield inconsistencies with MRI acquisitions across datasets or scanning sessions that can in turn cause inconsistent automated image analysis. Although image synthesis of MR images has been shown to be helpful in addressing this problem, an inability to synthesize both T2-weighted brain images that include the skull and FLuid Attenuated Inversion Recovery (FLAIR) images has been reported. The method described herein, called REPLICA, addresses these limitations. REPLICA is a supervised random forest image synthesis approach that learns a nonlinear regression to predict intensities of alternate tissue contrasts given specific input tissue contrasts. Experimental results include direct image comparisons between synthetic and real images, results from image analysis tasks on both synthetic and real images, and comparison against other state-of-the-art image synthesis methods. REPLICA is computationally fast, and is shown to be comparable to other methods on tasks they are able to perform. Additionally REPLICA has the capability to synthesize both T2-weighted images of the full head and FLAIR images, and perform intensity standardization between different imaging datasets.

Consistent cortical reconstruction and multi-atlas brain segmentation

Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source.

Combining Multi-atlas Segmentation with Brain Surface Estimation

Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

Construction of 4D high-definition cortical surface atlases of infants: Methods and applications

In neuroimaging, cortical surface atlases play a fundamental role for spatial normalization, analysis, visualization, and comparison of results across individuals and different studies. However, existing cortical surface atlases created for adults are not suitable for infant brains during the first two postnatal years, which is the most dynamic period of postnatal structural and functional development of the highly-folded cerebral cortex. Therefore, spatiotemporal cortical surface atlases for infant brains are highly desired yet still lacking for accurate mapping of early dynamic brain development. To bridge this significant gap, leveraging our infant-dedicated computational pipeline for cortical surface-based analysis and the unique longitudinal infant MRI dataset acquired in our research center, in this paper, we construct the first spatiotemporal (4D) high-definition cortical surface atlases for the dynamic developing infant cortical structures at seven time points, including 1, 3, 6, 9, 12, 18, and 24 months of age, based on 202 serial MRI scans from 35 healthy infants. For this purpose, we develop a novel method to ensure the longitudinal consistency and unbiasedness to any specific subject and age in our 4D infant cortical surface atlases. Specifically, we first compute the within-subject mean cortical folding by unbiased groupwise registration of longitudinal cortical surfaces of each infant. Then we establish longitudinally-consistent and unbiased inter-subject cortical correspondences by groupwise registration of the geometric features of within-subject mean cortical folding across all infants. Our 4D surface atlases capture both longitudinally-consistent dynamic mean shape changes and the individual variability of cortical folding during early brain development. Experimental results on two independent infant MRI datasets show that using our 4D infant cortical surface atlases as templates leads to significantly improved accuracy for spatial normalization of cortical surfaces across infant individuals, in comparison to the infant surface atlases constructed without longitudinal consistency and also the FreeSurfer adult surface atlas. Moreover, based on our 4D infant surface atlases, for the first time, we reveal the spatially-detailed, region-specific correlation patterns of the dynamic cortical developmental trajectories between different cortical regions during early brain development.

Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study

A comprehensive analysis of the effect of lesion in-painting on the estimation of cortical thickness using magnetic resonance imaging was performed on a large cohort of 918 relapsing-remitting multiple sclerosis patients who participated in a phase III multicenter clinical trial. An automatic lesion in-painting algorithm was developed and implemented. Cortical thickness was measured using the FreeSurfer pipeline with and without in-painting. The effect of in-painting was evaluated using FreeSurfer's paired analysis pipeline. Multivariate regression analysis was also performed with field strength and lesion load as additional factors. Overall, the estimated cortical thickness was different with in-painting than without. The effect of in-painting was observed to be region dependent, more significant in the left hemisphere compared to the right, was more prominent at 1.5 T relative to 3 T, and was greater at higher lesion volumes. Our results show that even for data acquired at 1.5 T in patients with high lesion load, the mean cortical thickness difference with and without in-painting is ∼2%. Based on these results, it appears that in-painting has only a small effect on the estimated regional and global cortical thickness. Hum Brain Mapp 36:3749-3760, 2015. © 2015 Wiley Periodicals, Inc.

MR image synthesis by contrast learning on neighborhood ensembles

Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. Although intensity standardization and image synthesis methods have been introduced to address this problem, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this paper, an image synthesis approach that first estimates the pulse sequence parameters of the subject image is presented. The estimated parameters are then used with a collection of atlas or training images to generate a new atlas image having the same contrast as the subject image. This additional image provides an ideal source from which to synthesize any other target pulse sequence image contained in the atlas. In particular, a nonlinear regression intensity mapping is trained from the new atlas image to the target atlas image and then applied to the subject image to yield the particular target pulse sequence within the atlas. Both intensity standardization and synthesis of missing tissue contrasts can be achieved using this framework. The approach was evaluated on both simulated and real data, and shown to be superior in both intensity standardization and synthesis to other established methods.

Multi-Output Decision Trees for Lesion Segmentation in Multiple Sclerosis

Multiple Sclerosis (MS) is a disease of the central nervous system in which the protective myelin sheath of the neurons is damaged. MS leads to the formation of lesions, predominantly in the white matter of the brain and the spinal cord. The number and volume of lesions visible in magnetic resonance (MR) imaging (MRI) are important criteria for diagnosing and tracking the progression of MS. Locating and delineating lesions manually requires the tedious and expensive efforts of highly trained raters. In this paper, we propose an automated algorithm to segment lesions in MR images using multi-output decision trees. We evaluated our algorithm on the publicly available MICCAI 2008 MS Lesion Segmentation Challenge training dataset of 20 subjects, and showed improved results in comparison to state-of-the-art methods. We also evaluated our algorithm on an in-house dataset of 49 subjects with a true positive rate of 0.41 and a positive predictive value 0.36.

White matter lesion filling improves the accuracy of cortical thickness measurements in multiple sclerosis patients: a longitudinal study

Background

Previous studies have demonstrated that white matter (WM) lesions bias automated brain tissue classifications and cerebral volume measurements. However, filling WM lesions using the intensity of neighbouring normal-appearing WM has been shown to increase the accuracy of automated volume measurements in the brain. In the present study, we investigate the influence of WM lesions on cortical thickness (CTh) measures and assessed the impact of lesion filling on both cross-sectional/longitudinal and global/regional measurements of CTh in multiple sclerosis (MS) patients.

Methods

Fifty MS patients were studied at baseline as well as after three and six years of follow-up. CTh was estimated using a fully automated pipeline (CIVET) on T1-weighted magnetic resonance images data acquired at 1.5 Tesla without (original) and with WM lesion filling (filled). WM lesions were semi-automatically segmented and then filled with the mean intensity of the neighbouring voxels. For both original and filled T1 images we investigated and compared the main CIVET’s steps: tissue classification, surfaces generation and CTh measurement.

Results

On the original T1 images, the majority of WM lesion volume (72%) was wrongly classified as gray matter (GM). After lesion filling the accuracy of WM lesions classification improved significantly (p < 0.001, 94% of WM lesion volume correctly classified) as well as the WM surface generation (p < 0.0001). The mean CTh computed on the original T1 images, overall time points, was significantly thinner (p < 0.001) compared the CTh estimated on the filled T1 images. The vertex-wise longitudinal analysis performed on the filled T1 images showed an increased number of vertices in the fronto-temporal region with a significantly decrease of CTh over time compared the analysis performed on the original images.

Conclusion

These results indicate that WM lesions bias the CTh estimation both cross-sectionally as well as longitudinally. The lesion filling approach significantly improved the accuracy of the regional CTh estimation and has an impact also on the global estimation of CTh.

Robust cortical thickness measurement with LOGISMOS-B

Cortical thickness (CT) is an important morphometric measure that has implications for psychiatric and neurologic processes. We propose a novel approach for automatically computing CT in an accurate and robust manner using LOGISMOS-B: Layered Optimal Graph Image Segmentation of Multiple Objects and Surfaces for the Brain. LOGISMOS-B is a cortical surface segmentation method based on LOGISMOS graph segmentation and generalized gradient vector flows. We evaluate our method on two different datasets (n = 83 total). The results show that LOGISMOS-B is more accurate than the popular FreeSurfer (FS) method and provides more reliable thickness measurements across a variety of challenging images. LOGISMOS-B accurately recovers known CT patterns, both across cortical lobes and locally, such as between the banks of the central sulcus, in healthy subjects and MS patients. Manual landmarks indicate a signed surface distance of 0.081 +/- 0.447mm for WM and 0.018 +/- 0.498mm for LOGISMOS-B, compared to 0.263 +/- 0.452mm for WM and -0.167 +/- 0.556mm for GM for FS, highlighting the surface placement accuracy of LOGISMOS-B. Finally, a regresion study shows that LOGISMOS-B provides strong correlation with age and plausible annual thinning rates across the cortex, with locally discerning thinning patterns, in agreement with the literature.