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A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain

Mostapha, Mahmoud; Kim, Sun Hyung; Evans, Alan C; Dager, Stephen R; Estes, Annette M; McKinstry, Robert C; Botteron, Kelly N; Gerig, Guido; Pizer, Stephen M; Schultz, Robert T; Hazlett, Heather C; Piven, Joseph; Girault, Jessica B; Shen, Mark D; Styner, Martin A
Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development.
PMCID:7561674
PMID: 33132824
ISSN: 1662-4548
CID: 4965462

Rapid Radial T1 and T2 Mapping of the Hip Articular Cartilage With Magnetic Resonance Fingerprinting

Cloos, Martijn A; Assländer, Jakob; Abbas, Batool; Fishbaugh, James; Babb, James S; Gerig, Guido; Lattanzi, Riccardo
BACKGROUND:Quantitative MRI can detect early changes in cartilage biochemical components, but its routine clinical implementation is challenging. PURPOSE/OBJECTIVE:along radial sections of the hip for accurate and reproducible multiparametric quantitative cartilage assessment in a clinically feasible scan time. STUDY TYPE/METHODS:Reproducibility, technical validation. SUBJECTS/PHANTOM/UNASSIGNED:A seven-compartment phantom and three healthy volunteers. FIELD STRENGTH/SEQUENCE/UNASSIGNED:at 3 T was developed. Automatic positioning and semiautomatic cartilage segmentation were implemented to improve consistency and simplify workflow. ASSESSMENT/RESULTS:Intra- and interscanner variability of our technique was assessed over multiple scans on three different MR scanners. STATISTICAL TESTS/UNASSIGNED:over six radial slices was calculated. Restricted maximum likelihood estimation of variance components was used to estimate intrasubject variances reflecting variation between results from the two scans using the same scanner (intrascanner variance) and variation among results from the three scanners (interscanner variance). RESULTS:. DATA CONCLUSION/UNASSIGNED:Our method, which includes slice positioning, model-based parameter estimation, and cartilage segmentation, is highly reproducible. It could enable employing quantitative hip cartilage evaluation for longitudinal and multicenter studies. LEVEL OF EVIDENCE/METHODS:1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018.
PMID: 30584691
ISSN: 1522-2586
CID: 3560362

Tensor decomposition of hyperspectral images to study autofluorescence in age-related macular degeneration

Dey, Neel; Hong, Sungmin; Ach, Thomas; Koutalos, Yiannis; Curcio, Christine A; Smith, R Theodore; Gerig, Guido
Autofluorescence is the emission of light by naturally occurring tissue components on the absorption of incident light. Autofluorescence within the eye is associated with several disorders, such as Age-related Macular Degeneration (AMD) which is a leading cause of central vision loss. Its pathogenesis is incompletely understood, but endogenous fluorophores in retinal tissue might play a role. Hyperspectral fluorescence microscopy of ex-vivo retinal tissue can be used to determine the fluorescence emission spectra of these fluorophores. Comparisons of spectra in healthy and diseased tissues can provide important insights into the pathogenesis of AMD. However, the spectrum from each pixel of the hyperspectral image is a superposition of spectra from multiple overlapping tissue components. As spectra cannot be negative, there is a need for a non-negative blind source separation model to isolate individual spectra. We propose a tensor formulation by leveraging multiple excitation wavelengths to excite the tissue sample. Arranging images from different excitation wavelengths as a tensor, a non-negative tensor decomposition can be performed to recover a provably unique low-rank model with factors representing emission and excitation spectra of these materials and corresponding abundance maps of autofluorescent substances in the tissue sample. We iteratively impute missing values common in fluorescence measurements using Expectation-Maximization and use L2 regularization to reduce ill-posedness. Further, we present a framework for performing group hypothesis testing on hyperspectral images, finding significant differences in spectra between AMD and control groups in the peripheral macula. In the absence of ground truth, i.e. molecular identification of fluorophores, we provide a rigorous validation of chosen methods on both synthetic and real images where fluorescence spectra are known. These methodologies can be applied to the study of other pathologies presenting autofluorescence that can be captured by hyperspectral imaging.
PMID: 31203169
ISSN: 1361-8423
CID: 3962272

Facilitating manual segmentation of 3d datasets using contour and intensity guided interpolation

Chapter by: Ravikumar, Sadhana; Wisse, Laura; Gao, Yang; Gerig, Guido; Yushkevich, Paul
in: Proceedings - International Symposium on Biomedical Imaging by
[S.l.] : IEEE Computer Societyhelp@computer.org, 2019
pp. 714-718
ISBN: 9781538636411
CID: 4164812

ACCELERATION CONTROLLED DIFFEOMORPHISMS FOR NONPARAMETRIC IMAGE REGRESSION

Fishbaugh, James; Gerig, Guido
The analysis of medical image time-series is becoming increasingly important as longitudinal imaging studies are maturing and large scale open imaging databases are becoming available. Image regression is widely used for several purposes: as a statistical representation for hypothesis testing, to bring clinical scores and images not acquired at the same time into temporal correspondence, or as a consistency filter to enforce temporal correlation. Geodesic image regression is the most prominent method, but the geodesic constraint limits the flexibility and therefore the application of the model, particularly when the observation time window is large or the anatomical changes are non-monotonic. In this paper, we propose to parameterize diffeomorphic flow by acceleration rather than velocity, as in the geodesic model. This results in a nonparametric image regression model which is completely flexible to capture complex change trajectories, while still constrained to be diffeomorphic and with a guarantee of temporal smoothness. We demonstrate the application of our model on synthetic 2D images as well as real 3D images of the cardiac cycle.
PMCID:6959201
PMID: 31938451
ISSN: 1945-7928
CID: 4294612

Model selection for spatiotemporal modeling of early childhood sub-cortical development

Fishbaugh, James; Paniagua, Beatriz; Mostapha, Mahmoud; Styner, Martin; Murphy, Veronica; Gilmore, John; Gerig, Guido
Spatiotemporal shape models capture the dynamics of shape change over time and are an essential tool for monitoring and measuring anatomical growth or degeneration. In this paper we evaluate non-parametric shape regression on the challenging problem of modeling early childhood sub-cortical development starting from birth. Due to the flexibility of the model, it can be challenging to choose parameters which lead to a good model fit yet does not over fit. We systematically test a variety of parameter settings to evaluate model fit as well as the sensitivity of the method to specific parameters, and we explore the impact of missing data on model estimation.
PMCID:6503845
PMID: 31073259
ISSN: 0277-786x
CID: 3903192

Multi-modal Image Fusion for Multispectral Super-resolution in Microscopy

Dey, Neel; Li, Shijie; Bermond, Katharina; Heintzmann, Rainer; Curcio, Christine A; Ach, Thomas; Gerig, Guido
Spectral imaging is a ubiquitous tool in modern biochemistry. Despite acquiring dozens to thousands of spectral channels, existing technology cannot capture spectral images at the same spatial resolution as structural microscopy. Due to partial voluming and low light exposure, spectral images are often difficult to interpret and analyze. This highlights a need to upsample the low-resolution spectral image by using spatial information contained in the high-resolution image, thereby creating a fused representation with high specificity both spatially and spectrally. In this paper, we propose a framework for the fusion of co-registered structural and spectral microscopy images to create super-resolved representations of spectral images. As a first application, we super-resolve spectral images of retinal tissue imaged with confocal laser scanning microscopy, by using spatial information from structured illumination microscopy. Second, we super-resolve mass spectroscopic images of mouse brain tissue, by using spatial information from high-resolution histology images. We present a systematic validation of model assumptions crucial towards maintaining the original nature of spectra and the applicability of super-resolution. Goodness-of-fit for spectral predictions are evaluated through functional R2 values, and the spatial quality of the super-resolved images are evaluated using normalized mutual information.
PMCID:6881105
PMID: 31777411
ISSN: 0277-786x
CID: 4216552

Analysis of the kinematic motion of the wrist from 4D magnetic resonance imaging

Chapter by: Abbas, Batool; Fishbaugh, James; Petchprapa, Catherine; Lattanzi, Riccardo; Gerig, Guido
in: Progress in Biomedical Optics and Imaging - Proceedings of SPIE by
[S.l.] : SPIEspie@spie.org, 2019
pp. ?-?
ISBN: 9781510625457
CID: 4008682

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

Yushkevich, Paul A; Pashchinskiy, Artem; Oguz, Ipek; Mohan, Suyash; Schmitt, J Eric; Stein, Joel M; Zukić, Dženan; Vicory, Jared; McCormick, Matthew; Yushkevich, Natalie; Schwartz, Nadav; Gao, Yang; Gerig, Guido
ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.
PMID: 29946897
ISSN: 1559-0089
CID: 3163142

Restricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder

McKinnon, Claire J; Eggebrecht, Adam T; Todorov, Alexandre; Wolff, Jason J; Elison, Jed T; Adams, Chloe M; Snyder, Abraham Z; Estes, Annette M; Zwaigenbaum, Lonnie; Botteron, Kelly N; McKinstry, Robert C; Marrus, Natasha; Evans, Alan; Hazlett, Heather C; Dager, Stephen R; Paterson, Sarah J; Pandey, Juhi; Schultz, Robert T; Styner, Martin A; Gerig, Guido; Schlaggar, Bradley L; Petersen, Steven E; Piven, Joseph; Pruett, John R
BACKGROUND:Restricted and repetitive behaviors (RRBs), detectable by 12 months in many infants in whom autism spectrum disorder (ASD) is later diagnosed, may represent some of the earliest behavioral markers of ASD. However, brain function underlying the emergence of these key behaviors remains unknown. METHODS:Behavioral and resting-state functional connectivity (fc) magnetic resonance imaging data were collected from 167 children at high and low familial risk for ASD at 12 and 24 months (n = 38 at both time points). Twenty infants met criteria for ASD at 24 months. We divided RRBs into four subcategories (restricted, stereotyped, ritualistic/sameness, self-injurious) and used a data-driven approach to identify functional brain networks associated with the development of each RRB subcategory. RESULTS:Higher scores for ritualistic/sameness behavior were associated with less positive fc between visual and control networks at 12 and 24 months. Ritualistic/sameness and stereotyped behaviors were associated with less positive fc between visual and default mode networks at 12 months. At 24 months, stereotyped and restricted behaviors were associated with more positive fc between default mode and control networks. Additionally, at 24 months, stereotyped behavior was associated with more positive fc between dorsal attention and subcortical networks, whereas restricted behavior was associated with more positive fc between default mode and dorsal attention networks. No significant network-level associations were observed for self-injurious behavior. CONCLUSIONS:These observations mark the earliest known description of functional brain systems underlying RRBs, reinforce the construct validity of RRB subcategories in infants, and implicate specific neural substrates for future interventions targeting RRBs.
PMID: 30446435
ISSN: 2451-9030
CID: 3500772