DeepComBat: A statistically motivated, hyperparameter-robust, deep learning approach to harmonization of neuroimaging data.

Neuroimaging data acquired using multiple scanners or protocols are increasingly available. However, such data exhibit technical artifacts across batches which introduce confounding and decrease reproducibility. This is especially true when multi-batch data are analyzed using complex downstream models which are more likely to pick up on and implicitly incorporate batch-related information. Previously proposed image harmonization methods have sought to remove these batch effects; however, batch effects remain detectable in the data after applying these methods. We present DeepComBat, a deep learning harmonization method based on a conditional variational autoencoder and the ComBat method. DeepComBat combines the strengths of statistical and deep learning methods in order to account for the multivariate relationships between features while simultaneously relaxing strong assumptions made by previous deep learning harmonization methods. As a result, DeepComBat can perform multivariate harmonization while preserving data structure and avoiding the introduction of synthetic artifacts. We apply this method to cortical thickness measurements from a cognitive-aging cohort and show DeepComBat qualitatively and quantitatively outperforms existing methods in removing batch effects while preserving biological heterogeneity. Additionally, DeepComBat provides a new perspective for statistically motivated deep learning harmonization methods.

Differential effects of emotional valence on mnemonic performance with greater hippocampal maturity.

The hippocampal formation (HF) facilitates declarative memory, with subfields providing unique contributions to memory performance. Maturational differences across subfields facilitate a shift toward increased memory specificity, with peripuberty sitting at the inflection point. Peripuberty is also a sensitive period in the development of anxiety disorders. We believe HF development during puberty is critical to negative overgeneralization, a common feature of anxiety disorders. To investigate this claim, we examined the relationship between mnemonic generalization and a cross-sectional pubertal maturity index (PMI) derived from partial least squares correlation (PLSC) analyses of subfield volumes and structural connectivity from T1-weighted and diffusion-weighted scans, respectively. Participants aged 9-14 yr, from clinical and community sources, performed a recognition task with emotionally valent (positive, negative, and neutral) images. HF volumetric PMI was positively associated with generalization for negative images. Hippocampal-medial prefrontal cortex connectivity PMI evidenced a behavioral relationship similar to that of the HF volumetric approach. These findings reflect a novel developmentally related balance between generalization behavior supported by the hippocampus and its connections with other regions, with maturational differences in this balance potentially contributing to negative overgeneralization during peripuberty.

Automated analysis of low-field brain MRI in cerebral malaria.

A central challenge of medical imaging studies is to extract biomarkers that characterize disease pathology or outcomes. Modern automated approaches have found tremendous success in high-resolution, high-quality magnetic resonance images. These methods, however, may not translate to low-resolution images acquired on magnetic resonance imaging (MRI) scanners with lower magnetic field strength. In low-resource settings where low-field scanners are more common and there is a shortage of radiologists to manually interpret MRI scans, it is critical to develop automated methods that can augment or replace manual interpretation, while accommodating reduced image quality. We present a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, inspired by a project in which children with cerebral malaria (CM) were imaged using low-field 0.35 Tesla MRI. We integrate multiatlas label fusion, which leverages high-resolution images from another sample as prior spatial information, with parametric Gaussian hidden Markov models based on image intensities, to create a robust method for determining ventricular cerebrospinal fluid volume. We also propose normalized image intensity and texture measurements to determine the loss of gray-to-white matter tissue differentiation and sulcal effacement. These integrated biomarkers have excellent classification performance for determining severe brain swelling due to CM.

Mitigating site effects in covariance for machine learning in neuroimaging data.

To acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple sites. These effects have been shown to bias comparison between sites, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing site-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of machine learning (ML) in neuroimaging has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing site effects in mean and variance may not be sufficient for ML. This stems from the fact that such methods fail to address how correlations between measurements can vary across sites. Data from the Alzheimer's Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across sites and that popular harmonization techniques do not address this issue. We then propose a novel harmonization method called Correcting Covariance Batch Effects (CovBat) that removes site effects in mean, variance, and covariance. We apply CovBat and show that within-site correlation matrices are successfully harmonized. Furthermore, we find that ML methods are unable to distinguish scanner manufacturer after our proposed harmonization is applied, and that the CovBat-harmonized data retain accurate prediction of disease group.

Unexpected role of interferon-γ in regulating neuronal connectivity and social behaviour.

Immune dysfunction is commonly associated with several neurological and mental disorders. Although the mechanisms by which peripheral immunity may influence neuronal function are largely unknown, recent findings implicate meningeal immunity influencing behaviour, such as spatial learning and memory. Here we show that meningeal immunity is also critical for social behaviour; mice deficient in adaptive immunity exhibit social deficits and hyper-connectivity of fronto-cortical brain regions. Associations between rodent transcriptomes from brain and cellular transcriptomes in response to T-cell-derived cytokines suggest a strong interaction between social behaviour and interferon-γ (IFN-γ)-driven responses. Concordantly, we demonstrate that inhibitory neurons respond to IFN-γ and increase GABAergic (γ-aminobutyric-acid) currents in projection neurons, suggesting that IFN-γ is a molecular link between meningeal immunity and neural circuits recruited for social behaviour. Meta-analysis of the transcriptomes of a range of organisms reveals that rodents, fish, and flies elevate IFN-γ/JAK-STAT-dependent gene signatures in a social context, suggesting that the IFN-γ signalling pathway could mediate a co-evolutionary link between social/aggregation behaviour and an efficient anti-pathogen response. This study implicates adaptive immune dysfunction, in particular IFN-γ, in disorders characterized by social dysfunction and suggests a co-evolutionary link between social behaviour and an anti-pathogen immune response driven by IFN-γ signalling.

Image- versus histogram-based considerations in semantic segmentation of pulmonary hyperpolarized gas images.

PURPOSE: To characterize the differences between histogram-based and image-based algorithms for segmentation of hyperpolarized gas lung images. METHODS: Four previously published histogram-based segmentation algorithms (ie, linear binning, hierarchical k-means, fuzzy spatial c-means, and a Gaussian mixture model with a Markov random field prior) and an image-based convolutional neural network were used to segment 2 simulated data sets derived from a public (n = 29 subjects) and a retrospective collection (n = 51 subjects) of hyperpolarized 129Xe gas lung images transformed by common MRI artifacts (noise and nonlinear intensity distortion). The resulting ventilation-based segmentations were used to assess algorithmic performance and characterize optimization domain differences in terms of measurement bias and precision. RESULTS: Although facilitating computational processing and providing discriminating clinically relevant measures of interest, histogram-based segmentation methods discard important contextual spatial information and are consequently less robust in terms of measurement precision in the presence of common MRI artifacts relative to the image-based convolutional neural network. CONCLUSIONS: Direct optimization within the image domain using convolutional neural networks leverages spatial information, which mitigates problematic issues associated with histogram-based approaches and suggests a preferred future research direction. Further, the entire processing and evaluation framework, including the newly reported deep learning functionality, is available as open source through the well-known Advanced Normalization Tools ecosystem.

Single-Session Bronchial Thermoplasty Guided by 129Xe Magnetic Resonance Imaging. A Pilot Randomized Controlled Clinical Trial.

Rationale: Adverse events have limited the use of bronchial thermoplasty (BT) in severe asthma.Objectives: We sought to evaluate the effectiveness and safety of using 129Xe magnetic resonance imaging (129Xe MRI) to prioritize the most involved airways for guided BT.Methods: Thirty subjects with severe asthma were imaged with volumetric computed tomography and 129Xe MRI to quantitate segmental ventilation defects. Subjects were randomized to treatment of the six most involved airways in the first session (guided group) or a standard three-session BT (unguided). The primary outcome was the change in Asthma Quality of Life Questionnaire score from baseline to 12 weeks after the first BT for the guided group compared with after three treatments for the unguided group.Measurements and Main Results: There was no significant difference in quality of life after one guided compared with three unguided BTs (change in Asthma Quality of Life Questionnaire guided = 0.91 [95% confidence interval, 0.28-1.53]; unguided = 1.49 [95% confidence interval, 0.84-2.14]; P = 0.201). After one BT, the guided group had a greater reduction in the percentage of poorly and nonventilated lung from baseline when compared with unguided (-17.2%; P = 0.009). Thirty-three percent experienced asthma exacerbations after one guided BT compared with 73% after three unguided BTs (P = 0.028).Conclusions: Results of this pilot study suggest that similar short-term improvements can be achieved with one BT treatment guided by 129Xe MRI when compared with standard three-treatment-session BT with fewer periprocedure adverse events.

Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.

While aggregation of neuroimaging datasets from multiple sites and scanners can yield increased statistical power, it also presents challenges due to systematic scanner effects. This unwanted technical variability can introduce noise and bias into estimation of biological variability of interest. We propose a method for harmonizing longitudinal multi-scanner imaging data based on ComBat, a method originally developed for genomics and later adapted to cross-sectional neuroimaging data. Using longitudinal cortical thickness measurements from 663 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, we demonstrate the presence of additive and multiplicative scanner effects in various brain regions. We compare estimates of the association between diagnosis and change in cortical thickness over time using three versions of the ADNI data: unharmonized data, data harmonized using cross-sectional ComBat, and data harmonized using longitudinal ComBat. In simulation studies, we show that longitudinal ComBat is more powerful for detecting longitudinal change than cross-sectional ComBat and controls the type I error rate better than unharmonized data with scanner included as a covariate. The proposed method would be useful for other types of longitudinal data requiring harmonization, such as genomic data, or neuroimaging studies of neurodevelopment, psychiatric disorders, or other neurological diseases.

Positional Therapy and Regional Pulmonary Ventilation.

BACKGROUND: Prone ventilation redistributes lung inflation along the gravitational axis; however, localized, nongravitational effects of body position are less well characterized. The authors hypothesize that positional inflation improvements follow both gravitational and nongravitational distributions. This study is a nonoverlapping reanalysis of previously published large animal data. METHODS: Five intubated, mechanically ventilated pigs were imaged before and after lung injury by tracheal injection of hydrochloric acid (2 ml/kg). Computed tomography scans were performed at 5 and 10 cm H2O positive end-expiratory pressure (PEEP) in both prone and supine positions. All paired prone-supine images were digitally aligned to each other. Each unit of lung tissue was assigned to three clusters (K-means) according to positional changes of its density and dimensions. The regional cluster distribution was analyzed. Units of tissue displaying lung recruitment were mapped. RESULTS: We characterized three tissue clusters on computed tomography: deflation (increased tissue density and contraction), limited response (stable density and volume), and reinflation (decreased density and expansion). The respective clusters occupied (mean ± SD including all studied conditions) 29.3 ± 12.9%, 47.6 ± 11.4%, and 23.1 ± 8.3% of total lung mass, with similar distributions before and after lung injury. Reinflation was slightly greater at higher PEEP after injury. Larger proportions of the reinflation cluster were contained in the dorsal versus ventral (86.4 ± 8.5% vs. 13.6 ± 8.5%, P < 0.001) and in the caudal versus cranial (63.4 ± 11.2% vs. 36.6 ± 11.2%, P < 0.001) regions of the lung. After injury, prone positioning recruited 64.5 ± 36.7 g of tissue (11.4 ± 6.7% of total lung mass) at lower PEEP, and 49.9 ± 12.9 g (8.9 ± 2.8% of total mass) at higher PEEP; more than 59.0% of this recruitment was caudal. CONCLUSIONS: During mechanical ventilation, lung reinflation and recruitment by the prone positioning were primarily localized in the dorso-caudal lung. The local effects of positioning in this lung region may determine its clinical efficacy.

Automated apparent diffusion coefficient analysis for genotype prediction in lower grade glioma: association with the T2-FLAIR mismatch sign.

PURPOSE: The prognosis of lower grade glioma (LGG) patients depends (in large part) on both isocitrate dehydrogenase (IDH) gene mutation and chromosome 1p/19q codeletion status. IDH-mutant LGG without 1p/19q codeletion (IDHmut-Noncodel) often exhibit a unique imaging appearance that includes high apparent diffusion coefficient (ADC) values not observed in other subtypes. The purpose of this study was to develop an ADC analysis-based approach that can automatically identify IDHmut-Noncodel LGG. METHODS: Whole-tumor ADC metrics, including fractional tumor volume with ADC > 1.5 × 10-3mm2/s (VADC>1.5), were used to identify IDHmut-Noncodel LGG in a cohort of N = 134 patients. Optimal threshold values determined in this dataset were then validated using an external dataset containing N = 93 cases collected from The Cancer Imaging Archive. Classifications were also compared with radiologist-identified T2-FLAIR mismatch sign and evaluated concurrently to identify added value from a combined approach. RESULTS: VADC>1.5 classified IDHmut-Noncodel LGG in the internal cohort with an area under the curve (AUC) of 0.80. An optimal threshold value of 0.35 led to sensitivity/specificity = 0.57/0.93. Classification performance was similar in the validation cohort, with VADC>1.5 ≥ 0.35 achieving sensitivity/specificity = 0.57/0.91 (AUC = 0.81). Across both groups, 37 cases exhibited positive T2-FLAIR mismatch sign-all of which were IDHmut-Noncodel. Of these, 32/37 (86%) also exhibited VADC>1.5 ≥ 0.35, as did 23 additional IDHmut-Noncodel cases which were negative for T2-FLAIR mismatch sign. CONCLUSION: Tumor subregions with high ADC were a robust indicator of IDHmut-Noncodel LGG, with VADC>1.5 achieving > 90% classification specificity in both internal and validation cohorts. VADC>1.5 exhibited strong concordance with the T2-FLAIR mismatch sign and the combination of both parameters improved sensitivity in detecting IDHmut-Noncodel LGG.

ABCA7 risk variant in healthy older African Americans is associated with a functionally isolated entorhinal cortex mediating deficient generalization of prior discrimination training.

Using high-resolution resting state functional magnetic resonance imaging (fMRI), the present study tested the hypothesis that ABCA7 genetic risk differentially affects intra-medial temporal lobe (MTL) functional connectivity between MTL subfields, versus internetwork connectivity of the MTL with the medial prefrontal cortex (mPFC), in nondemented older African Americans. Although the association of ABCA7 risk variants with Alzheimer's disease (AD) has been confirmed worldwide, its effect size on the relative odds of being diagnosed with AD is significantly higher in African Americans. However, little is known about the neural correlates of cognitive function in older African Americans and how they relate to AD risk conferred by ABCA7. In a case-control fMRI study of 36 healthy African Americans, we observed ABCA7 related impairments in behavioral generalization that was mediated by dissociation in entorhinal cortex (EC) resting state functional connectivity. Specifically, ABCA7 risk variant was associated with EC-hippocampus hyper-synchronization and EC-mPFC hypo-synchronization. Carriers of the risk genotype also had a significantly smaller anterolateral EC, despite our finding no group differences on standardized neuropsychological tests. Our findings suggest a model where impaired cortical connectivity leads to a more functionally isolated EC at rest, which translates into aberrant EC-hippocampus hyper-synchronization resulting in generalization deficits. While we cannot identify the exact mechanism underlying the observed alterations in EC structure and network function, considering the relevance of Aß in ABCA7 related AD pathogenesis, the results of our study may reflect the synergistic reinforcement between amyloid and tau pathology in the EC, which significantly increases tau-induced neuronal loss and accelerates synaptic alterations. Finally, our results add to a growing literature suggesting that generalization of learning may be a useful tool for assessing the mild cognitive deficits seen in the earliest phases of prodromal AD, even before the more commonly reported deficits in episodic memory arise.

The Relationship Between Cumulative Exogenous Corticosteroid Exposure and Volumes of Hippocampal Subfields and Surrounding Structures.

PURPOSE/BACKGROUND: Glucocorticoids are a class of hormones that include naturally occurring cortisol and corticosterone, as well as prescription drugs commonly used to manage inflammatory, autoimmune, and allergic conditions. Adverse effects, including neuropsychiatric symptoms, are common. The hippocampus appears to be especially sensitive to the effects of glucocorticoids. However, to our knowledge, no studies to date have examined hippocampal subfields in humans receiving glucocorticoids. We examined patients on chronic glucocorticoid regimens to determine relationships between dose and duration of treatment, and hippocampal subfields, and related regions volumes. METHODS/PROCEDURES: The study included adult men and women receiving at least 5 mg daily of prednisone equivalents for at least 6 months. Volumes of brain regions were measured via magnetic resonance imaging. A multivariate general linear model was used for analysis, with brain volumes as dependent variables and age, sex, and cumulative corticosteroid exposure, as predictors. FINDINGS/RESULTS: The study population consisted of 81 adult outpatients (43 male) on corticosteroids (mean dose, 7.88 mg; mean duration, 76.75 months). Cumulative glucocorticoid exposure was negatively associated with left and right hippocampal dentate gyrus/CA3 volume. In subsequent subgroup analysis, this association held true for the age group older than the median age of 46 years but not for the younger age group. IMPLICATIONS/CONCLUSIONS: This finding is consistent with previous studies showing detrimental effects of elevated glucocorticoids on the hippocampus but further suggests that the dentate gyrus and CA3 regions are particularly vulnerable to those effects, which is consistent with animal models of chronic stress but has not been previously demonstrated in humans.

Unstable Inflation Causing Injury. Insight from Prone Position and Paired Computed Tomography Scans.

RATIONALE: It remains unclear how prone positioning improves survival in acute respiratory distress syndrome. Using serial computed tomography (CT), we previously reported that "unstable" inflation (i.e., partial aeration with large tidal density swings, indicating increased local strain) is associated with injury progression. OBJECTIVES: We prospectively tested whether prone position contains the early propagation of experimental lung injury by stabilizing inflation. METHODS: Injury was induced by tracheal hydrochloric acid in rats; after randomization to supine or prone position, injurious ventilation was commenced using high tidal volume and low positive end-expiratory pressure. Paired end-inspiratory (EI) and end-expiratory (EE) CT scans were acquired at baseline and hourly up to 3 hours. Each sequential pair (EI, EE) of CT images was superimposed in parametric response maps to analyze inflation. Unstable inflation was then measured in each voxel in both dependent and nondependent lung. In addition, five pigs were imaged (EI and EE) prone versus supine, before and (1 hour) after hydrochloric acid aspiration. MEASUREMENTS AND MAIN RESULTS: In rats, prone position limited lung injury propagation and increased survival (11/12 vs. 7/12 supine; P = 0.01). EI-EE densities, respiratory mechanics, and blood gases deteriorated more in supine versus prone rats. At baseline, more voxels with unstable inflation occurred in dependent versus nondependent regions when supine (41 ± 6% vs. 18 ± 7%; P < 0.01) but not when prone. In supine pigs, unstable inflation predominated in dorsal regions and was attenuated by prone positioning. CONCLUSIONS: Prone position limits the radiologic progression of early lung injury. Minimizing unstable inflation in this setting may alleviate the burden of acute respiratory distress syndrome.

Tidal changes on CT and progression of ARDS.

BACKGROUND: Uncertain prediction of outcome in acute respiratory distress syndrome (ARDS) impedes individual patient management and clinical trial design. OBJECTIVES: To develop a radiological metric of injurious inflation derived from matched inspiratory and expiratory CT scans, calibrate it in a model of experimental lung injury, and test it in patients with ARDS. METHODS: 73 anaesthetised rats (acid aspiration model) were ventilated (protective or non-protective) for up to 4 hours to generate a spectrum of lung injury. CT was performed (inspiratory and expiratory) at baseline each hour, paired inspiratory and expiratory images were superimposed and voxels tracked in sequential scans. In nine patients with ARDS, paired inspiratory and expiratory CT scans from the first intensive care unit week were analysed. RESULTS: In experimental studies, regions of lung with unstable inflation (ie, partial or reversible airspace filling reflecting local strain) were the areas in which subsequent progression of injury was greatest in terms of progressive infiltrates (R=0.77) and impaired compliance (R=0.67, p<0.01). In patients with ARDS, a threshold fraction of tissue with unstable inflation was apparent: >28% in all patients who died and ≤28% in all who survived, whereas segregation of survivors versus non-survivors was not possible based on oxygenation or lung mechanics. CONCLUSIONS: A single set of superimposed inspiratory-expiratory CT scans may predict progression of lung injury and outcome in ARDS; if these preliminary results are validated, this could facilitate clinical trial recruitment and individualised care.

A population level atlas of Mus musculus craniofacial skeleton and automated image-based shape analysis.

Laboratory mice are staples for evo/devo and genetics studies. Inbred strains provide a uniform genetic background to manipulate and understand gene-environment interactions, while their crosses have been instrumental in studies of genetic architecture, integration and modularity, and mapping of complex biological traits. Recently, there have been multiple large-scale studies of laboratory mice to further our understanding of the developmental basis, evolution, and genetic control of shape variation in the craniofacial skeleton (i.e. skull and mandible). These experiments typically use micro-computed tomography (micro-CT) to capture the craniofacial phenotype in 3D and rely on manually annotated anatomical landmarks to conduct statistical shape analysis. Although the common choice for imaging modality and phenotyping provides the potential for collaborative research for even larger studies with more statistical power, the investigator (or lab-specific) nature of the data collection hampers these efforts. Investigators are rightly concerned that subtle differences in how anatomical landmarks were recorded will create systematic bias between studies that will eventually influence scientific findings. Even if researchers are willing to repeat landmark annotation on a combined dataset, different lab practices and software choices may create obstacles for standardization beyond the underlying imaging data. Here, we propose a freely available analysis system that could assist in the standardization of micro-CT studies in the mouse. Our proposal uses best practices developed in biomedical imaging and takes advantage of existing open-source software and imaging formats. Our first contribution is the creation of a synthetic template for the adult mouse craniofacial skeleton from 25 inbred strains and five F1 crosses that are widely used in biological research. The template contains a fully segmented cranium, left and right hemi-mandibles, endocranial space, and the first few cervical vertebrae. We have been using this template in our lab to segment and isolate cranial structures in an automated fashion from a mixed population of mice, including craniofacial mutants, aged 4-12.5 weeks. As a secondary contribution, we demonstrate an application of nearly automated shape analysis, using symmetric diffeomorphic image registration. This approach, which we call diGPA, closely approximates the popular generalized Procrustes analysis (GPA) but negates the collection of anatomical landmarks. We achieve our goals by using the open-source advanced normalization tools (ANT) image quantification library, as well as its associated R library (ANTsR) for statistical image analysis. Finally, we make a plea to investigators to commit to using open imaging standards and software in their labs to the extent possible to increase the potential for data exchange and improve the reproducibility of findings. Future work will incorporate more anatomical detail (such as individual cranial bones, turbinals, dentition, middle ear ossicles) and more diversity into the template.

Clinical correlates of lung ventilation defects in asthmatic children.

BACKGROUND: Lung ventilation defects identified by using hyperpolarized 3-helium gas ((3)He) lung magnetic resonance imaging (MRI) are prevalent in asthmatic patients, but the clinical importance of ventilation defects is poorly understood. OBJECTIVES: We sought to correlate the lung defect volume quantified by using (3)He MRI with clinical features in children with mild and severe asthma. METHODS: Thirty-one children with asthma (median age, 10 years; age range, 3-17 years) underwent detailed characterization and (3)He lung MRI. Quantification of the (3)He signal defined ventilation defect and hypoventilated, ventilated, and well-ventilated volumes. RESULTS: The ventilation defect to total lung volume fraction ranged from 0.1% to 11.6%. Children with ventilation defect percentages in the upper tercile were more likely to have severe asthma than children in the lower terciles (P = .005). The ventilation defect percentage correlated (P < .05 for all) positively with the inhaled corticosteroid dose, total number of controller medications, and total blood eosinophil counts and negatively with the Asthma Control Test score, FEV1 (percent predicted), FEV1/forced vital capacity ratio (percent predicted), and forced expiratory flow rate from 25% to 75% of expired volume (percent predicted). CONCLUSION: The lung defect volume percentage measured by using (3)He MRI correlates with several clinical features of asthma, including severity, symptom score, medication requirement, airway physiology, and atopic markers.

Atlas-based estimation of lung and lobar anatomy in proton MRI.

PURPOSE: To propose an accurate methodological framework for automatically segmenting pulmonary proton MRI based on an optimal consensus of a spatially normalized library of annotated lung atlases. METHODS: A library of 62 manually annotated lung atlases comprising 48 mixed healthy, chronic obstructive pulmonary disease, and asthmatic subjects of a large age range with multiple ventilation levels is used to produce an optimal segmentation in proton MRI, based on a consensus of the spatially normalized library. An extension of this methodology is used to provide best-guess estimates of lobar subdivisions in proton MRI from annotated computed tomography data. RESULTS: A leave-one-out evaluation strategy was used for evaluation. Jaccard overlap measures for the left and right lungs were used for performance comparisons relative to the current state-of-the-art (0.966 ± 0.018 and 0.970 ± 0.016, respectively). Best-guess estimates for the lobes exhibited comparable performance levels (left upper: 0.882 ± 0.059, left lower: 0.868 ± 0.06, right upper: 0.852 ± 0.067, right middle: 0.657 ± 0.130, right lower: 0.873 ± 0.063). CONCLUSION: An annotated atlas library approach can be used to provide good lung and lobe estimation in proton MRI. The proposed framework is useful for subsequent anatomically based analysis of structural and/or functional pulmonary image data. Magn Reson Med 76:315-320, 2016. © 2015 Wiley Periodicals, Inc.

Quantitative structural neuroimaging of mild traumatic brain injury in the Chronic Effects of Neurotrauma Consortium (CENC): Comparison of volumetric data within and across scanners.

BACKGROUND: An important component of the multicentre Chronic Effects of Neurotrauma Consortium (CENC) project is the development of improved quantitative magnetic resonance imaging (MRI) methods, including volumetric analysis. Although many studies routinely employ quality assurance (QA) procedures including MR and human phantoms to promote accuracy and monitor site differences, few studies perform rigorous direct comparisons of these data nor report findings that enable inference regarding site-to-site comparability. These gaps in evaluating cross-site differences are concerning, especially given the well-established differences that can occur between data acquired on scanners with different manufacturer, hardware or software. METHODS: This study reports findings on (1) a series of studies utilizing two MR phantoms to interrogate machine-based variability using data collected on the same magnet, (2) a human phantom repeatedly imaged on the same scanner to investigate within-subject, within-site variability and (3) a human phantom imaged on three different scanners to examine within subject, between-site variability. RESULTS: Although variability is relatively minimal for the phantom scanned on the same magnet, significantly more variability is introduced in a human subject, particularly when regions are relatively small or multiple sites used. CONCLUSION: Vigilance when combining data from different sites is suggested and that future efforts address these issues.

Supervised learning technique for the automated identification of white matter hyperintensities in traumatic brain injury.

BACKGROUND: White matter hyperintensities (WMHs) are foci of abnormal signal intensity in white matter regions seen with magnetic resonance imaging (MRI). WMHs are associated with normal ageing and have shown prognostic value in neurological conditions such as traumatic brain injury (TBI). The impracticality of manually quantifying these lesions limits their clinical utility and motivates the utilization of machine learning techniques for automated segmentation workflows. METHODS: This study develops a concatenated random forest framework with image features for segmenting WMHs in a TBI cohort. The framework is built upon the Advanced Normalization Tools (ANTs) and ANTsR toolkits. MR (3D FLAIR, T2- and T1-weighted) images from 24 service members and veterans scanned in the Chronic Effects of Neurotrauma Consortium's (CENC) observational study were acquired. Manual annotations were employed for both training and evaluation using a leave-one-out strategy. Performance measures include sensitivity, positive predictive value, [Formula: see text] score and relative volume difference. RESULTS: Final average results were: sensitivity = 0.68 ± 0.38, positive predictive value = 0.51 ± 0.40, [Formula: see text] = 0.52 ± 0.36, relative volume difference = 43 ± 26%. In addition, three lesion size ranges are selected to illustrate the variation in performance with lesion size. CONCLUSION: Paired with correlative outcome data, supervised learning methods may allow for identification of imaging features predictive of diagnosis and prognosis in individual TBI patients.

Rapid acquisition of helium-3 and proton three-dimensional image sets of the human lung in a single breath-hold using compressed sensing.

PURPOSE: To develop and validate a method for acquiring helium-3 ((3) He) and proton ((1) H) three-dimensional (3D) image sets of the human lung with isotropic spatial resolution within a 10-s breath-hold by using compressed sensing (CS) acceleration, and to assess the fidelity of undersampled images compared with fully sampled images. METHODS: The undersampling scheme for CS acceleration was optimized and tested using (3) He ventilation data. Rapid 3D acquisition of both (3) He and (1) H data during one breath-hold was then implemented, based on a balanced steady-state free-precession pulse sequence, by random undersampling of k-space with reconstruction by means of minimizing the L1 norm and total variance. CS-reconstruction fidelity was evaluated quantitatively by comparing fully sampled and retrospectively undersampled image sets. RESULTS: Helium-3 and (1) H 3D image sets of the lung with isotropic 3.9-mm resolution were acquired during a single breath-hold in 12 s and 8 s using acceleration factors of 2 and 3, respectively. Comparison of fully sampled and retrospectively undersampled (3) He and (1) H images yielded mean absolute errors <10% and structural similarity indices >0.9. CONCLUSION: By randomly undersampling k-space and using CS reconstruction, high-quality (3) He and (1) H 3D image sets with isotropic 3.9-mm resolution can be acquired within an 8-s breath-hold.