Brain Segmentation From Computed Tomography of Healthy Aging and Geriatric Concussion at Variable Spatial Resolutions

BounTI (boundary-preserving threshold iteration): A user-friendly tool for automatic hard tissue segmentation

X-ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub-parts and can be a time-consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open-access and user-friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High-resolution microCT images resulted in more accurate boundary-preserved segmentation, nonetheless significantly lower-quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add-on, a stand-alone Windows executable, and a Python library. We believe this accessible and user-friendly segmentation tool can benefit the wider anatomical community.

Increases in regional brain volume across two native South American male populations

Industrialized environments, despite benefits such as higher levels of formal education and lower rates of infections, can also have pernicious impacts upon brain atrophy. Partly for this reason, comparing age-related brain volume trajectories between industrialized and non-industrialized populations can help to suggest lifestyle correlates of brain health. The Tsimane, indigenous to the Bolivian Amazon, derive their subsistence from foraging and horticulture and are physically active. The Moseten, a mixed-ethnicity farming population, are physically active but less than the Tsimane. Within both populations (N = 1024; age range = 46-83), we calculated regional brain volumes from computed tomography and compared their cross-sectional trends with age to those of UK Biobank (UKBB) participants (N = 19,973; same age range). Surprisingly among Tsimane and Moseten (T/M) males, some parietal and occipital structures mediating visuospatial abilities exhibit small but significant increases in regional volume with age. UKBB males exhibit a steeper negative trend of regional volume with age in frontal and temporal structures compared to T/M males. However, T/M females exhibit significantly steeper rates of brain volume decrease with age compared to UKBB females, particularly for some cerebro-cortical structures (e.g., left subparietal cortex). Across the three populations, observed trends exhibit no interhemispheric asymmetry. In conclusion, the age-related rate of regional brain volume change may differ by lifestyle and sex. The lack of brain volume reduction with age is not known to exist in other human population, highlighting the putative role of lifestyle in constraining regional brain atrophy and promoting elements of non-industrialized lifestyle like higher physical activity.

Rapidly progressive cerebral atrophy following a posterior cranial fossa stroke: Assessment with semiautomatic CT volumetry

BACKGROUND

The effect of posterior cranial fossa stroke on changes in cerebral volume is not known. We assessed cerebral volume changes in patients with acute posterior fossa stroke using CT scans, and looked for risk factors for cerebral atrophy.

METHODS

Patients with cerebellar or brainstem hemorrhage/infarction admitted to the ICU, and who underwent at least two subsequent inpatient head CT scans during hospitalization were included (n = 60). The cerebral volume was estimated using an automatic segmentation method. Patients with cerebral volume reduction > 0% from the first to the last scan were defined as the "cerebral atrophy group (n = 47)," and those with ≤ 0% were defined as the "no cerebral atrophy group (n = 13)."

RESULTS

The cerebral atrophy group showed a significant decrease in cerebral volume (first CT scan: 0.974 ± 0.109 L vs. last CT scan: 0.927 ± 0.104 L, P < 0.001). The mean percentage change in cerebral volume between CT scans in the cerebral atrophy group was -4.7%, equivalent to a cerebral volume of 46.8 cm³, over a median of 17 days. The proportions of cases with a history of hypertension, diabetes mellitus, and median time on mechanical ventilation were significantly higher in the cerebral atrophy group than in the no cerebral atrophy group.

CONCLUSIONS

Many ICU patients with posterior cranial fossa stroke showed signs of cerebral atrophy. Those with rapidly progressive cerebral atrophy were more likely to have a history of hypertension or diabetes mellitus and required prolonged ventilation.

Mapping spreading depolarisations after traumatic brain injury: a pilot clinical study protocol

INTRODUCTION

Cortical spreading depolarisation (CSD) is characterised by a near-complete loss of the ionic membrane potential of cortical neurons and glia propagating across the cerebral cortex, which generates a transient suppression of spontaneous neuronal activity. CSDs have become a recognised phenomenon that imparts ongoing secondary insults after brain injury. Studies delineating CSD generation and propagation in humans after traumatic brain injury (TBI) are lacking. Therefore, this study aims to determine the feasibility of using a multistrip electrode array to identify CSDs and characterise their propagation in space and time after TBI.

METHODS AND ANALYSIS

This pilot, prospective observational study will enrol patients with TBI requiring therapeutic craniotomy or craniectomy. Subdural electrodes will be placed for continuous electrocorticography monitoring for seizures and CSDs as a research procedure, with surrogate informed consent obtained preoperatively. The propagation of CSDs relative to structural brain pathology will be mapped using reconstructed CT and electrophysiological cross-correlations. The novel use of multiple subdural strip electrodes in conjunction with brain morphometric segmentation is hypothesised to provide sufficient spatial information to characterise CSD propagation across the cerebral cortex and identify cortical foci giving rise to CSDs.

ETHICS AND DISSEMINATION

Ethical approval for the study was obtained from the Hennepin Healthcare Research Institute's ethics committee, HSR 17-4400, 25 October 2017 to present. Study findings will be submitted for publication in peer-reviewed journals and presented at scientific conferences.

TRIAL REGISTRATION NUMBER

NCT03321370.

Deep learning-based automated segmentation of eight brain anatomical regions using head CT images in PET/CT

Objective

We aim to propose a deep learning-based method of automated segmentation of eight brain anatomical regions in head computed tomography (CT) images obtained during positron emission tomography/computed tomography (PET/CT) scans. The brain regions include basal ganglia, cerebellum, hemisphere, and hippocampus, all split into left and right.

Materials and methods

We enrolled patients who underwent both PET/CT imaging (with an extra head CT scan) and magnetic resonance imaging (MRI). The segmentation of eight brain regions in CT was achieved by using convolutional neural networks (CNNs): DenseVNet and 3D U-Net. The same segmentation task in MRI was performed by using BrainSuite13, which was a public atlas label method. The mean Dice scores were used to assess the performance of the CNNs. Then, the agreement and correlation of the volumes of the eight segmented brain regions between CT and MRI methods were analyzed.

Results

18 patients were enrolled. Four of the eight brain regions obtained high mean Dice scores (> 0.90): left (0.978) and right (0.912) basal ganglia and left (0.945) and right (0.960) hemisphere. Regarding the agreement and correlation of the brain region volumes between two methods, moderate agreements were observed on the left (ICC: 0.618, 95% CI 0.242, 0.835) and right (ICC: 0.654, 95% CI 0.298, 0.853) hemisphere. Poor agreements were observed on the other regions. A moderate correlation was observed on the right hemisphere (Spearman’s rho 0.68, p = 0.0019). Lower correlations were observed on the other regions.

Conclusions

The proposed deep learning-based method performed automated segmentation of eight brain anatomical regions on head CT imaging in PET/CT. Some regions obtained high mean Dice scores and the agreement and correlation results of the segmented region volumes between two methods were moderate to poor.

A Comparison of Global Brain Volumetrics Obtained from CT versus MRI Using 2 Publicly Available Software Packages

BACKGROUND AND PURPOSE

Brain volumetrics have historically been obtained from MR imaging data. However, advances in CT, along with refined publicly available software packages, may support tissue-level segmentations of clinical CT images. Here, brain volumetrics obtained by applying two publicly available software packages to paired CT-MR data are compared.

MATERIALS AND METHODS

In a group of patients (n = 69; 35 men) who underwent both MR imaging and CT brain scans within 12 months of one another, brain tissue was segmented into WM, GM, and CSF compartments using 2 publicly available software packages: Statistical Parametric Mapping and FMRIB Software Library. A subset of patients with repeat imaging sessions was used to assess the repeatability of each segmentation. Regression analysis and Bland-Altman limits of agreement were used to determine the level of agreement between segmented volumes.

RESULTS

Regression analysis showed good agreement between volumes derived from MR images versus those from CT. The correlation coefficients between the 2 methods were 0.93 and 0.98 for Statistical Parametric Mapping and FMRIB Software Library, respectively. Differences between global volumes were significant (P < .05) for all volumes compared within a given segmentation pipeline. WM bias was 36% (SD, 38%) and 18% (SD, 18%) for Statistical Parametric Mapping and FMRIB Software Library, respectively, and 10% (SD, 30%) and 6% (SD, 20%) for GM (bias ± limits of agreement), with CT overestimating WM and underestimating GM compared with MR imaging. Repeatability was good for all segmentations, with coefficients of variation of <10% for all volumes.

CONCLUSIONS

The repeatability of CT segmentations using publicly available software is good, with good correlation with MR imaging. With careful study design and acknowledgment of measurement biases, CT may be a viable alternative to MR imaging in certain settings.

Rapidly progressive brain atrophy in septic ICU patients: a retrospective descriptive study using semiautomatic CT volumetry

Background

Sepsis is often associated with multiple organ failure; however, changes in brain volume with sepsis are not well understood. We assessed brain atrophy in the acute phase of sepsis using brain computed tomography (CT) scans, and their findings’ relationship to risk factors and outcomes.

Methods

Patients with sepsis admitted to an intensive care unit (ICU) and who underwent at least two head CT scans during hospitalization were included (n = 48). The first brain CT scan was routinely performed on admission, and the second and further brain CT scans were obtained whenever prolonged disturbance of consciousness or abnormal neurological findings were observed. Brain volume was estimated using an automatic segmentation method and any changes in brain volume between the two scans were recorded. Patients with a brain volume change < 0% from the first CT scan to the second CT scan were defined as the “brain atrophy group (n = 42)”, and those with ≥ 0% were defined as the “no brain atrophy group (n = 6).” Use and duration of mechanical ventilation, length of ICU stay, length of hospital stay, and mortality were compared between the groups.

Results

Analysis of all 42 cases in the brain atrophy group showed a significant decrease in brain volume (first CT scan: 1.041 ± 0.123 L vs. second CT scan: 1.002 ± 0.121 L, t (41) = 9.436, p < 0.001). The mean percentage change in brain volume between CT scans in the brain atrophy group was –3.7% over a median of 31 days, which is equivalent to a brain volume of 38.5 cm³. The proportion of cases on mechanical ventilation (95.2% vs. 66.7%; p = 0.02) and median time on mechanical ventilation (28 [IQR 15–57] days vs. 15 [IQR 0–25] days, p = 0.04) were significantly higher in the brain atrophy group than in the no brain atrophy group.

Conclusions

Many ICU patients with severe sepsis who developed prolonged mental status changes and neurological sequelae showed signs of brain atrophy. Patients with rapidly progressive brain atrophy were more likely to have required mechanical ventilation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03828-7.

Multi-Modal Segmentation of 3D Brain Scans Using Neural Networks

Anatomical segmentation of brain scans is highly relevant for diagnostics and neuroradiology research. Conventionally, segmentation is performed on T₁-weighted MRI scans, due to the strong soft-tissue contrast. In this work, we report on a comparative study of automated, learning-based brain segmentation on various other contrasts of MRI and also computed tomography (CT) scans and investigate the anatomical soft-tissue information contained in these imaging modalities. A large database of in total 853 MRI/CT brain scans enables us to train convolutional neural networks (CNNs) for segmentation. We benchmark the CNN performance on four different imaging modalities and 27 anatomical substructures. For each modality we train a separate CNN based on a common architecture. We find average Dice scores of 86.7 ± 4.1% (T₁-weighted MRI), 81.9 ± 6.7% (fluid-attenuated inversion recovery MRI), 80.8 ± 6.6% (diffusion-weighted MRI) and 80.7 ± 8.2% (CT), respectively. The performance is assessed relative to labels obtained using the widely-adopted FreeSurfer software package. The segmentation pipeline uses dropout sampling to identify corrupted input scans or low-quality segmentations. Full segmentation of 3D volumes with more than 2 million voxels requires <1s of processing time on a graphical processing unit.

Cross-Sectional Volumes and Trajectories of the Human Brain, Gray Matter, White Matter and Cerebrospinal Fluid in 9473 Typically Aging Adults

Accurate knowledge of adult human brain volume (BV) is critical for studies of aging- and disease-related brain alterations, and for monitoring the trajectories of neural and cognitive functions in conditions like Alzheimer's disease and traumatic brain injury. This scoping meta-analysis aggregates normative reference values for BV and three related volumetrics-gray matter volume (GMV), white matter volume (WMV) and cerebrospinal fluid volume (CSFV)-from typically-aging adults studied cross-sectionally using magnetic resonance imaging (MRI). Drawing from an aggregate sample of 9473 adults, this study provides (A) regression coefficients β describing the age-dependent trajectories of volumetric measures by sex within the range from 20 to 70 years based on both linear and quadratic models, and (B) average values for BV, GMV, WMV and CSFV at the representative ages of 20 (young age), 45 (middle age) and 70 (old age). The results provided synthesize ~20 years of brain volumetrics research and allow one to estimate BV at any age between 20 and 70. Importantly, however, such estimates should be used and interpreted with caution because they depend on MRI hardware specifications (e.g. scanner manufacturer, magnetic field strength), data acquisition parameters (e.g. spatial resolution, weighting), and brain segmentation algorithms. Guidelines are proposed to facilitate future meta- and mega-analyses of brain volumetrics.

The Indigenous South American Tsimane Exhibit Relatively Modest Decrease in Brain Volume With Age Despite High Systemic Inflammation

Brain atrophy is correlated with risk of cognitive impairment, functional decline, and dementia. Despite a high infectious disease burden, Tsimane forager-horticulturists of Bolivia have the lowest prevalence of coronary atherosclerosis of any studied population and present few cardiovascular disease (CVD) risk factors despite a high burden of infections and therefore inflammation. This study (a) examines the statistical association between brain volume (BV) and age for Tsimane and (b) compares this association to that of 3 industrialized populations in the United States and Europe. This cohort-based panel study enrolled 746 participants aged 40-94 (396 males), from whom computed tomography (CT) head scans were acquired. BV and intracranial volume (ICV) were calculated from automatic head CT segmentations. The linear regression coefficient estimate β^T of the Tsimane (T), describing the relationship between age (predictor) and BV (response, as a percentage of ICV), was calculated for the pooled sample (including both sexes) and for each sex. β^T was compared to the corresponding regression coefficient estimate β^R of samples from the industrialized reference (R) countries. For all comparisons, the null hypothesis β T = β R was rejected both for the combined samples of males and females, as well as separately for each sex. Our results indicate that the Tsimane exhibit a significantly slower decrease in BV with age than populations in the United States and Europe. Such reduced rates of BV decrease, together with a subsistence lifestyle and low CVD risk, may protect brain health despite considerable chronic inflammation related to infectious burden.

Acute cognitive impairment after traumatic brain injury predicts the occurrence of brain atrophy patterns similar to those observed in Alzheimer's disease

Traumatic brain injuries (TBIs) are often followed by persistent structural brain alterations and by cognitive sequalae, including memory deficits, reduced neural processing speed, impaired social function, and decision-making difficulties. Although mild TBI (mTBI) is a risk factor for Alzheimer's disease (AD), the extent to which these conditions share patterns of macroscale neurodegeneration has not been quantified. Comparing such patterns can not only reveal how the neurodegenerative trajectories of TBI and AD are similar, but may also identify brain atrophy features which can be leveraged to prognosticate AD risk after TBI. The primary aim of this study is to systematically map how TBI affects white matter (WM) and gray matter (GM) properties in AD-analogous patterns. Our findings identify substantial similarities in the regional macroscale neurodegeneration patterns associated with mTBI and AD. In cerebral GM, such similarities are most extensive in brain areas involved in memory and executive function, such as the temporal poles and orbitofrontal cortices, respectively. Our results indicate that the spatial pattern of cerebral WM degradation observed in AD is broadly similar to the pattern of diffuse axonal injury observed in TBI, which frequently affects WM structures like the fornix, corpus callosum, and corona radiata. Using machine learning, we find that the severity of AD-like brain changes observed during the chronic stage of mTBI can be accurately prognosticated based on acute assessments of post-traumatic mild cognitive impairment. These findings suggest that acute post-traumatic cognitive impairment predicts the magnitude of AD-like brain atrophy, which is itself associated with AD risk.

Toward a global and reproducible science for brain imaging in neurotrauma: the ENIGMA adult moderate/severe traumatic brain injury working group

The global burden of mortality and morbidity caused by traumatic brain injury (TBI) is significant, and the heterogeneity of TBI patients and the relatively small sample sizes of most current neuroimaging studies is a major challenge for scientific advances and clinical translation. The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Adult moderate/severe TBI (AMS-TBI) working group aims to be a driving force for new discoveries in AMS-TBI by providing researchers world-wide with an effective framework and platform for large-scale cross-border collaboration and data sharing. Based on the principles of transparency, rigor, reproducibility and collaboration, we will facilitate the development and dissemination of multiscale and big data analysis pipelines for harmonized analyses in AMS-TBI using structural and functional neuroimaging in combination with non-imaging biomarkers, genetics, as well as clinical and behavioral measures. Ultimately, we will offer investigators an unprecedented opportunity to test important hypotheses about recovery and morbidity in AMS-TBI by taking advantage of our robust methods for large-scale neuroimaging data analysis. In this consensus statement we outline the working group's short-term, intermediate, and long-term goals.

Are endocasts reliable proxies for brains? A 3D quantitative comparison of the extant human brain and endocast

Endocasts (i.e., replicas of the inner surface of the bony braincase) constitute a critical proxy for qualifying and quantifying variations in brain shape and organization in extinct taxa. In the absence of brain tissues preserved in the fossil record, endocasts provide the only direct evidence of brain evolution. However, debates on whether or not information inferred from the study of endocasts reflects brain shape and organization have polarized discussions in paleoneurology since the earliest descriptions of cerebral imprints in fossil hominin crania. By means of imaging techniques (i.e., MRIs and CT scans) and 3D modelling methods (i.e., surface-based comparisons), we collected consistent morphological (i.e., shape) and structural (i.e., sulci) information on the variation patterns between the brain and the endocast based on a sample of extant human individuals (N = 5) from the 3D clinical image database of the Steve Biko Academic Hospital in Pretoria (South Africa) and the Hôpitaux Universitaires Pitié Salpêtrière in Paris (France). Surfaces of the brain and endocast of the same individual were segmented from the 3D MRIs and CT images, respectively. Sulcal imprints were automatically detected. We performed a deformation-based shape analysis to compare both the shape and the sulcal pattern of the brain and the endocast. We demonstrated that there is close correspondence in terms of morphology and organization between the brain and the corresponding endocast with the exception of the superior region. By comparatively quantifying the shape and organization of the brain and endocast, this work represents an important reference for paleoneurological studies.

Fully Automated Segmentation of Head CT Neuroanatomy Using Deep Learning

Purpose

To develop a deep learning model that segments intracranial structures on head CT scans.

Materials and Methods

In this retrospective study, a primary dataset containing 62 normal noncontrast head CT scans from 62 patients (mean age, 73 years; age range, 27-95 years) acquired between August and December 2018 was used for model development. Eleven intracranial structures were manually annotated on the axial oblique series. The dataset was split into 40 scans for training, 10 for validation, and 12 for testing. After initial training, eight model configurations were evaluated on the validation dataset and the highest performing model was evaluated on the test dataset. Interobserver variability was reported using multirater consensus labels obtained from the test dataset. To ensure that the model learned generalizable features, it was further evaluated on two secondary datasets containing 12 volumes with idiopathic normal pressure hydrocephalus (iNPH) and 30 normal volumes from a publicly available source. Statistical significance was determined using categorical linear regression with P < .05.

Results

Overall Dice coefficient on the primary test dataset was 0.84 ± 0.05 (standard deviation). Performance ranged from 0.96 ± 0.01 (brainstem and cerebrum) to 0.74 ± 0.06 (internal capsule). Dice coefficients were comparable to expert annotations and exceeded those of existing segmentation methods. The model remained robust on external CT scans and scans demonstrating ventricular enlargement. The use of within-network normalization and class weighting facilitated learning of underrepresented classes.

Conclusion

Automated segmentation of CT neuroanatomy is feasible with a high degree of accuracy. The model generalized to external CT scans as well as scans demonstrating iNPH.Supplemental material is available for this article.© RSNA, 2020.

Neuroimaging and Psychometric Assessment of Mild Cognitive Impairment After Traumatic Brain Injury

Traumatic brain injury (TBI) can be serious partly due to the challenges of assessing and treating its neurocognitive and affective sequelae. The effects of a single TBI may persist for years and can limit patients’ activities due to somatic complaints (headaches, vertigo, sleep disturbances, nausea, light or sound sensitivity), affective sequelae (post-traumatic depressive symptoms, anxiety, irritability, emotional instability) and mild cognitive impairment (MCI, including social cognition disturbances, attention deficits, information processing speed decreases, memory degradation and executive dysfunction). Despite a growing amount of research, study comparison and knowledge synthesis in this field are problematic due to TBI heterogeneity and factors like injury mechanism, age at or time since injury. The relative lack of standardization in neuropsychological assessment strategies for quantifying sequelae adds to these challenges, and the proper administration of neuropsychological testing relative to the relationship between TBI, MCI and neuroimaging has not been reviewed satisfactorily. Social cognition impairments after TBI (e.g., disturbed emotion recognition, theory of mind impairment, altered self-awareness) and their neuroimaging correlates have not been explored thoroughly. This review consolidates recent findings on the cognitive and affective consequences of TBI in relation to neuropsychological testing strategies, to neurobiological and neuroimaging correlates, and to patient age at and assessment time after injury. All cognitive domains recognized by the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) are reviewed, including social cognition, complex attention, learning and memory, executive function, language and perceptual-motor function. Affect and effort are additionally discussed owing to their relationships to cognition and to their potentially confounding effects. Our findings highlight non-negligible cognitive and affective impairments following TBI, their gravity often increasing with injury severity. Future research should study (A) language, executive and perceptual-motor function (whose evolution post-TBI remains under-explored), (B) the effects of age at and time since injury, and (C) cognitive impairment severity as a function of injury severity. Such efforts should aim to develop and standardize batteries for cognitive subdomains—rather than only domains—with high ecological validity. Additionally, they should utilize multivariate techniques like factor analysis and related methods to clarify which cognitive subdomains or components are indeed measured by standardized tests.

Creation of an anthropomorphic CT head phantom for verification of image segmentation

Purpose

Many methods are available to segment structural magnetic resonance (MR) images of the brain into different tissue types. These have generally been developed for research purposes but there is some clinical use in the diagnosis of neurodegenerative diseases such as dementia. The potential exists for computed tomography (CT) segmentation to be used in place of MRI segmentation, but this will require a method to verify the accuracy of CT processing, particularly if algorithms developed for MR are used, as MR has notably greater tissue contrast.

Methods

To investigate these issues we have created a three‐dimensional (3D) printed brain with realistic Hounsfield unit (HU) values based on tissue maps segmented directly from an individual T1 MRI scan of a normal subject. Several T1 MRI scans of normal subjects from the ADNI database were segmented using SPM12 and used to create stereolithography files of different tissues for 3D printing. The attenuation properties of several material blends were investigated, and three suitable formulations were used to print an object expected to have realistic geometry and attenuation properties. A skull was simulated by coating the object with plaster of Paris impregnated bandages. Using two CT scanners, the realism of the phantom was assessed by the measurement of HU values, SPM12 segmentation and comparison with the source data used to create the phantom.

Results

Realistic relative HU values were measured although a subtraction of 60 was required to obtain equivalence with the expected values (gray matter 32.9–35.8 phantom, 29.9–34.2 literature). Segmentation of images acquired at different kVps/mAs showed excellent agreement with the source data (Dice Similarity Coefficient 0.79 for gray matter). The performance of two scanners with two segmentation methods was compared, with the scanners found to have similar performance and with one segmentation method clearly superior to the other.

Conclusion

The ability to use 3D printing to create a realistic (in terms of geometry and attenuation properties) head phantom has been demonstrated and used in an initial assessment of CT segmentation accuracy using freely available software developed for MRI.

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease

BACKGROUND AND PURPOSE

Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume.

MATERIALS AND METHODS

CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive.

RESULTS

CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P < .001) for total intracranial volume and 0.94 (P < .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P < .001) as well as a group difference between patients with Alzheimer disease and controls (P < .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable.

CONCLUSIONS

In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies.

Multimodal Imaging of Cerebral Microhemorrhages and White Matter Degradation in Geriatric Patients with Mild Traumatic Brain Injury

Traditionally, neurobiologists have utilized microscale techniques of scientific investigation to uncover the fundamental organization and function of brain cells and neuronal ensembles. In recent decades, however, macroscale brain imaging methods like magnetic resonance imaging (MRI) and computed tomography (CT) have facilitated a wider scope of understanding neural structure and function across the lifespan. Thanks to such methods, a broader picture of the relationship between microscale processes-studied by neurobiologists-and macroscale observations-made by clinicians-has emerged. More recently, the vascular component of neurodegeneration has come under renewed scrutiny partly due to increased appreciation of the relationship between neurovascular injury, cardiovascular disease, and senescence. Cerebral microbleeds (CMBs) are among the smallest lesions of the cerebrum which can be visualized using MRI to indicate blood-brain barrier (BBB) impairment; as such, this class of hemorrhages are important for the evaluation and macroscale detection of geriatric patients' microscale pathologies associated with neurovascular disease and/or neurodegeneration. This chapter details a streamlined protocol for MRI/CT multimodal imaging data acquisition, archiving and digital processing, including methods tailored for the analysis of susceptibility-weighted imaging (SWI) and diffusion-weighted imaging (DWI) scans to reveal CMB-related alterations of the human connectome. Efficient and effective MRI/CT methods like ours, when tailored for CMB and connectome analysis, are essential for future progress in this important field of scientific inquiry.