Clinical use of brain volumetry

Image Based Brain Segmentation: From Multi-Atlas Fusion to Deep Learning

Background:

This review aims to identify the development of the algorithms for brain tissue and structure segmentation in MRI images.

Discussion:

Starting from the results of the Grand Challenges on brain tissue and structure segmentation held in Medical Image Computing and Computer-Assisted Intervention (MICCAI), this review analyses the development of the algorithms and discusses the tendency from multi-atlas label fusion to deep learning. The intrinsic characteristics of the winners’ algorithms on the Grand Challenges from the year 2012 to 2018 are analyzed and the results are compared carefully.

Conclusion:

Although deep learning has got higher rankings in the challenge, it has not yet met the expectations in terms of accuracy. More effective and specialized work should be done in the future.

Lifespan normative data on rates of brain volume changes

We provide here normative values of yearly percentage brain volume change (PBVC/y) as obtained with Structural Imaging Evaluation, using Normalization, of Atrophy, a widely used open-source software, developing a PBVC/y calculator for assessing the deviation from the expected PBVC/y in patients with neurological disorders. We assessed multicenter (34 centers, 11 acquisition protocols) magnetic resonance imaging data of 720 healthy participants covering the whole adult lifespan (16-90 years). Data of 421 participants with a follow-up > 6 months were used to obtain the normative values for PBVC/y and data of 392 participants with a follow-up <1 month were selected to assess the intrasubject variability of the brain volume measurement. A mixed model evaluated PBVC/y dependence on age, sex, and magnetic resonance imaging parameters (scan vendor and magnetic field strength). PBVC/y was associated with age (p < 0.001), with 60- to 70-year-old participants showing twice more volume decrease than participants aged 30-40 years. PBVC/y was also associated with magnetic field strength, with higher decreases when measured by 1.5T than 3T scanners (p < 0.001). The variability of PBVC/y normative percentiles was narrower as the interscan interval was longer (e.g., 80th normative percentile was 50% smaller for participants with 2-year than with 1-year follow-up). The use of these normative data, eased by the freely available calculator, might help in better discriminating pathological from physiological conditions in the clinical setting.

Brain Edema in Chronic Hepatic Encephalopathy

Brain edema is a common feature associated with hepatic encephalopathy (HE). In patients with acute HE, brain edema has been shown to play a crucial role in the associated neurological deterioration. In chronic HE, advanced magnetic resonance imaging (MRI) techniques have demonstrated that low-grade brain edema appears also to be an important pathological feature. This review explores the different methods used to measure brain edema ex vivo and in vivo in animal models and in humans with chronic HE. In addition, an in-depth description of the main studies performed to date is provided. The role of brain edema in the neurological alterations linked to HE and whether HE and brain edema are the manifestations of the same pathophysiological mechanism or two different cerebral manifestations of brain dysfunction in liver disease are still under debate. In vivo MRI/magnetic resonance spectroscopy studies have allowed insight into the development of brain edema in chronic HE. However, additional in vivo longitudinal and multiparametric/multimodal studies are required (in humans and animal models) to elucidate the relationship between liver function, brain metabolic changes, cellular changes, cell swelling, and neurological manifestations in chronic HE.

Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review

Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.

Comparison of electroencephalographic findings with hippocampal magnetic resonance imaging volumetry in dogs with idiopathic epilepsy

BACKGROUND

In humans, temporal lobe epilepsy (TLE), is a type of focal epilepsy occurring mainly in the mesial TLE (mTLE), commonly associated with hippocampal sclerosis (HS).

OBJECTIVES

According to recent studies, TLE might also occur in dogs and could be associated with hippocampal atrophy (HA)/HS. To date, hippocampal lesions have not been correlated with electroencephalographic (EEG) findings in epileptic dogs.

ANIMALS

An EEG examination, brain magnetic resonance imaging, and volumetric assessment of the hippocampus were performed in 16 nonepileptic and 41 epileptic dogs.

METHODS

In this retrospective study, the presence and localization of EEG-defined epileptiform discharges (EDs) was blindly evaluated. The hippocampus was measured and assessed for unilateral atrophy. The results of EEG and volumetric findings were correlated to determine whether the functional epileptic focus is equivalent to structural changes.

RESULTS

The median hippocampal asymmetric ratio (AR) in epileptic dogs was significantly greater than in the control group (P < .001). Using a cut-off threshold AR of >6%, 56% (23/41) of the dogs were characterized with unilateral HA. Of those animals, 35% (8/23) had EDs in the temporal leads and 26% (6/23) had no EDs. In 88% (7/8) of dogs with EDs in the temporal leads that had unilateral HA, the EDs correlated with the side of the decreased hippocampal volume.

CONCLUSIONS AND CLINICAL IMPORTANCE

The results indicate an association between the presence of EDs detectable on EEG and a decrease in the unilateral hippocampal volume in some cases of canine idiopathic epilepsy that might reflect features of human mTLE.

Analysis of conversion of Alzheimer’s disease using a multi-state Markov model

With rapid aging of world population, Alzheimer’s disease is becoming a leading cause of death after cardiovascular disease and cancer. Nearly 10% of people who are over 65 years old are affected by Alzheimer’s disease. The causes have been studied intensively, but no definitive answer has been found. Genetic predisposition, abnormal protein deposits in brain, and environmental factors are suspected to play a role in the development of this disease. In this paper, we model progression of Alzheimer’s disease using a multi-state Markov model to investigate the significance of known risk factors such as age, apolipoprotein E4, and some brain structural volumetric variables from magnetic resonance imaging scans (e.g., hippocampus, etc.) while predicting transitions between different clinical diagnosis states. With the Alzheimer’s Disease Neuroimaging Initiative data, we found that the model with age is not significant (p = 0.1733) according to the likelihood ratio test, but the apolipoprotein E4 is a significant risk factor, and the examination of apolipoprotein E4-by-sex interaction suggests that the apolipoprotein E4 link to Alzheimer’s disease is stronger in women. Given the estimated transition probabilities, the prediction accuracy is as high as 0.7849.

Fast CSF MRI for brain segmentation; Cross-validation by comparison with 3D T1-based brain segmentation methods

OBJECTIVE

In previous work we have developed a fast sequence that focusses on cerebrospinal fluid (CSF) based on the long T2 of CSF. By processing the data obtained with this CSF MRI sequence, brain parenchymal volume (BPV) and intracranial volume (ICV) can be automatically obtained. The aim of this study was to assess the precision of the BPV and ICV measurements of the CSF MRI sequence and to validate the CSF MRI sequence by comparison with 3D T1-based brain segmentation methods.

MATERIALS AND METHODS

Ten healthy volunteers (2 females; median age 28 years) were scanned (3T MRI) twice with repositioning in between. The scan protocol consisted of a low resolution (LR) CSF sequence (0:57min), a high resolution (HR) CSF sequence (3:21min) and a 3D T1-weighted sequence (6:47min). Data of the HR 3D-T1-weighted images were downsampled to obtain LR T1-weighted images (reconstructed imaging time: 1:59 min). Data of the CSF MRI sequences was automatically segmented using in-house software. The 3D T1-weighted images were segmented using FSL (5.0), SPM12 and FreeSurfer (5.3.0).

RESULTS

The mean absolute differences for BPV and ICV between the first and second scan for CSF LR (BPV/ICV: 12±9/7±4cc) and CSF HR (5±5/4±2cc) were comparable to FSL HR (9±11/19±23cc), FSL LR (7±4, 6±5cc), FreeSurfer HR (5±3/14±8cc), FreeSurfer LR (9±8, 12±10cc), and SPM HR (5±3/4±7cc), and SPM LR (5±4, 5±3cc). The correlation between the measured volumes of the CSF sequences and that measured by FSL, FreeSurfer and SPM HR and LR was very good (all Pearson's correlation coefficients >0.83, R2 .67-.97). The results from the downsampled data and the high-resolution data were similar.

CONCLUSION

Both CSF MRI sequences have a precision comparable to, and a very good correlation with established 3D T1-based automated segmentations methods for the segmentation of BPV and ICV. However, the short imaging time of the fast CSF MRI sequence is superior to the 3D T1 sequence on which segmentation with established methods is performed.

Autoimmune comorbidities in multiple sclerosis: what is the influence on brain volumes? A case-control MRI study

BACKGROUND

Several studies indicated that multiple sclerosis (MS) is frequently associated with other autoimmune diseases. However, it is little known if the coexistence of these conditions may influence the radiologic features of MS, and in particular the brain volumes.

OBJECTIVES

To evaluate the effect of autoimmune comorbidities on brain atrophy in a large case-control MS population.

METHODS

A group of MS patients affected by a second autoimmune disorder, and a control MS group without any comorbidity, were recruited. Patients underwent a brain MRI and volumes of whole brain (WB), white matter (WM), and gray matter (GM) with cortical GM were estimated by SIENAX.

RESULTS

The sample included 286 MS patients, of which 30 (10.5%) subjects with type 1 diabetes (T1D), 53 (18.5%) with autoimmune thyroiditis (AT) and 4 (0.1%) with celiac disease. Multiple regression analysis found an association between T1D and lower GM (p = 0.038) and cortical GM (p = 0.036) volumes, independent from MS clinical features and related to T1D duration (p < 0.01), while no association was observed with AT and celiac disease.

CONCLUSIONS

Our data support the importance of considering T1D as possible factors influencing the brain atrophy in MS. Further studies are needed to confirm our data and to clarify the underlying mechanisms.

SIENA-XL for improving the assessment of gray and white matter volume changes on brain MRI

In this article, SIENA-XL, a new segmentation-based longitudinal pipeline is introduced, for: (i) increasing the precision of longitudinal volume change estimation for white (WM) and gray (GM) matter separately, compared with cross-sectional segmentation methods such as SIENAX; and (ii) avoiding potential biases in registration-based methods when Jacobians are used, with a smoothing extent larger than spatial scale between tissue-interfaces, which is where atrophy usually occurs. SIENA-XL implements a new brain extraction procedure and a multi-time-point intensity equalization step before performing the final segmentation that also includes separate segmentation of deep GM structures by using FMRIB's Integrated Registration and Segmentation Tool. The detection of GM and WM volume changes with SIENA-XL was evaluated using different healthy control (HC) and multiple sclerosis (MS) MRI datasets and compared with the traditional SIENAX and two Jacobian-based approaches, SPM12 and SIENAX-JI (a version of SIENAX including Jacobian integration - JI). In scan-rescan data from HCs, SIENA-XL showed: (i) a significant decrease in error, of 50-70% when compared with SIENAX; (ii) no significant differences in error when compared with SIENAX-JI and SPM12 in a scan-rescan HC dataset that included repositioning. When tested in a HC dataset with scan-rescan both at baseline and after 1 year of follow-up, SIENA-XL showed: (i) significantly higher precision (P < 0.01) than SIENAX; (ii) no significant differences to SIENAX-JI and SPM12. Finally, in a dataset of 79 MS patients with a 2 years follow-up, SIENA-XL showed a substantial reduction of sample size, by comparison with SIENAX, SIENAX-JI, and SPM12, for detecting treatment effects of 25, 30, and 50%. Hum Brain Mapp 39:1063-1077, 2018. © 2017 Wiley Periodicals, Inc.

Reference-free error estimation for multiple measurement methods

We present a computational framework to select the most accurate and precise method of measurement of a certain quantity, when there is no access to the true value of the measurand. A typical use case is when several image analysis methods are applied to measure the value of a particular quantitative imaging biomarker from the same images. The accuracy of each measurement method is characterized by systematic error (bias), which is modeled as a polynomial in true values of measurand, and the precision as random error modeled with a Gaussian random variable. In contrast to previous works, the random errors are modeled jointly across all methods, thereby enabling the framework to analyze measurement methods based on similar principles, which may have correlated random errors. Furthermore, the posterior distribution of the error model parameters is estimated from samples obtained by Markov chain Monte-Carlo and analyzed to estimate the parameter values and the unknown true values of the measurand. The framework was validated on six synthetic and one clinical dataset containing measurements of total lesion load, a biomarker of neurodegenerative diseases, which was obtained with four automatic methods by analyzing brain magnetic resonance images. The estimates of bias and random error were in a good agreement with the corresponding least squares regression estimates against a reference.

Brain Volume Estimation Enhancement by Morphological Image Processing Tools

BACKGROUND

Volume estimation of brain is important for many neurological applications. It is necessary in measuring brain growth and changes in brain in normal/abnormal patients. Thus, accurate brain volume measurement is very important. Magnetic resonance imaging (MRI) is the method of choice for volume quantification due to excellent levels of image resolution and between-tissue contrast. Stereology method is a good method for estimating volume but it requires to segment enough MRI slices and have a good resolution. In this study, it is desired to enhance stereology method for volume estimation of brain using less MRI slices with less resolution.

METHODS

In this study, a program for calculating volume using stereology method has been introduced. After morphologic method, dilation was applied and the stereology method enhanced. For the evaluation of this method, we used T1-wighted MR images from digital phantom in BrainWeb which had ground truth.

RESULTS

The volume of 20 normal brain extracted from BrainWeb, was calculated. The volumes of white matter, gray matter and cerebrospinal fluid with given dimension were estimated correctly. Volume calculation from Stereology method in different cases was made. In three cases, Root Mean Square Error (RMSE) was measured. Case I with T=5, d=5, Case II with T=10, D=10 and Case III with T=20, d=20 (T=slice thickness, d=resolution as stereology parameters). By comparing these results of two methods, it is obvious that RMSE values for our proposed method are smaller than Stereology method.

CONCLUSION

Using morphological operation, dilation allows to enhance the estimation volume method, Stereology. In the case with less MRI slices and less test points, this method works much better compared to Stereology method.

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts.

'Timed up and go' and brain atrophy: a preliminary MRI study to assess functional mobility performance in multiple sclerosis

Motor and cognitive disabilities are related to brain atrophy in multiple sclerosis (MS). 'Timed up and go' (TUG) has been recently tested in MS as functional mobility test, as it is able to evaluate ambulation/coordination-related tasks, as well as cognitive function related to mobility. The objective of this study is to evaluate the relationship between brain volumes and TUG performances. Inclusion criteria were a diagnosis of MS and the ability to walk at least 20 m. TUG was performed using a wearable inertial sensor. Times and velocities of TUG sub-phases were calculated by processing trunk acceleration data. Patients underwent to a brain MRI, and volumes of whole brain, white matter (WM), grey matter (GM), and cortical GM (C) were estimated with SIENAX. Sixty patients were enrolled. Mean age was 41.5 ± 11.6 years and mean EDSS 2.3 ± 1.2. Total TUG duration was correlated to lower WM (ρ = 0.358, p = 0.005) and GM (ρ = 0.309, p = 0.017) volumes. A stronger association with lower GM volume was observed for intermediate (ρ = 0.427, p = 0.001) and final turning (ρ = 0.390, p = 0.002). TUG is a useful tool in a clinical setting as it can not only evaluate patients' disability in terms of impaired functional mobility, but also estimate pathological features, such as grey atrophy.

Diagnostic Efficacy of Structural MRI in Patients With Mild-to-Moderate Alzheimer Disease: Automated Volumetric Assessment Versus Visual Assessment

OBJECTIVE

The purpose of this study was to compare the diagnostic efficacies of an automated volumetric assessment tool and visual assessment in the evaluation of medial temporal lobar atrophy in mild-to-moderate Alzheimer disease (AD).

MATERIALS AND METHODS

This retrospective study included 30 patients with mild-to-moderate AD and 25 age-matched healthy control subjects undergoing MRI with a 3D fast spoiled gradient recalled-echo sequence at 3 T. The images were processed with fully automated volumetric analysis software. To assess medial temporal lobe (MTL) atrophy, two MTL indexes, which took into account the volumes of the hippocampus and the inferior lateral ventricle, were calculated with the automated volumetric assessment software. In addition, two neuroradiologists assessed MTL atrophy visually using the Scheltens scale. ROC curve analysis was used to compare the diagnostic performances of the two methods. The weighted kappa statistic was used to assess the intrarater and interrater reliability of visual inspection.

RESULTS

The automated volumetric assessment tool had moderate sensitivity (63.3%) and high specificity (100%) in differentiating patients with mild-to-moderate AD from control subjects. Visual inspection showed sensitivity of 63.3% and specificity of 92.0%. The diagnostic performance was not significantly different between the two methods (p = 0.536-0.906). Intraobserver reliability for visual inspection was 0.858 and 0.902 for the two reviewers, and interobserver reliability was 0.692-0.780.

CONCLUSION

Both the automated volumetric assessment tool and visual inspection can be used to evaluate MTL atrophy and differentiate patients with AD from healthy individuals with good diagnostic accuracy. Thus, the automated tool can be a useful and efficient adjunct in clinical practice for evaluating MTL atrophy in the diagnosis of AD.

Effect of Fingolimod on Brain Volume Loss in Patients with Multiple Sclerosis

Brain atrophy occurs at a faster rate in patients with multiple sclerosis (MS) than in healthy individuals. In three randomized, controlled, phase III trials, fingolimod reduced the annual rate of brain volume loss (BVL) in patients with relapsing MS (RMS) by approximately one-third relative to that in individuals receiving placebo or intramuscular interferon beta-1a. Analysis of brain volume changes during study extensions has shown that this reduced rate of BVL is sustained in patients with RMS receiving fingolimod continuously. Subgroup analyses of the core phase III and extension studies have shown that reductions in the rate of BVL are observed irrespective of levels of inflammatory lesion activity seen by magnetic resonance imaging at baseline and on study; levels of disability at baseline; and treatment history. The rate of BVL in these studies was predicted independently by T2 lesion and gadolinium-enhancing lesion burdens at baseline, and correlations observed between BVL and increasing levels of disability strengthened over time. In another phase III trial in patients with primary progressive MS (PPMS), fingolimod did not reduce BVL overall relative to placebo; however, consistent with findings in RMS, there was a treatment effect on BVL in patients with PPMS with gadolinium-enhancing lesion activity at baseline. The association between treatment effects on BVL and future accumulation of disability argues in favor of measuring BVL on a more routine basis and with a more structured approach than is generally the case in clinical practice. Despite several practical obstacles, progress is being made in achieving this goal.

Structural Magnetic Resonance Imaging Markers of Alzheimer's Disease and Its Retranslation to Rodent Models

The importance of imaging biomarkers has been acknowledged in the diagnosis and in the follow-up of Alzheimer's disease (AD), one of the major causes of dementia. Next to the molecular biomarkers and PET imaging investigations, structural MRI approaches provide important information about the disease progression and about the pathomechanism. Furthermore,a growing body of literature retranslates these imaging biomarkers to various rodent models of the disease. The goal of this review is to provide an overview of the macro- and microstructural imaging biomarkers of AD, concentrating on atrophy measures and diffusion MRI alterations. A survey is also given of the imaging approaches used in rodent models of dementias that can promote drug development.

Brain MRI abnormalities in the adult form of myotonic dystrophy type 1: A longitudinal case series study

This study aimed to verify whether brain abnormalities, previously described in patients with myotonic dystrophy type 1 (DM1) by magnetic resonance imaging (MRI), progressed over time and, if so, to characterize their progression. Thirteen DM1 patients, who had at least two MRI examinations, were retrospectively evaluated and included in the study. The mean duration (± standard deviation) of follow-up was 13.4 (±3.8) years, over a range of 7-20 years. White matter lesions (WMLs) were rated by semi-quantitative method, the signal intensity of white matter poster-superior to trigones (WMPST) by reference to standard images and brain atrophy by ventricular/brain ratio (VBR). At the end of MRI follow-up, the scores relative to lobar, temporal and periventricular WMLs, to WMPST signal intensity and to VBR were significantly increased compared to baseline, and MRI changes were more evident in some families than in others. No correlation was found between the MRI changes and age, onset, disease duration, muscular involvement, CTG repetition and follow-up duration. These results demonstrated that white matter involvement and brain atrophy were progressive in DM1 and suggested that progression rate varied from patient to patient, regardless of age, disease duration and genetic defect.

Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis

OBJECTIVE

To assess whether it is feasible to establish specific cut-off values able to discriminate 'physiological' or 'pathological' brain volume rates in patients with multiple sclerosis (MS).

METHODS

The study was based on the analysis of longitudinal MRI data sets of patients with MS (n=206, 87% relapsing-remitting, 7% secondary progressive and 6% primary progressive) and healthy controls (HC; n=35). Brain atrophy rates were computed over a mean follow-up of 7.5 years (range 1-12) for patients with MS and 6.3 years (range 1-12.5) for HC with the SIENA software and expressed as annualised per cent brain volume change (PBVC/y). A weighted (on the follow-up length) receiver operating characteristic analysis and the area under the curve (AUC) were used for statistics.

RESULTS

The weighted PBVC/y was -0.51±0.27% in patients with MS and -0.27±0.15% in HC (p<0.0001). There was a significant age-related difference in PBVC/y between HC older and younger than 35 years of age (p=0.02), but not in patients with MS (p=0.8). The cut-off of PBVC/y, as measured by SIENA that could maximise the accuracy in discriminating patients with MS from HC, was -0.37%, with 67% sensitivity and 80% specificity. According to the observed distribution, values of PBVC/y as measured by SIENA that could define a pathological range were above -0.52% with 95% specificity, above -0.46% with 90% specificity and above -0.40% with 80% specificity.

CONCLUSIONS

Our evidence-based criteria provide values able to discriminate the presence or absence of 'pathological' brain volume loss in MS with high specificity. Such results could be of great value in a clinical setting, particularly in assessing treatment efficacy in MS.

MRBrainS Challenge: Online Evaluation Framework for Brain Image Segmentation in 3T MRI Scans

Many methods have been proposed for tissue segmentation in brain MRI scans. The multitude of methods proposed complicates the choice of one method above others. We have therefore established the MRBrainS online evaluation framework for evaluating (semi)automatic algorithms that segment gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) on 3T brain MRI scans of elderly subjects (65–80 y). Participants apply their algorithms to the provided data, after which their results are evaluated and ranked. Full manual segmentations of GM, WM, and CSF are available for all scans and used as the reference standard. Five datasets are provided for training and fifteen for testing. The evaluated methods are ranked based on their overall performance to segment GM, WM, and CSF and evaluated using three evaluation metrics (Dice, H95, and AVD) and the results are published on the MRBrainS13 website. We present the results of eleven segmentation algorithms that participated in the MRBrainS13 challenge workshop at MICCAI, where the framework was launched, and three commonly used freeware packages: FreeSurfer, FSL, and SPM. The MRBrainS evaluation framework provides an objective and direct comparison of all evaluated algorithms and can aid in selecting the best performing method for the segmentation goal at hand.

3D Modeling of the Lateral Ventricles and Histological Characterization of Periventricular Tissue in Humans and Mouse

The ventricular system carries and circulates cerebral spinal fluid (CSF) and facilitates clearance of solutes and toxins from the brain. The functional units of the ventricles are ciliated epithelial cells termed ependymal cells, which line the ventricles and through ciliary action are capable of generating laminar flow of CSF at the ventricle surface. This monolayer of ependymal cells also provides barrier and filtration functions that promote exchange between brain interstitial fluids (ISF) and circulating CSF. Biochemical changes in the brain are thereby reflected in the composition of the CSF and destruction of the ependyma can disrupt the delicate balance of CSF and ISF exchange. In humans there is a strong correlation between lateral ventricle expansion and aging. Age-associated ventriculomegaly can occur even in the absence of dementia or obstruction of CSF flow. The exact cause and progression of ventriculomegaly is often unknown; however, enlarged ventricles can show regional and, often, extensive loss of ependymal cell coverage with ventricle surface astrogliosis and associated periventricular edema replacing the functional ependymal cell monolayer. Using MRI scans together with postmortem human brain tissue, we describe how to prepare, image and compile 3D renderings of lateral ventricle volumes, calculate lateral ventricle volumes, and characterize periventricular tissue through immunohistochemical analysis of en face lateral ventricle wall tissue preparations. Corresponding analyses of mouse brain tissue are also presented supporting the use of mouse models as a means to evaluate changes to the lateral ventricles and periventricular tissue found in human aging and disease. Together, these protocols allow investigations into the cause and effect of ventriculomegaly and highlight techniques to study ventricular system health and its important barrier and filtration functions within the brain.

Magnetic resonance outcome measures in multiple sclerosis trials: time to rethink?

PURPOSE OF REVIEW

We summarize MRI measures currently available to assess treatment efficacy and safety in multiple sclerosis (MS) clinical trials and discuss novel metrics that could enter the clinical arena in the near future.

RECENT FINDINGS

In relapsing remitting MS, MRI measures of disease activity (new T2 and gadolinium-enhancing lesions) provide a good surrogacy of treatment effect on relapse rate and disability progression; however, their value in progressive MS remains elusive. For the progressive disease forms, these measures need to be combined with quantities assessing the extent of irreversible tissue loss, which have already been introduced in some clinical trials (e.g., evolution of active lesions into permanent black holes and brain atrophy). Novel measures (e.g., quantification of gray matter and spinal cord atrophy) have demonstrated a great value in explaining patients' clinical outcome, but still need to be fully validated. Despite showing promise, evaluations of cortical lesions, of microscopic tissue abnormalities, and of functional cortical reorganization are still some way off for monitoring of treatment effects.

SUMMARY

Trial outcomes in MS should include measures of inflammation and neurodegeneration, which should be combined according to the disease clinical phenotype, phase of the study, and the supposed mechanism of action of the drug tested.

An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease

Voxel-based morphometry from conventional T1-weighted images has proved effective to quantify Alzheimer's disease (AD) related brain atrophy and to enable fairly accurate automated classification of AD patients, mild cognitive impaired patients (MCI) and elderly controls. Little is known, however, about the classification power of volume-based morphometry, where features of interest consist of a few brain structure volumes (e.g. hippocampi, lobes, ventricles) as opposed to hundreds of thousands of voxel-wise gray matter concentrations. In this work, we experimentally evaluate two distinct volume-based morphometry algorithms (FreeSurfer and an in-house algorithm called MorphoBox) for automatic disease classification on a standardized data set from the Alzheimer's Disease Neuroimaging Initiative. Results indicate that both algorithms achieve classification accuracy comparable to the conventional whole-brain voxel-based morphometry pipeline using SPM for AD vs elderly controls and MCI vs controls, and higher accuracy for classification of AD vs MCI and early vs late AD converters, thereby demonstrating the potential of volume-based morphometry to assist diagnosis of mild cognitive impairment and Alzheimer's disease.

Advances in MRI biomarkers for the diagnosis of Alzheimer's disease

With the prevalence of Alzheimer's disease (AD) predicted to increase substantially over the coming decades, the development of effective biomarkers for the early detection of the disease is paramount. In this short review, the main neuroimaging techniques which have shown potential as biomarkers for AD are introduced, with a focus on MRI. Structural MRI measures of the hippocampus and medial temporal lobe are still the most clinically validated biomarkers for AD, but newer techniques such as functional MRI and diffusion tensor imaging offer great scope in tracking changes in the brain, particularly in functional and structural connectivity, which may precede gray matter atrophy. These new advances in neuroimaging methods require further development and crucially, standardization; however, before they are used as biomarkers to aid in the diagnosis of AD.

Magnetic resonance neuroimaging study of brain structural differences in diabetic peripheral neuropathy

OBJECTIVE

Diabetic peripheral neuropathy (DPN) has hitherto been considered a disease of the peripheral nervous system only, with central nervous system (CNS) involvement largely overlooked. The aim of this study was to investigate any differences in brain structure in subjects with DPN.

RESEARCH DESIGN AND METHODS

Thirty-six subjects with type 1 diabetes (No DPN [n = 18], Painful DPN [n = 9], Painless DPN [n = 9]) underwent neurophysiological assessment to quantify the severity of DPN. All subjects, including 18 healthy volunteers (HVs), underwent volumetric brain magnetic resonance imaging at 3 Tesla.

RESULTS

Adjusted peripheral gray matter volume was statistically significantly lower in subjects with painless and painful DPN (mean 599.6 mL [SEM 9.8 mL] and 585.4 mL [10.0 mL], respectively) compared with those with No DPN (626.5 mL [5.7 mL]) and HVs (639.9 mL [7.2 mL]; ANCOVA, P = 0.001). The difference in adjusted peripheral gray matter volume between subjects with No DPN and HVs and those with Painful DPN and Painless DPN was not statistically significant (P = 0.16 and 0.30, respectively). Voxel-based morphometry analyses revealed greater localized volume loss in the primary somatosensory cortex, supramarginal gyrus, and cingulate cortex (corrected P < 0.05) in DPN subjects.

CONCLUSIONS

This is the first study to focus on structural changes in the brain associated with DPN. Our findings suggest increased peripheral gray matter volume loss, localized to regions involved with somatosensory perception in subjects with DPN. This may have important implications for the long-term prognosis of DPN.

An algorithmic approach to structural imaging in dementia

Accurate and timely diagnosis of dementia is important to guide management and provide appropriate information and support to patients and families. Currently, with the exception of individuals with genetic mutations, postmortem examination of brain tissue remains the only definitive means of establishing diagnosis in most cases, however, structural neuroimaging, in combination with clinical assessment, has value in improving diagnostic accuracy during life. Beyond the exclusion of surgical pathology, signal change and cerebral atrophy visible on structural MRI can be used to identify diagnostically relevant imaging features, which provide support for clinical diagnosis of neurodegenerative dementias. While no structural imaging feature has perfect sensitivity and specificity for a given diagnosis, there are a number of imaging characteristics which provide positive predictive value and help to narrow the differential diagnosis. While neuroradiological expertise is invaluable in accurate scan interpretation, there is much that a non-radiologist can gain from a focused and structured approach to scan analysis. In this article we describe the characteristic MRI findings of the various dementias and provide a structured algorithm with the aim of providing clinicians with a practical guide to assessing scans.

Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition

OBJECT

The aim of this study was to demonstrate a new automatic brain segmentation method in magnetic resonance imaging (MRI).

MATERIALS AND METHODS

The signal of a spoiled gradient-recalled echo (SPGR) sequence acquired with multiple flip angles was used to map T1, and a subsequent fit of a multi-compartment model yielded parametric maps of partial volume estimates of the different compartments. The performance of the proposed method was assessed through simulations as well as in-vivo experiments in five healthy volunteers.

RESULTS

Simulations indicated that the proposed method was capable of producing robust segmentation maps with good reliability. Mean bias was below 3% for all tissue types, and the corresponding similarity index (Dice's coefficient) was over 95% (SNR = 100). In-vivo experiments yielded realistic segmentation maps, with comparable quality to results obtained with an established segmentation method. Relative whole-brain cerebrospinal fluid, grey matter, and white matter volumes were (mean ± SE) respectively 6.8 ± 0.5, 47.3 ± 1.1, and 45.9 ± 1.3% for the proposed method, and 7.5 ± 0.6, 46.2 ± 1.2, and 46.3 ± 0.9% for the reference method.

CONCLUSION

The proposed approach is promising for brain segmentation and partial volume estimation. The straightforward implementation of the method is attractive, and protocols that already rely on SPGR-based T1 mapping may employ this method without additional scans.