MR image-based measurement of rates of change in volumes of brain structures. Part I: method and validation

Neuroanatomy of Patients with Deficit Schizophrenia: An Exploratory Quantitative Meta-Analysis of Structural Neuroimaging Studies

Little is known regarding the neuroanatomical correlates of patients with deficit schizophrenia or persistent negative symptoms. In this meta-analysis, we aimed to determine whether patients with deficit schizophrenia have characteristic brain abnormalities. We searched PubMed, CINAHL and Ovid to identify studies that examined the various regions of interest amongst patients with deficit schizophrenia, patients with non-deficit schizophrenia and healthy controls. A total of 24 studies met our inclusion criteria. A random-effects model was used to calculate a combination of outcome measures, and heterogeneity was assessed by the I² statistic and Cochran’s Q statistic. Our findings suggested that there was statistically significant reduction in grey matter volume (−0.433, 95% confidence interval (CI): −0.853 to −0.014, p = 0.043) and white matter volume (−0.319, 95% CI: −0.619 to −0.018, p = 0.038) in patients with deficit schizophrenia compared to healthy controls. There is also statistically significant reduction in total brain volume (−0.212, 95% CI: −0.384 to −0.041, p = 0.015) and white matter volume (−0.283, 95% CI: −0.546 to −0.021, p = 0.034) in patients with non-deficit schizophrenia compared to healthy controls. Between patients with deficit and non-deficit schizophrenia, there were no statistically significant differences in volumetric findings across the various regions of interest.

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.

Accelerated Brain Atrophy on Serial Computed Tomography: Potential Marker of the Progression of Alzheimer Disease

OBJECTIVE

The aim of this study was to validate computed tomography (CT)-based longitudinal markers of the progression of Alzheimer disease (AD).

MATERIALS AND METHODS

We retrospectively studied 33 AD patients and 39 nondemented patients with other neurological illnesses (non-AD) having 4 to 12 CT examinations of the head, with over a mean (SD) of 3.9 (1.7) years. At each time point, we applied an automatic software to measure whole brain, cerebrospinal fluid, and intracranial space volumes. Longitudinal measures were then related to disease status and time since the first scan using hierarchical models.

RESULTS

Absolute brain volume loss accelerated for non-AD patients by 0.86 mL/y (95% confidence interval [CI], 0.64-1.08 mL/y) and 1.5× faster, that is, 1.32 mL/y (95% CI, 1.09-1.56 mL/y) for AD patients (P = 0.006). In terms of brain volume normalized to intracranial space, the acceleration in atrophy rate for non-AD patients was 0.0578%/y (95% CI, 0.0389%/y to 0.0767%/y), again 1.5× faster, that is, 0.0919%/y (95% CI, 0.0716%/y to 0.1122%/y) for AD patients (P = 0.017). This translates to an increase in atrophy rate from 0.5% to 1.4% in AD versus to 1.1% in non-AD group after 10 years.

CONCLUSIONS

Brain volumetry on CT reliably detected accelerated volume loss in AD and significantly lower acceleration factor in age-matched non-AD patients, leading to the possibility of its use to monitor the progression of cognitive decline and dementia.

Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review

Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.

Width of the third ventricle assessed by transcranial sonography can monitor brain atrophy in a time- and cost-effective manner--results from a longitudinal study on 500 subjects

Ventricular width and its enlargement over time are discussed as promising markers for preclinical brain atrophy. The aim of our study was to define whether brain atrophy can reliably be monitored by transcranial ultrasound (TCS). In a prospective longitudinal trial over 5years, 500 healthy persons were examined by a standardized protocol with TCS in addition to an extensive cognitive testing using the Consortium to Establish a Registry of Alzheimer's Disease - Neuropsychological Testing (CERAD-NP). TCS displayed the third ventricle in 96% of all cases at the follow-up with a high intra-individual reproducibility and excellent inter-rater coefficient (0.992). The mean diameter of the third ventricle in subjects with a cognitive decline was significantly wider (6mm±2) than in subjects with normal cognitive testing results (4.6mm±1.8). We demonstrated that the width of the third ventricle, as a marker of brain atrophy can reliably be monitored by using TCS as a non-invasive, time- and cost-effective method. We provide evidence that the assessed width of the third ventricle can differentiate between subjects with a normal cognitive performance and subjects with a cognitive decline. TCS may be a useful screening tool in the early diagnosis of cognitive decline.

Open access series of imaging studies: longitudinal MRI data in nondemented and demented older adults

The Open Access Series of Imaging Studies is a series of neuroimaging data sets that are publicly available for study and analysis. The present MRI data set consists of a longitudinal collection of 150 subjects aged 60 to 96 years all acquired on the same scanner using identical sequences. Each subject was scanned on two or more visits, separated by at least 1 year for a total of 373 imaging sessions. Subjects were characterized using the Clinical Dementia Rating (CDR) as either nondemented or with very mild to mild Alzheimer's disease. Seventy-two of the subjects were characterized as nondemented throughout the study. Sixty-four of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with CDR 0.5 similar level of impairment to individuals elsewhere considered to have "mild cognitive impairment." Another 14 subjects were characterized as nondemented at the time of their initial visit (CDR 0) and were subsequently characterized as demented at a later visit (CDR > 0). The subjects were all right-handed and include both men (n = 62) and women (n = 88). For each scanning session, three or four individual T1-weighted MRI scans were obtained. Multiple within-session acquisitions provide extremely high contrast to noise, making the data amenable to a wide range of analytic approaches including automated computational analysis. Automated calculation of whole-brain volume is presented to demonstrate use of the data for measuring differences associated with normal aging and Alzheimer's disease.

Measurement of bone cyst fluid volume using k-means clustering

We designed a semiautomatic segmentation method to easily measure the volume of a bone cyst (simple or aneurysmal) from magnetic resonance imaging (MRI). This method only considers the fluid part of the cyst, even when there are several fluid intensities (fluid-fluid levels) or the cyst is multi-loculated. The nonhomogeneity phenomenon inherent in MRI was handled by a k-means clustering algorithm that classified all of the voxels corresponding to the cyst fluid as the same voxel intensity. Level-set segmentation was expanded into the whole cyst volume and the resulting segmented volume provided the measured cyst volume. The semiautomatic method was compared with the usual manual method (manual contour tracing) in terms of its ability to measure a known volume of water (gold standard) as well as the volume of 29 bone cysts. Both methods were equivalent with regards to the gold standard, but the semiautomatic method was more accurate. In terms of the experimental measurements, the semiautomatic method was more repeatable and reproducible, and less time-consuming and fastidious than the manual method. Our semiautomatic method uses only freeware and can be used routinely whenever measurement of a bone cyst volume is needed.

Evaluation of automated brain MR image segmentation and volumetry methods

We compare three widely used brain volumetry methods available in the software packages FSL, SPM5, and FreeSurfer and evaluate their performance using simulated and real MR brain data sets. We analyze the accuracy of gray and white matter volume measurements and their robustness against changes of image quality using the BrainWeb MRI database. These images are based on "gold-standard" reference brain templates. This allows us to assess between- (same data set, different method) and also within-segmenter (same method, variation of image quality) comparability, for both of which we find pronounced variations in segmentation results for gray and white matter volumes. The calculated volumes deviate up to >10% from the reference values for gray and white matter depending on method and image quality. Sensitivity is best for SPM5, volumetric accuracy for gray and white matter was similar in SPM5 and FSL and better than in FreeSurfer. FSL showed the highest stability for white (<5%), FreeSurfer (6.2%) for gray matter for constant image quality BrainWeb data. Between-segmenter comparisons show discrepancies of up to >20% for the simulated data and 24% on average for the real data sets, whereas within-method performance analysis uncovered volume differences of up to >15%. Since the discrepancies between results reach the same order of magnitude as volume changes observed in disease, these effects limit the usability of the segmentation methods for following volume changes in individual patients over time and should be taken into account during the planning and analysis of brain volume studies.

Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer's disease images

The evaluation of atrophy quantification methods based on magnetic resonance imaging have been usually hindered by the lack of realistic gold standard data against which to judge these methods or to help refine them. Recently [Camara, O., Schweiger, M., Scahill, R., Crum, W., Sneller, B., Schnabel, J., Ridgway, G., Cash, D., Hill, D., Fox, N., 2006. Phenomenological model of diffuse global and regional atrophy using finite-element methods. IEEE Trans. Med.l Imaging 25, 1417-1430], we presented a technique in which atrophy is realistically simulated in different tissue compartments or neuroanatomical structures with a phenomenological model. In this study, we have generated a cohort of realistic simulated Alzheimer's disease (AD) images with known amounts of atrophy, mimicking a set of 19 real controls and 27 probable AD subjects, with an improved version of our atrophy simulation methodology. This database was then used to assess the accuracy of several well-known computational anatomy methods which provide global (BSI and SIENA) or local (Jacobian integration) estimates of longitudinal atrophy in brain structures using MR images. SIENA and BSI results correlated very well with gold standard data (Pearson coefficient of 0.962 and 0.969 respectively), achieving small mean absolute differences with respect to the gold standard (percentage change from baseline volume): BSI of 0.23%+/-0.26%; SIENA of 0.22%+/-0.28%. Jacobian integration was guided by both fluid and FFD-based registration techniques and resulting deformation fields and associated Jacobians were compared, region by region, with gold standard ones. The FFD-based technique outperformed the fluid one in all evaluated structures (mean absolute differences from the gold standard in percentage change from baseline volume): whole brain, FFD=0.31%, fluid=0.58%; lateral ventricles, FFD=0.79%; fluid=1.45%; left hippocampus, FFD=0.82%; fluid=1.42%; right hippocampus, FFD=0.95%; fluid=1.62%. The largest errors for both local techniques occurred in the sulcal CSF (FFD=2.27%; fluid=3.55%) regions. For large structures such as the whole brain, these mean absolute differences, relative to the applied atrophy, represented similar percentages for the BSI, SIENA and FFD techniques (controls/patients): BSI, 51.99%/16.36%; SIENA, 62.34%/21.59%; FFD, 41.02%/24.95%. For small structures such as the hippocampi, these percentages were larger, especially for controls where errors were approximately equal to the small applied changes (controls/patients): FFD, 92.82%/43.61%. However, these apparently large relative errors have not prevented the global or hippocampal measures from finding significant group separation in our study. The evaluation framework presented here will help in quantifying whether the accuracy of future methodological developments is sufficient for analysing change in smaller or less atrophied local brain regions. Results obtained in our experiments with realistic simulated data confirm previously published estimates of accuracy for both evaluated global techniques. Regarding Jacobian Integration methods, the FFD-based one demonstrated promising results and potential for being used in clinical studies alongside (or in place of) the more common global methods. The generated gold standard data has also allowed us to identify some stages and sets of parameters in the evaluated techniques--the brain extraction step in the global techniques and the number of multi-resolution levels and the stopping criteria in the registration-based methods--that are critical for their accuracy.

Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

Diffusion tensor MRI (DTI) has been widely used to investigate brain microstructural changes in pathological conditions as well as for normal development and aging. In particular, longitudinal changes are vital to the understanding of progression but these studies are typically designed for specific regions of interest. To analyze changes in these regions traditional statistical methods are often employed to elucidate group differences which are measured against the variability found in a control cohort. However, in some cases, rather than collecting multiple subjects into two groups, it is necessary and more informative to analyze the data for individual subjects. There is also a need for understanding changes in a single subject without prior information regarding the spatial distribution of the pathology, but no formal statistical framework exists for these voxel-wise analyses of DTI. In this study, we present PERVADE (permutation voxel-wise analysis of diffusion estimates), a whole brain analysis method for detecting localized FA changes between two separate points in time of any given subject, without any prior hypothesis about where changes might occur. Exploiting the nature of DTI that it is calculated from multiple diffusion-weighted images of each region, permutation testing, a non-parametric hypothesis testing technique, was modified for the analysis of serial DTI data and implemented for voxel-wise hypothesis tests of diffusion metric changes, as well as for suprathreshold cluster analysis to correct for multiple comparisons. We describe PERVADE in detail and present results from Monte Carlo simulation supporting the validity of the technique as well as illustrative examples from a healthy subject and patients in the early stages of multiple sclerosis.

A new serial image registration method for contrast-enhanced MRI study

This paper presents a new method for aligning serial images acquired in contrast-enhanced MRI studies. A unique feature of the proposed method is that it uses dynamic references, rather than a single reference image as in the conventional method, to obtain co-registration of serial images. Specifically, each image serves as a reference for its neighboring images and the overall registration of all serial images is derived subsequently. We tested the new method using a digital image phantom and in-vivo contrast-enhanced tumor MRI data, using a least-square registration criterion and a rigid-body spatial transformation. Preliminary results showed that the new method is generally more robust and accurate than the conventional method.

Phenomenological model of diffuse global and regional atrophy using finite-element methods

The main goal of this work is the generation of ground-truth data for the validation of atrophy measurement techniques, commonly used in the study of neurodegenerative diseases such as dementia. Several techniques have been used to measure atrophy in cross-sectional and longitudinal studies, but it is extremely difficult to compare their performance since they have been applied to different patient populations. Furthermore, assessment of performance based on phantom measurements or simple scaled images overestimates these techniques' ability to capture the complexity of neurodegeneration of the human brain. We propose a method for atrophy simulation in structural magnetic resonance (MR) images based on finite-element methods. The method produces cohorts of brain images with known change that is physically and clinically plausible, providing data for objective evaluation of atrophy measurement techniques. Atrophy is simulated in different tissue compartments or in different neuroanatomical structures with a phenomenological model. This model of diffuse global and regional atrophy is based on volumetric measurements such as the brain or the hippocampus, from patients with known disease and guided by clinical knowledge of the relative pathological involvement of regions and tissues. The consequent biomechanical readjustment of structures is modelled using conventional physics-based techniques based on biomechanical tissue properties and simulating plausible tissue deformations with finite-element methods. A thermoelastic model of tissue deformation is employed, controlling the rate of progression of atrophy by means of a set of thermal coefficients, each one corresponding to a different type of tissue. Tissue characterization is performed by means of the meshing of a labelled brain atlas, creating a reference volumetric mesh that will be introduced to a finite-element solver to create the simulated deformations. Preliminary work on the simulation of acquisition artefacts is also presented. Cross-sectional and longitudinal sets of simulated data are shown and a visual classification protocol has been used by experts to rate real and simulated scans according to their degree of atrophy. Results confirm the potential of the proposed methodology.

Cerebral atrophy measurements using Jacobian integration: Comparison with the boundary shift integral

We compared two methods of measuring cerebral atrophy in a cohort of 38 clinically probable Alzheimer's disease (AD) subjects and 22 age-matched normal controls, using metrics of zero atrophy, consistency, scaled atrophy and AD/control group separation. The two methods compared were the boundary shift integral (BSI) and a technique based on the integration of Jacobian determinants from non-rigid registration. For each subject, we used two volumetric magnetic resonance (MR) scans at baseline and a third obtained 1 year later. The case of zero atrophy was established by registering the same-day baseline scan pair, which should approximate zero change. Consistency was established by registering the 1-year follow-up scan to each of the baseline scans, giving two measurements of atrophy that should be very similar, while scaled atrophy was established by reducing one of the same-day scans by a fixed amount, and rigidly registering this to the other same-day scan. Group separation was ascertained by calculating atrophy rates over the two 1-year measures for the control and AD subjects. The results showed the Jacobian integration technique was significantly more accurate in calculating scaled atrophy (P < 0.001) and was able to distinguish between control and AD subjects more clearly (P < 0.01).

Brain volume in first-episode schizophrenia: systematic review and meta-analysis of magnetic resonance imaging studies

BACKGROUND

Studies of people with schizophrenia assessed using magnetic resonance imaging (MRI) usually include patients with first-episode and chronic disease, yet brain abnormalities may be limited to those with chronic schizophrenia.

AIMS

To determine whether patients with a first episode of schizophrenia have characteristic brain abnormalities.

METHOD

Systematic review and meta-analysis of 66 papers comparing brain volume in patients with a first psychotic episode with volume in healthy controls.

RESULTS

A total of 52 cross-sectional studies included 1424 patients with a first psychotic episode; 16 longitudinal studies included 465 such patients. Meta-analysis suggests that whole brain and hippocampal volume are reduced (both P<0.0001) and that ventricular volume is increased (P<0.0001) in these patients relative to healthy controls.

CONCLUSIONS

Average volumetric changes are close to the limit of detection by MRI methods. It remains to be determined whether schizophrenia is a neurodegenerative process that begins at about the time of symptom onset, or whether it is better characterised as a neurodevelopmental process that produces abnormal brain volumes at an early age.

A critical discussion of the role of neuroimaging in mild cognitive impairment

OBJECTIVE

In this paper, the current neuroimaging literature is reviewed with regard to characteristic findings in mild cognitive impairment (MCI). Particular attention is drawn to the possible value of neuroimaging modalities in the prediction and early diagnosis of Alzheimer's disease (AD).

METHODS

First, the potential contribution of neuroimaging to an early, preclinical diagnosis of degenerative disorders is discussed at the background of our knowledge about the pathogenesis of AD. Second, relevant neuroimaging studies focusing on MCI are explored and summarized. Neuroimaging studies were found through Medline search and by systematically checking through the bibliographies of relevant articles.

RESULTS

Structural volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET)/single photon emission tomography (SPECT) are currently the most commonly used neuroimaging modalities in studies focusing on MCI. There were considerable variations in demographical and clinical characteristics across studies. However, significant hippocampal and entorhinal cortex volume reductions were consistently found in subjects with MCI as compared with cognitively unimpaired controls. While hippocampal and entorhinal cortex atrophy in subjects with MCI are also well-established risk factors for the development of AD, these measures cannot be regarded as being of high predictive value in an individual case. Evidence for other typical neuroimaging changes in MCI is still scarce. In PET and SPECT studies, reduced blood flow and/or glucose metabolism in temporoparietal association areas, posterior cingulate and hippocampus were associated with a higher risk of progressive cognitive decline in MCI. In quantitative electroencephalogram (QEEG), low beta, high theta, low alpha and slowed mean frequency were associated with development of dementia.

CONCLUSIONS

Existing studies suggest that neuroimaging measures have the potential to become valuable tools in the early diagnosis of AD. To establish their value in routine use, larger studies, preferably with long prospective follow-up are needed.

Automatic segmentation of the brain and intracranial cerebrospinal fluid in T1-weighted volume MRI scans of the head, and its application to serial cerebral and intracranial volumetry

A new fully automatic algorithm for the segmentation of the brain and total intracranial cerebrospinal fluid (CSF) from T(1)-weighted volume MRI scans of the head, called Exbrain v.2, is described. The algorithm was developed in the context of serial intracranial volumetry. A brain mask obtained using a previous version of the algorithm forms the basis of the CSF segmentation. Improved brain segmentation is then obtained by iterative tracking of the brain-CSF interface. Gray matter (GM), white matter (WM), and intracranial CSF volumes and probability maps are calculated based on a model of intensity probability distribution (IPD) that includes two partial volume classes: GM-CSF and GM-WM. Accuracy was assessed using the Montreal Neurological Institute's (MNI) digital phantom scan. Reproducibility was assessed using scan pairs from 24 controls and 10 patients with epilepsy. Segmentation overlap with the gold standard was 98% for the brain and 95%, 96%, and 97% for the GM, WM, and total intracranial contents, respectively; CSF overlap was 86%. In the controls, the Bland and Altman coefficient of reliability (CR) was 35.2 cm(3) for the total brain volume (TBV) and 29.0 cm(3) for the intracranial volume (ICV). Scan-matching reduced CR to 25.2 cm(3) and 17.1 cm(3) for the TBV and ICV, respectively. For the patients, similar CR values were obtained for the ICV.