Automatic "pipeline" analysis of 3-D MRI data for clinical trials: application to multiple sclerosis

Cortical thickness analysis examined through power analysis and a population simulation

We have previously developed a procedure for measuring the thickness of cerebral cortex over the whole brain using 3-D MRI data and a fully automated surface-extraction (ASP) algorithm. This paper examines the precision of this algorithm, its optimal performance parameters, and the sensitivity of the method to subtle, focal changes in cortical thickness. The precision of cortical thickness measurements was studied using a simulated population study and single subject reproducibility metrics. Cortical thickness was shown to be a reliable method, reaching a sensitivity (probability of a true-positive) of 0.93. Six different cortical thickness metrics were compared. The simplest and most precise method measures the distance between corresponding vertices from the white matter to the gray matter surface. Given two groups of 25 subjects, a 0.6-mm (15%) change in thickness can be recovered after blurring with a 3-D Gaussian kernel (full-width half max = 30 mm). Smoothing across the 2-D surface manifold also improves precision; in this experiment, the optimal kernel size was 30 mm.

Two methods for validating brain tissue classifiers

In this paper, we present an evaluation of seven automatic brain tissue classifiers based on level of agreements. A number of agreement measures are explained, and we show how they can be used to compare different segmentation techniques. We use the Simultaneous Truth and Performance Level Estimation (STAPLE) of Warfield et al. but also introduce a novel evaluation technique based on the Williams' index. The methods are evaluated using these two techniques on a population of forty subjects, each having an SPGR scan and a co-registered T2 weighted scan. We provide an interpretation of the results and show how similar the output of the STAPLE analysis and Williams' index are. When no ground truth is required, we recommend the use of Williams' index as it is easy and fast to compute.

Evaluation of accuracy in MS lesion volumetry using realistic lesion phantoms

RATIONALE AND OBJECTIVES

Quantitative analysis of such small structures as focal lesions in patients with multiple sclerosis (MS) is an important issue in both diagnosis and therapy monitoring. To reach clinical relevance, the reproducibility and accuracy of a proposed method have to be validated. We propose a framework for the generation of realistic digital phantoms of MS lesions of known volumes and their incorporation into a magnetic resonance (MR) data set of a healthy volunteer.

MATERIALS AND METHODS

We generated 54 data sets from a multispectral brain scan of a healthy volunteer with incorporated MS lesion phantoms. Lesion phantoms were created using different shapes (three), sizes (six), and orientations (three). An evaluation is carried out from a manual analysis of three human experts and two different semiautomatic approaches, with and without explicit modeling of partial volume effects (PVEs).

RESULTS

Intraobserver and interobserver studies were performed for the phantom data sets. All experts overestimated the true lesion volume for any phantom data set (median overestimation between 42.9% and 63.2%). Relative error and variability increased with decreasing lesion size. Similar results were obtained for the semiautomatic approach without PVE modeling. Only the approach with explicit PVE modeling was capable of generating accurate volumetric results with low systematic error.

CONCLUSION

The proposed framework based on realistic lesion phantoms incorporated into an MR scan allows for quantitative assessment of the accuracy of manual and automated lesion volumetry. Results clearly show the importance of an improved gold standard in lesion volumetry beyond voxel counting.

Focal Decline of Cortical Thickness in Alzheimer's Disease Identified by Computational Neuroanatomy

Alzheimer's disease (AD) is characterized by a heterogeneous distribution of pathological changes throughout the brain. Magnetic resonance imaging can be used to investigate the regional distribution of cortical atrophy in AD in vivo. One marker for the disease-specific atrophy is the thickness of the cortical mantle across the brain, obtained with automated 3-D image processing. Here, we present data from 36 subjects (17 controls and, 19 patients diagnosed as probable AD) investigated for cortical thickness across the entire brain. We show significant cortical thickness decline in AD in temporal, orbitofrontal and parietal regions, with the most pronounced changes occurring in the allocortical region of the medial temporal lobes, outlining the parahippocampal gyrus, and representing a loss of >1.25 millimeters of cortical thickness. Moreover, focal cortical areas decline with progression of the disease as measured by time from baseline scan as well as the Mini-Mental State Exam. The results demonstrate the ability of this method to detect changes in cortical thickness in AD, across the entire brain, without need of prior anatomical definitions. The regional distribution of changes reported here is consistent with independent findings on the distribution of neuropathological alterations in AD. Using cortical thickness, moreover, we provide a direct quantitative index of atrophy in the disease.

Voxel-based morphometry of human brain with age and cerebrovascular risk factors

The objectives of this study were to evaluate the correlations of the volumes of the gray matter and white matter with age, and the correlations of the tissue probabilities of the gray matter and white matter with age and several cerebrovascular risk factors. We obtained magnetic resonance (MR) images of the brain and clinical information from 769 normal Japanese subjects. We processed the MR images automatically by correcting for inter-individual differences in brain size and shape, and by segmenting the MR images into the gray matter and white matter. Volumetry of the brain revealed a significant negative correlation between the gray matter volume and age, which was not observed between white matter volume and age. Voxel-based morphometry showed that age, systolic blood pressure, and alcohol drinking correlated with the regional tissue probabilities of the gray matter and white matter.

Probabilistic segmentation of white matter lesions in MR imaging

A new method has been developed for fully automated segmentation of white matter lesions (WMLs) in cranial MR imaging. The algorithm uses information from T1-weighted (T1-w), inversion recovery (IR), proton density-weighted (PD), T2-weighted (T2-w) and fluid attenuation inversion recovery (FLAIR) scans. It is based on the K-Nearest Neighbor (KNN) classification technique that builds a feature space from voxel intensities and spatial information. The technique generates images representing the probability per voxel being part of a WML. By application of thresholds on these probability maps, binary segmentations can be obtained. ROC curves show that the segmentations achieve both high sensitivity and specificity. A similarity index (SI), overlap fraction (OF) and extra fraction (EF) are calculated for additional quantitative analysis of the result. The SI is also used for determination of the optimal probability threshold for generation of the binary segmentation. Using probabilistic equivalents of the SI, OF and EF, the probability maps can be evaluated directly, providing a powerful tool for comparison of different classification results. This method for automated WML segmentation reaches an accuracy that is comparable to methods for multiple sclerosis (MS) lesion segmentation and is suitable for detection of WMLs in large and longitudinal population studies.

A fully automatic and robust brain MRI tissue classification method

A novel, fully automatic, adaptive, robust procedure for brain tissue classification from 3D magnetic resonance head images (MRI) is described in this paper. The procedure is adaptive in that it customizes a training set, by using a 'pruning' strategy, such that the classification is robust against anatomical variability and pathology. Starting from a set of samples generated from prior tissue probability maps (a 'model') in a standard, brain-based coordinate system ('stereotaxic space'), the method first reduces the fraction of incorrectly labeled samples in this set by using a minimum spanning tree graph-theoretic approach. Then, the corrected set of samples is used by a supervised kNN classifier for classifying the entire 3D image. The classification procedure is robust against variability in the image quality through a non-parametric implementation: no assumptions are made about the tissue intensity distributions. The performance of this brain tissue classification procedure is demonstrated through quantitative and qualitative validation experiments on both simulated MRI data (10 subjects) and real MRI data (43 subjects). A significant improvement in output quality was observed on subjects who exhibit morphological deviations from the model due to aging and pathology.

Quantitative image analysis: software systems in drug development trials

Multi-dimensional image analysis is being used increasingly to arrive at surrogate end-points for drug development trials. Various imaging modalities such as computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET) and ultrasound are used to analyze treatments for diseases such as cancer, multiple sclerosis, osteoarthritis, and Alzheimer's disease. However, extracting information from images can be tedious and is prone to high user variability. The medical image analysis community is moving towards advanced software systems specifically designed for drug development trials. These systems can automatically identify the anatomy of interest in medical images (segmentation methods), can compare the anatomy over time or between patients (registration methods) and allow the quantitative extraction of anatomical features and the integration of the data and results into a database management system, automatically tracking the changes made to the data (audit trail generation). In this article, we present a case study using a prototype system that is used for quantifying multiple sclerosis lesions from multivariate MRI.