Graphical analysis of MR feature space for measurement of CSF, gray-matter, and white-matter volumes

Quantitative measurement of cortical superficial siderosis in cerebral amyloid angiopathy

Cerebral amyloid angiopathy (CAA) is a cerebrovascular disease affecting the small arteries in the brain with hallmark depositions of amyloid-β in the vessel wall, leading to cognitive decline and intracerebral hemorrhage (ICH). An emerging MRI marker for CAA is cortical superficial siderosis (cSS) as it is strongly related to the risk of (recurrent) ICH. Current assessment of cSS is mainly done on T2*- weighted MRI using a qualitative score consisting of 5 categories of severity which is hampered by ceiling effects. Therefore, the need for a more quantitative measurement is warranted to better map disease progression for prognosis and future therapeutic trials. We propose a semi-automated method to quantify cSS burden on MRI and investigated it in 20 patients with CAA and cSS. The method showed excellent inter-observer (Pearson's 0.991, P < 0.001) and intra-observer reproducibility (ICC 0.995, P < 0.001). Furthermore, in the highest category of the multifocality scale a large spread in the quantitative score is observed, demonstrating the ceiling effect in the traditional score. We observed a quantitative increase in cSS volume in two of the 5 patients who had a 1 year follow up, while the traditional qualitative method failed to identify an increase because these patients were already in the highest category. The proposed method could therefore potentially be a better way of tracking progression. In conclusion, semi-automated segmenting and quantifying cSS is feasible and repeatable and may be used for further studies in CAA cohorts.

Quantitative Synthetic MRI in Children: Normative Intracranial Tissue Segmentation Values during Development

BACKGROUND AND PURPOSE

Synthetic MR imaging is a new technique to create absolute R1 relaxivity (1/T1), R2 relaxivity (1/T2), and proton-density maps using a single multiple-spin-echo saturation recovery sequence. These relaxivity maps allow rapid automated intracranial segmentation of tissue types. To assess its utility in children, we created a normative data base of intracranial volume and brain parenchymal, GM, WM, CSF, and myelin volumes in a pediatric population with normal brain MRI findings using synthetic MR imaging.

MATERIALS AND METHODS

All multiple-spin-echo saturation recovery sequences containing brain MR imaging examinations performed during 34 months were retrospectively reviewed. Abnormal examination findings were excluded following a detailed radiographic and clinical chart review. The remaining normal examination findings were then quantitatively analyzed with synthetic MR imaging. Intracranial, brain parenchymal, GM, WM, CSF, and myelin volumes were plotted versus age. Qualitative assessment of segmentation accuracy was performed. Selected abnormal examination findings were compared with these normative curves.

RESULTS

One hundred twenty-two MRI examinations with normal findings were included of individuals ranging from 0.1 to 21.5 years of age (median, 11.8 years). Resulting normative data plots compared favorably with previously published data obtained using more onerous techniques. Differentiation from pathologic states was possible using quantitative values in select cases.

CONCLUSIONS

A pediatric data base of normal intracranial tissue volumes using a single sequence and rapid software analysis has been compiled and correlates with previously published data. This provides a framework for clinical interpretation of quantitative synthetic MR images during development. Improved age-based segmentation algorithms in young children are needed.

Interactive Feature Space Explorer© for multi-modal magnetic resonance imaging

Wider information content of multi-modal biomedical imaging is advantageous for detection, diagnosis and prognosis of various pathologies. However, the necessity to evaluate a large number images might hinder these advantages and reduce the efficiency. Herein, a new computer aided approach based on the utilization of feature space (FS) with reduced reliance on multiple image evaluations is proposed for research and routine clinical use. The method introduces the physician experience into the discovery process of FS biomarkers for addressing biological complexity, e.g., disease heterogeneity. This, in turn, elucidates relevant biophysical information which would not be available when automated algorithms are utilized. Accordingly, the prototype platform was designed and built for interactively investigating the features and their corresponding anatomic loci in order to identify pathologic FS regions. While the platform might be potentially beneficial in decision support generally and specifically for evaluating outlier cases, it is also potentially suitable for accurate ground truth determination in FS for algorithm development. Initial assessments conducted on two different pathologies from two different institutions provided valuable biophysical perspective. Investigations of the prostate magnetic resonance imaging data resulted in locating a potential aggressiveness biomarker in prostate cancer. Preliminary findings on renal cell carcinoma imaging data demonstrated potential for characterization of disease subtypes in the FS.

Application of quantitative MRI for brain tissue segmentation at 1.5 T and 3.0 T field strengths

BACKGROUND

Brain tissue segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) are important in neuroradiological applications. Quantitative Mri (qMRI) allows segmentation based on physical tissue properties, and the dependencies on MR scanner settings are removed. Brain tissue groups into clusters in the three dimensional space formed by the qMRI parameters R1, R2 and PD, and partial volume voxels are intermediate in this space. The qMRI parameters, however, depend on the main magnetic field strength. Therefore, longitudinal studies can be seriously limited by system upgrades. The aim of this work was to apply one recently described brain tissue segmentation method, based on qMRI, at both 1.5 T and 3.0 T field strengths, and to investigate similarities and differences.

METHODS

In vivo qMRI measurements were performed on 10 healthy subjects using both 1.5 T and 3.0 T MR scanners. The brain tissue segmentation method was applied for both 1.5 T and 3.0 T and volumes of WM, GM, CSF and brain parenchymal fraction (BPF) were calculated on both field strengths. Repeatability was calculated for each scanner and a General Linear Model was used to examine the effect of field strength. Voxel-wise t-tests were also performed to evaluate regional differences.

RESULTS

Statistically significant differences were found between 1.5 T and 3.0 T for WM, GM, CSF and BPF (p<0.001). Analyses of main effects showed that WM was underestimated, while GM and CSF were overestimated on 1.5 T compared to 3.0 T. The mean differences between 1.5 T and 3.0 T were -66 mL WM, 40 mL GM, 29 mL CSF and -1.99% BPF. Voxel-wise t-tests revealed regional differences of WM and GM in deep brain structures, cerebellum and brain stem.

CONCLUSIONS

Most of the brain was identically classified at the two field strengths, although some regional differences were observed.

Novel whole brain segmentation and volume estimation using quantitative MRI

OBJECTIVES

Brain segmentation and volume estimation of grey matter (GM), white matter (WM) and cerebro-spinal fluid (CSF) are important for many neurological applications. Volumetric changes are observed in multiple sclerosis (MS), Alzheimer's disease and dementia, and in normal aging. A novel method is presented to segment brain tissue based on quantitative magnetic resonance imaging (qMRI) of the longitudinal relaxation rate R(1), the transverse relaxation rate R(2) and the proton density, PD.

METHODS

Previously reported qMRI values for WM, GM and CSF were used to define tissues and a Bloch simulation performed to investigate R(1), R(2) and PD for tissue mixtures in the presence of noise. Based on the simulations a lookup grid was constructed to relate tissue partial volume to the R(1)-R(2)-PD space. The method was validated in 10 healthy subjects. MRI data were acquired using six resolutions and three geometries.

RESULTS

Repeatability for different resolutions was 3.2% for WM, 3.2% for GM, 1.0% for CSF and 2.2% for total brain volume. Repeatability for different geometries was 8.5% for WM, 9.4% for GM, 2.4% for CSF and 2.4% for total brain volume.

CONCLUSION

We propose a new robust qMRI-based approach which we demonstrate in a patient with MS.

KEY POINTS

• A method for segmenting the brain and estimating tissue volume is presented • This method measures white matter, grey matter, cerebrospinal fluid and remaining tissue • The method calculates tissue fractions in voxel, thus accounting for partial volume • Repeatability was 2.2% for total brain volume with imaging resolution <2.0 mm.

Statistical validation of image segmentation quality based on a spatial overlap index

RATIONALE AND OBJECTIVES

To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy.

MATERIALS AND METHODS

The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA).

RESULTS

Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899).

CONCLUSION

The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.

FLAIR histogram segmentation for measurement of leukoaraiosis volume

The purposes of this study were to develop a method to measure brain and white matter hyperintensity (leukoaraiosis) volume that is based on the segmentation of the intensity histogram of fluid-attenuated inversion recovery (FLAIR) images and to assess the accuracy and reproducibility of the method. Whole-head synthetic image phantoms with manually introduced leukoaraiosis lesions of varying severity were constructed. These synthetic image phantom sets incorporated image contrast and anatomic features that mimicked leukoaraiosis found in real life. One set of synthetic image phantoms was used to develop the segmentation algorithm (FLAIR-histoseg). A second set was used to measure its accuracy. Test retest reproducibility was assessed in 10 elderly volunteers who were imaged twice. The mean absolute error of the FLAIR-histoseg method was 6.6% for measurement of leukoaraiosis volume and 1.4% for brain volume. The mean test retest coefficient of variation was 1.4% for leukoaraiosis volume and 0.3% for brain volume. We conclude that the FLAIR-histoseg method is an accurate and reproducible method for measuring leukoaraiosis and whole-brain volume in elderly subjects.

Segmenting brain white matter, gray matter and cerebro-spinal fluid using diffusion tensor-MRI derived indices

Based on its ability to provide quantitative information about tissue microstructure, diffusion tensor magnetic resonance imaging (DT-MRI) might be a valuable approach to improve the reliability of segmentation of the various brain tissues. In this study, a fully automated and easy-to-implement technique based on 2D histogram analysis of DT-MRI derived images was used to segment white and gray matter of the brain from 10 healthy subjects (aged = 27-56 years). The results obtained with this novel segmentation strategy were compared to those achieved by two experienced observers using an operator-dependent segmentation on the dual-echo scans. Visual inspection of the segmented tissues from a third senior observer disclosed that the automated technique worked properly on all images from all subjects and was more accurate than the human raters in defining thalamus white and gray matter portions as well as in tissue classification at the external brain edge. In addition, this segmentation technique resulted in an average gray/white matter ratio similar to that reported by post-mortem assessment. The application of the operator-dependent segmentation strategy was extremely time-consuming and the two observers achieved poorly reproducible results. Segmenting brain white and gray matter using information from DT-MRI proved to be an accurate approach with the potential for improving the understanding of the pathophysiology of many neurologic conditions.

Reliability of tissue volumes and their spatial distribution for segmented magnetic resonance images

Before using MRI tissue segmentation in clinical studies as a dependent variable or as a means to correct functional data for differential tissue contribution, we must first establish the volume reliability and spatial distribution reproducibility of the segmentation method. Although several reports of volume reliability can be found in the literature, there are no articles assessing the reproducibility of the spatial distribution of tissue. In this report, we examine the validity, volume reliability, and spatial distribution reproducibility for our K-means cluster segmentation. Validation was examined by classifying gray matter, white matter, and CSF on images constructed using an MRI simulator and digital brain phantom, with percentage volume differences of less than 5% and spatial distribution overlaps greater than 0.94 (1.0 is perfect). We also segmented repeat scan MRIs from 10 healthy subjects, with intraclass correlation coefficients greater than 0.92 for cortical gray matter, white matter, sulcal CSF, and ventricular CSF. The original scans were also coregistered to the repeat scan of the same subject, and the spatial overlap for each tissue was then computed. Our overlaps ranged from 0.75 to 0.86 for these tissues. Our results support the use of K-means cluster segmentation, and the use of segmented structural MRIs to guide the analysis of functional and other images.

Magnetic resonance image tissue classification using a partial volume model

We describe a sequence of low-level operations to isolate and classify brain tissue within T1-weighted magnetic resonance images (MRI). Our method first removes nonbrain tissue using a combination of anisotropic diffusion filtering, edge detection, and mathematical morphology. We compensate for image nonuniformities due to magnetic field inhomogeneities by fitting a tricubic B-spline gain field to local estimates of the image nonuniformity spaced throughout the MRI volume. The local estimates are computed by fitting a partial volume tissue measurement model to histograms of neighborhoods about each estimate point. The measurement model uses mean tissue intensity and noise variance values computed from the global image and a multiplicative bias parameter that is estimated for each region during the histogram fit. Voxels in the intensity-normalized image are then classified into six tissue types using a maximum a posteriori classifier. This classifier combines the partial volume tissue measurement model with a Gibbs prior that models the spatial properties of the brain. We validate each stage of our algorithm on real and phantom data. Using data from the 20 normal MRI brain data sets of the Internet Brain Segmentation Repository, our method achieved average kappa indices of kappa = 0.746 +/- 0.114 for gray matter (GM) and kappa = 0.798 +/- 0.089 for white matter (WM) compared to expert labeled data. Our method achieved average kappa indices kappa = 0.893 +/- 0.041 for GM and kappa = 0.928 +/- 0.039 for WM compared to the ground truth labeling on 12 volumes from the Montreal Neurological Institute's BrainWeb phantom.

Volume distribution of cerebrospinal fluid using multispectral MR imaging

The goal of this study was to design a reliable method to quantify and visualize the anatomical distribution of cerebrospinal fluid (CSF) intracranially. The method should be clinically applicable and based on multispectral analysis of three-dimensional (3D) magnetic resonance images. T1-weighted, T2-weighted and proton density-weighted fast 3D gradient pulse sequences were used to form high resolution multispectral 3D images of the entire head. Training on single 2D slices, the Mahalanobis distances between the resulting multivariate tissue-specific densities were studied as functions of the feature vector composition and dimension. Multispectral analysis was applied to the images of four human brains. One feature vector with three components gave CSF volumes that were in the normal range and corresponding anatomical distributions that largely agreed with general anatomical knowledge. The exception was CSF missing around the basal parts of the brain due to signal artifacts. These artifacts were almost certainly due to the coil effect and magnetic field inhomogeneities induced by the imaged head. Such misclassifications could probably be reduced by bias field estimation and proper image restoration. Most CSF voxels formed large connected components that were found automatically, so the manual post-processing of the classified 3D image to locate CSF voxels was moderate. It is concluded that some of the fast, high resolution 3D gradient echo pulse sequences that have become available on conventional clinical scanners can be used to obtain good estimates of brain cerebrospinal fluid anatomical distribution and volume.

Intimate combination of low- and high-resolution image data: I. Real-space PET and (1)H(2)O MRI, PETAMRI

Two different types of (co-registered) images of the same slice of tissue will generally have different spatial resolutions. The judicious pixel-by-pixel combination of their data can be accomplished to yield a single image exhibiting properties of both. Here, axial (18)FDG PET and (1)H(2)O MR images of the human brain are used as the low- and high-resolution members of the pair. A color scale is necessary in order to provide for separate intensity parameters from the two image types. However, not all color scales can accommodate this separability. The HSV color model allows one to choose a color scale in which the intensity of the low-resolution image type is coded as hue, while that of the high-resolution type is coded as value, a reasonably independent parameter. Furthermore, the high-resolution image must have high contrast and be quantitative in the same sense as the low-resolution image almost always is. Here, relaxographic MR images (naturally segmented quantitative (1)H(2)O spin-density components) are used. Their essentially complete contrast serves to effect an apparent editing function when encoded as the value of the color scale. Thus, the combination of (18)FDG PET images with gray-matter (GM) relaxographic (1)H(2)O images produces visually "GM-edited" (18)FDG PETAMR (positron emission tomography and magnetic resonance) images. These exhibit the high sensitivity to tracer amounts characteristic of PET along with the high spatial resolution of (1)H(2)O MRI. At the same time, however, they retain the complete quantitative measures of each of their basis images. Magn Reson Med 42:345-360, 1999. Published 1999 Wiley-Liss, Inc.

Quantification of white matter and gray matter volumes from three-dimensional magnetic resonance volume studies using fuzzy classifiers

We accurately measured white matter (WM) and gray matter (GM) from three-dimensional (3D) volume studies, using a fuzzy classification technique. The new segmentation method is a modification of a recently published method developed for T1 parametric images. 3D MR images were transformed into pseudo forms of T1 parametric images and segmented into WM and GM voxel fraction images with a set of standardized fuzzy classifiers. This segmentation method was validated with synthesized 3D MR images as phantoms. These phantoms were developed from cryosectioned human brain images located in the superior, middle, and inferior regions of the cerebrum. Phantom volume measurements revealed that, generally, the difference between measured and actual volumes was less than 3% for 1.5-mm simulated brain slices. The average cerebral GM/WM ratio calculated from 3D MR studies in four subjects was 1.77, which compared favorably with the estimate of 1.67 derived from anatomical data. Results indicate that this is an accurate and rapid method for quantifying WM and GM from Tl-weighted 3D volume studies.

Volumetric analysis of white matter, gray matter, and CSF using fractional volume analysis

Quantitative cerebral tissue volumes may be useful for an objective assessment of pathological changes in brain. Accurate determination of tissue volumes is complicated, however, by the partial volume averaging (PVA) effect. We have, therefore, developed a new pulse sequence that minimizes the PVA through the use of inversion-recovery (IR) and double inversion-recovery (DIR) techniques. This pulse sequence simultaneously acquires four different sets of images to provide the necessary information for volumetric analysis and reduces potential spatial misregistration of images due to patient motion. The image sets acquired from the proposed pulse sequence are 1) gray matter visible, 2) white matter visible, 3) FLAIR, and 4) fast spin-echo proton-density weighted images. An algorithm has been implemented to correct for differential T1-weighting and for tissue quantitation.

Normal brain volume measurements using multispectral MRI segmentation

The performance of a supervised k-nearest neighbor (kNN) classifier and a semisupervised fuzzy c-means (SFCM) clustering segmentation method are evaluated for reproducible measurement of the volumes of normal brain tissues and cerebrospinal fluid. The stability of the two segmentation methods is evaluated for (a) operator selection of training data, (b) reproducibility during repeat imaging sessions to determine any variations in the sensor performance over time, (c) variations in the measured volumes between different subjects, and (d) variability with different imaging parameters. The variations were found to be dependent on the type of measured tissue and the operator performing the segmentations. The variability during repeat imaging sessions for the SFCM method was < 3%. The absolute volumes of the brain matter and cerebrospinal fluid between subjects varied quite large, ranging from 9% to 13%. The intraobserver and interobserver reproducibility for SFCM were < 4% for the soft tissues and 6% for cerebrospinal fluid. The corresponding results for the kNN segmentation method were higher compared to the SFCM method.

Unsupervised, automated segmentation of the normal brain using a multispectral relaxometric magnetic resonance approach

The purpose of this study was the development and testing of a method for unsupervised, automated brain segmentation. Two spin-echo sequences were used to obtain relaxation rates and proton-density maps from 1.5 T MR studies, with two axial data sets including the entire brain. Fifty normal subjects (age range, 16 to 76 years) were studied. A Three-dimensional (3D) spectrum of the tissue voxels was used for automatic segmentation of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) and for calculation of their volumes. Accuracy and reproducibility were tested with a three-compartment phantom simulating GM, WM, and CSF. In the normal subjects, a significant decrease of GM fractional volume and increased CSF volume with age were observed (P < 0.0001), with no significant changes in WM. This multispectral segmentation method permits reproducible, operator-independent volumetric measurements.

Reliability and exactness of MRI-based volumetry: a phantom study

This study investigated the influence of slice thickness, section orientation, contrast, shape, and sequence type on the exactness of MRI-based volumetry. Ni-doped agarose gel phantoms (4 to 46 ml) were scanned with a T1-weighted three-dimensional Fourier transform (FT) fast low-angle shot (FLASH) and a multiecho two-dimensional FT-Turbo spin-echo (SE) sequence. After segmentation with a three-dimensional region-growing algorithm, the geometric volume was measured considering the partial volume effect. The variability coefficient (Pearson) was .7%. The volumetric error increased with slice thickness, depending on the size and form of the object. Cross sections resulted in smaller error than longitudinal sections (finger-shaped phantoms, nonisotropic image data). Three-dimensional FT imaging. Results of slice thickness and section orientation experiments can be explained by the partial volume effect Higher errors in two-dimensional FT imaging were caused by object movements between two interleaved acquisitions. The study shows a considerable influence of the imaging parameters on the exactness, which depends on size and form of the structure of interest.

Quantitative 1H spectroscopic imaging of human brain at 4.1 T using image segmentation

Metabolic differences in the content of N-acetylaspartate (NAA), creatinine (CR), and choline (CH) in cerebral gray and white matter can complicate the interpretation of 1H spectroscopic images. To account for these variations, the gray- and white-matter content of each voxel must be known. To provide these data, a T1-based image segmentation scheme was implemented at 4.1 T. The tissue composition of each voxel was determined using the point-spread function of the spectroscopic imaging acquisition and the segmented anatomical image. Pure gray- and white-matter values for CR/NAA and CH/NAA, and the content of CR, CH, and NAA, were determined using a linear-regression analysis of 984 voxels acquired from 10 subjects using white-matter CR as an internal standard. This information was used to establish means and confidence intervals for CR/NAA and CH/NAA from a voxel of arbitrary tissue composition. Using a single-tailed t test, the extent and locations of the metabolic abnormalities (P < 0.05) in a patient with multiple sclerosis were identified.

MR-based correction of brain PET measurements for heterogeneous gray matter radioactivity distribution

Partial volume and mixed tissue sampling errors can cause significant inaccuracy in quantitative positron emission tomographic (PET) measurements. We previously described a method of correcting PET data for the effects of partial volume averaging on gray matter (GM) quantitation; however, this method may incompletely correct GM structures when local tissue concentrations are highly heterogeneous. We have extended this three-compartment algorithm to include a fourth compartment: a GM volume of interest (VOI) that can be delineated on magnetic resonance (MR) imaging. Computer simulations of PET images created from human MR data demonstrated errors of up to 120% in assigned activity values in small brain structures in uncorrected data. Four-compartment correction achieved full recovery of a wide range of coded activity in GM VOIs such as the amygdala, caudate, and thalamus. Further validation was performed in an agarose brain phantom in actual PET acquisitions. Implementation of this partial volume correction approach in [18F]fluorodeoxyglucose and [11C]-carfentanil PET data acquired in a healthy elderly human subject was also performed. This newly developed MR-based partial volume correction algorithm permits the accurate determination of the true radioactivity concentration in specific structures that can be defined by MR by accounting for the influence of heterogeneity of GM radioactivity.

Quantification of white matter and gray matter volumes from T1 parametric images using fuzzy classifiers

White matter (WM) and gray matter (GM) were accurately measured using a technique based on a single standardized fuzzy classifier (FC) for each tissue. Fuzzy classifier development was based on experts' visual assessments of WM and GM boundaries from a set of T1 parametric MR images. The fuzzy classifier method's accuracy was validated and optimized by a set of T1 phantom images that were based on hand-detailed human brain cryosection images. Nine sets of axial T1 images of varying thickness equally distributed throughout the brain were simulated. All T1 data sets were mapped to the standardized FCs and rapidly segmented into WM and GM voxel fraction images. Resulting volumes revealed that, in most cases, the difference between measured and actual volumes was less than 5%. This was consistent throughout most of the brain, and as expected, the accuracy improved to generally less than 2% for the 1-mm simulated brain slices.

Local histogram correction of MRI spatially dependent image pixel intensity nonuniformity

We describe a computationally straightforward post-hoc statistical method of correcting spatially dependent image pixel intensity nonuniformity based on differences in local tissue intensity distributions. Pixel intensity domains for the various tissues of the composite image are identified and compared to the distributions of local samples. The nonuniformity correction is calculated as the difference of the local sample median from the composite sample median for the tissue class most represented by the sample. The median was chosen to reduce the effecters on determining the sample statistic and to allow a sample size small enough to accurately estimate the spatial variance of the image intensity nonuniformity. The method was designed for application to two-dimensional images. Simulations were used to estimate optimal conditions of local histogram kernel size and to test the accuracy of the method under known spatially dependent nonuniformities. The method was also applied to correct a phantom image and cerebral MRIs from 15 healthy subjects. Results show that the method accurately models simulated spatially dependent image intensity differences. Further analysis of clinical MR data showed that the variance of pixel intensities within the cerebral MRI slices and the variance of slice volumes within individuals were significantly reduced after nonuniformity correction. Improved brain-cerebrospinal fluid segmentation was also obtained. The method significantly reduced the variance of slice volumes within individuals, whether it was applied to the native images or images edited to remove nonbrain tissues. This statistical method was well behaved under the assumptions and the images tested. The general utility of the method was not determined, but conditions for testing the method under a variety of imaging sequences is discussed. We believe that this algorithm can serve as a method for improving MR image segmentation for clinical and research applications.

Knowledge-based interpretation of MR brain images

The authors have developed a method for fully automated segmentation and labeling of 17 neuroanatomic structures such as thalamus, caudate nucleus, ventricular system, etc. in magnetic resonance (MR) brain images. The authors' method is based on a hypothesize-and-verify principle and uses a genetic algorithm (GA) optimization technique to generate and evaluate image interpretation hypotheses in a feedback loop. The authors' method was trained in 20 individual T1-weighted MR images. Observer-defined contours of neuroanatomic structures were used as a priori knowledge. The method's performance was validated in eight MR images by comparison to observer-defined independent standards. The GA-based image interpretation method correctly interpreted neuroanatomic structures in all images from the test set. Computer-identified and observer-defined neuroanatomic structure areas correlated very well (r=0.99, y=0,95x-2.1). Border positioning errors were small, with a root mean square (rms) border positioning error of 1.5+/-0.6 pixels. The authors' GA-based image interpretation method represents a novel approach to image interpretation and has been shown to produce accurate labeling of neuroanatomic structures in a set of MR brain images.

MR image segmentation using vector decomposition and probability techniques: a general model and its application to dual-echo images

A general model is developed for segmenting magnetic resonance images using vector decomposition and probability techniques. Each voxel is assigned fractional volumes of q tissues from p differently weighted images (q < or = p + 1) in the presence of partial-volume mixing, random noise, and other tissues. Compared with the eigenimage method, fewer differently weighted images are needed for segmenting the q tissues, and the contrast-to-noise ratio in the calculated fractional volumes is improved. The model can produce composite tissue-type images similar to that of the probability methods, by comparing the fractional volumes assigned to different tissues on each voxel. A three-tissue (p = 2, q = 3) model is illustrated for segmenting three tissues from dual-echo images. It provides statistical analysis to the algebraic method. A three-compartment phantom is segmented for validation. Two clinical examples are presented.

Principal component analysis and the scaled subprofile model compared to intersubject averaging and statistical parametric mapping: I. "Functional connectivity" of the human motor system studied with [15O]water PET

Using [15O]water PET and a previously well studied motor activation task, repetitive finger-to-thumb opposition, we compared the spatial activation patterns produced by (1) global normalization and intersubject averaging of paired-image subtractions, (2) the mean differences of ANCOVA-adjusted voxels in Statistical Parametric Mapping, (3) ANCOVA-adjusted voxels followed by principal component analysis (PCA), (4) ANCOVA-adjustment of mean image volumes (mean over subjects at each time point) followed by F-masking and PCA, and (5) PCA with Scaled Subprofile Model pre- and postprocessing. All data analysis techniques identified large positive focal activations in the contralateral sensorimotor cortex and ipsilateral cerebellar cortex, with varying levels of activation in other parts of the motor system, e.g., supplementary motor area, thalamus, putamen; techniques 1-4 also produced extensive negative areas. The activation signal of interest constitutes a very small fraction of the total nonrandom signal in the original dataset, and the exact choice of data preprocessing steps together with a particular analysis procedure have a significant impact on the identification and relative levels of activated regions. The challenge for the future is to identify those preprocessing algorithms and data analysis models that reproducibly optimize the identification and quantification of higher-order sensorimotor and cognitive responses.

Segmentation of small structures in MR images: semiautomated tissue hydration measurement

Segmentation of small anatomic structures in noisy magnetic resonance (MR) images is inherently challenging because the edge information is contained in the same high-frequency image component as the noise. The authors overcame this obstacle in the analysis of the sural nerve in the ankle by processing images to reduce noise and extracting edges with an edge detection algorithm less sensitive to noise. Anatomic accuracy of the segmentation was confirmed by a neuroradiologist. A nerve hydration coefficient was determined from the signal intensity of the nerve in these segmented images. These semiautomated measurements of hydration agreed closely with those obtained with a previously described manual method (n = 44, P = .76). Each image in the study was analyzed identically, with no modification of the computer algorithm parameters. The data suggest that this robust method may be useful in a multicenter evaluation of diabetes treatment protocols.

Rapid automatic brain volumetry on the basis of multispectral 3D MR imaging data on personal computers

Fast magnetic resonance imaging sequences are an excellent basis for the volumetry of the human brain. We present a concept for an automatic three-dimensional (3D) segmentation and volumetry which provides rapid image processing along with minimal requirements of manual interaction on low-end hardware platforms. The concept is based on standard image segmentation techniques and provides for the determination of the total brain tissue and cerebrospinal fluid volumes from the input data base, a T1- and a T2-weighted 3D image data cube. We used a 3D MPRAGE sequence for the T1- and a 3D PSIF sequence for the T2-weighted images; both 3D image data cubes consisted of 128 sagittal image slices with a voxel size of 1.0 x 1.0 x 1.2 mm3. The segmentation and volumetry tool was applied to clinical image data of volunteers and Alzheimer patients in order to prove its stability.

Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images

Presents a new method to segment brain parenchyma and cerebrospinal fluid spaces automatically in routine axial spin echo multispectral MR images. The algorithm simultaneously incorporates information about anatomical boundaries (shape) and tissue signature (grey scale) using a priori knowledge. The head and brain are divided into four regions and seven different tissue types. Each tissue type c is modeled by a multivariate Gaussian distribution N(mu(c),Sigma(c)). Each region is associated with a finite mixture density corresponding to its constituent tissue types. Initial estimates of tissue parameters {mu(c),Sigma(c )}(c=1,...,7) are obtained from k-means clustering of a single slice used for training. The first algorithmic step uses the EM-algorithm for adjusting the initial tissue parameter estimates to the MR data of new patients. The second step uses a recently developed model of dynamic contours to detect three simply closed nonintersecting curves in the plane, constituting the arachnoid/dura mater boundary of the brain, the border between the subarachnoid space and brain parenchyma, and the inner border of the parenchyma toward the lateral ventricles. The model, which is formulated by energy functions in a Bayesian framework, incorporates a priori knowledge, smoothness constraints, and updated tissue type parameters. Satisfactory maximum a posteriori probability estimates of the closed contour curves defined by the model were found using simulated annealing.

Statistical models of partial volume effect

Statistical models of partial volume effect for systems with various types of noise or pixel value distributions are developed and probability density functions are derived. The models assume either Gaussian system sampling noise or intrinsic material variances with Gaussian or Poisson statistics. In particular, a material can be viewed as having a distinct value that has been corrupted by additive noise either before or after partial volume mixing, or the material could have nondistinct values with a Poisson distribution as might be the case in nuclear medicine images. General forms of the probability density functions are presented for the N material cases and particular forms for two- and three-material cases are derived. These models are incorporated into finite mixture densities in order to more accurately model the distribution of image pixel values. Examples are presented using simulated histograms to demonstrate the efficacy of the models for quantification. Modeling of partial volume effect is shown to be useful when one of the materials is present in images mainly as a pixel component.

Dual-echo MRI segmentation using vector decomposition and probability techniques: a two-tissue model

We combined a vector decomposition technique with Gaussian probability thresholding in feature space to segment normal brain tissues, tumors, or other abnormalities on dual-echo MR images. The vector decomposition technique assigns to each voxel a fractional volume for each of two tissues. A probability threshold, based on an assumed Gaussian probability density function describing random noise, isolates a region in feature space for fractional volume calculation that minimizes contamination from other tissues. The calculated fractional volumes are unbiased estimates of the true fractional volumes. The contrast-to-noise ratio (CNR) between tissues on the segmented images is the same as the Euclidean norm of CNRs in the original images. The method is capable of segmenting more than two tissues from a set of dual-echo images by sequentially analyzing different pairs of tissues. The model is analyzed mathematically and in experiments with a phantom. Two clinical examples are presented.