Surgery – Diagnostic testing – Detecting nuclear – electromagnetic – or ultrasonic radiation
Reexamination Certificate
1999-08-24
2002-04-02
Lateef, Marvin M. (Department: 3737)
Surgery
Diagnostic testing
Detecting nuclear, electromagnetic, or ultrasonic radiation
C382S128000, C128S922000
Reexamination Certificate
active
06366797
ABSTRACT:
BACKGROUND OF THE INVENTION
The present invention relates to medical image analysis arts. It finds particular application to a method and system for analyzing brain volume from magnetic resonance images for measuring brain atrophy and quantitative multiple sclerosis disease evaluation and will be described with particular reference thereto. Of course, it will be appreciated that the present invention will also find application to the analysis of medical images obtained from other medical imaging systems capable of imaging the brain, and for quantitative evaluation of other conditions which lead to neurodegeneration or axonal damage.
Magnetic resonance imaging (MRI) offers high contrast images which are far superior to those of other imaging modalities in terms of anatomical detail. In addition, flexible imaging sequences and choices of image orientation allow for detailed analysis of the brain, and for computer methods which can accurately segment the brain into cerebral spinal fluid (CSF), gray matter and white matter compartments.
To date, a number of quantitative MRI methods have reported measurements of cerebral spinal fluid, gray and white matter volumes in brain. Three general methods have been employed: (1) operator directed outlining of a region of interest (ROI); (2) special sequences to enhance cerebral spinal fluid and suppress brain matter signals; and (3) segmentation routines which utilize either automatic boundary outlining or threshold determinations.
Although tracing a region of interest can be quick and simple, it is the most operator intensive, and requires extensive training as well as a detailed knowledge of neuroanatomy. Special MRI sequences have been designed to selectively enhance the cerebral spinal fluid signal for volume determination, but it is not clear how partial volume averaging is accounted for with those sequences. Moreover, the MRI images generated are not suitable for standard radiological interpretation. Semi-automatic boundary outlining or thresholding routines are usually time consuming, and require the operator to select “seed” pixel values to choose numerical thresholds, or to sample representative pixel intensity values for brain and cerebral spinal fluid segmentation. The requirement for operator interaction leads to variable results due to differences in human perception.
The segmentation of the brain in magnetic resonance (MR) images is essential for quantitative analysis in clinical applications. Highly accurate and precise segmentation is required for serial studies which depend on repeated measurements over time for the detection of small changes. For example, for treatment evaluation in multiple sclerosis (MS), quantification of brain atrophy and lesion volumes in MR images is often used in conjunction with clinical evaluation to provide more objective measures of MS burden. Most current methods of quantification incorporate subjective techniques such as manual counting of lesions in 2D slices, or manual tracing for calculating areas and volumes. These segmentation methods may yield intra- and inter-operator variability rates which are higher than the expected changes to be measured. Therefore, computer-aided approaches have been designed to improve reliability. Brain segmentation is essential to determination of the brain volume. It is also important in surface-based registration since the segmented brain surface can be used for alignment of serial images acquired at different times. For these reasons, an automated segmentation technique is necessary to fully exploit the quantitative capabilities of MR imaging.
Automated segmentation of brain structures and tissues in MR is complicated by several factors. The overlap of gray level characteristics of different tissue-types precludes the use of global thresholding as a stand-alone segmentation technique. Typically, there are also inter- and intra-slice nonuniform intensity variations due to RF and/or receiver inhomogeneities which must be handled. Partial volume effects, due to the averaging of signals from different tissues sampled in the same voxel, result in voxels which can not be classified as any one particular type. For highest possible accuracy, an ideal segmentation technique should take all three of these factors into account. The segmentation should also be flexible in order to accommodate images from a variety of MR pulse sequences. Since the relative intensities of tissues vary according to each particular pulse sequence, a purely intensity-based strategy may not be flexible in this regard.
Techniques for “automated” segmentation of brain MR images have been developed to address these problems and to minimize manual interaction. Generic algorithms, such as multi-spectral clustering or thresholding combined with connectivity operations, still usually require the user to select appropriate thresholds or training sets to identify tissues, which can lead to significant variability in the results. Even with methods in which the parameters are determined automatically, anatomic structures connected to the brain and of similar intensity pose problems for thresholding/connectivity-based algorithms of this type, and often require manual editing at the end. Knowledge-based methods attempt to solve these problems by using anatomic information to drive the segmentation to fit some a priori model of the brain. However, the model assumptions are often overly restrictive, especially since there is significant diversity in anatomic features of the brain in different pathological conditions, and considerable differences in the intensity characteristics arising from different MR image acquisition techniques.
The present invention provides a new and unique method and system for fully automated brain image analysis, which solves the above problems and others for the evaluation of multiple sclerosis and other conditions that lead to neurodegeneration and/or axonal damage.
SUMMARY OF THE INVENTION
A method of analyzing magnetic resonance images of a brain to determine the severity and progression of a medical condition is provided. Magnetic resonance images of a brain are provided. A brain surface contour is identified from the magnetic resonance image data. A total volume within the brain surface contour is calculated. A brain volume within the brain surface contour is then determined. The severity of a medical condition is evaluated based on a ratio of the brain volume to the total contour volume.
In accordance with another aspect of the present invention, a method of determining brain atrophy from image data of a brain obtained from a magnetic resonance imaging system is provided. The image data represents brain tissue and non-brain regions of a subject. A surface contour of the brain is determined in the image data. A total volume is determined within the surface contour and a brain volume is determined that is the total contour volume less a volume of the non-brain regions within the surface contour. A brain parenchymal fraction is generated which is the brain volume normalized by the total contour volume. Brain atrophy is then determined according to the brain parenchymal fraction.
One advantage of the present invention is that it provides a reproducible and reliable measurement of brain atrophy by eliminating the subjective analysis of brain image data.
Another advantage of the present invention is that it provides a reliable measurement of brain volume changes over time such that an assessment of brain atrophy and the progression of multiple sclerosis or other condition which leads to neurodegeneration or axonal damage can be made. In this regard, the brain volume from the image data is analyzed in three-dimensions and normalized, which partially eliminates errors in volume determination caused by patient repositioning.
The normalization results in an additional advantange for cross-sectional studies. Since the normalized brain volume is within a constant range for healthy individuals, the present invention can be also be used to measure brain atrophy in individuals at a single
Fisher Elizabeth
Rudick Richard A.
Calfee Halter & Griswold LLP
Lateef Marvin M.
Shaw Shawna J
The Cleveland Clinic Foundation
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