76-Space analysis of grey matter diffusivity: Methods and applications
Introduction
Early detection and diagnosis of neurodegenerative and neurological diseases (e.g., Alzheimer's disease (AD) and Creutzfeldt–Jakob disease (CJD)) would provide clues to the underlying neuropathology of these diseases and would enable more effective treatment of patients (Nestor et al., 2004). Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) provide insights into the nature and degree of gray and white matter injury that occurs in neurological diseases and shed light on early detection and diagnosis of devastating neurological diseases. DTI is in wide use for the investigation of white matter (WM) abnormality associated with various progressive neuropathologies (Stahl et al., 2003, Horsfield and Jones, 2002, Sundgren et al., 2004, Moseley, 2002) because it yields quantitative measures reflecting the integrity of WM fiber tracts. These measures rely on the directionally constrained (‘anisotropic’) water diffusion in human brain WM related to the dense bundling of myelin sheaths. Although these DTI studies on WM are useful in investigation of the abnormality occurring on fiber pathways connecting remote computation centers of various Gray Matter (GM) regions, many neurodegenerative diseases, including Alzheimer's disease, primarily involve the GM (Horsfield and Jones, 2002, Brun and Englund, 1986). Fortunately, water diffusivity in GM is nearly isotropic, and scalar diffusivity quantified by the apparent diffusion coefficient (ADC) reflects pathologic change in a number of neurodegenerative diseases (Sundgren et al., 2004).
Investigation of brain diffusivity in AD over the past decade (Hanuy et al., 1998, Rose et al., 2000) has culminated in recent reports suggesting that DWI may help predict progression to AD in patients with Mild cognitive impairment (MCI) (Kantarci et al., 2005). In addition, DWI has been shown to be useful in the investigation and diagnosis of vascular dementia (Choi et al., 2000), post-procedural cognitive impairment following cardiac surgery (Restrepo et al., 2002) and in Creutzfeldt–Jakob disease (CJD) (Young et al., 2005). In CJD, the combination of diffusion-weighted MRI and FLAIR demonstrated over 90% sensitivity, specificity and accuracy in differentiation of CJD from other dementias, with high intra- and inter-reader reliability (Young et al., 2005). Recently, the utility of DWI/DTI has also been investigated in many other neurodegenerative and neuropsychiatric diseases, including Parkinson's disease (Adachi et al., 1999), multiple sclerosis (Cercignani et al., 2000) and schizophrenia (Kalus et al., 2005).
To date, many applications of DWI for quantification of GM diffusivity for clinical diagnosis of neurological and neurodegenerative diseases have been done via manual region of interests (ROI) analysis (Bilgili and Unal, 2004). To manually parcellate the entire brain cortex into well-established cytoarchitectonic regions, such as the 76 spaces employed in this paper (Table 1), would require a well-trained neuroanatomist days or weeks. This makes it very difficult, if not impossible, to perform statistical studies of the GM diffusivity over large datasets of subjects. In this paper, we report a novel computational framework which allows automated 76-space analysis of GM diffusivity throughout the entire cortex of brains of normal volunteers and patients suffering from neurodegenerative disease. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data normal volunteers and from patients with Creutzfeldt–Jakob disease. A brief description of the framework is referred to Liu et al., 2005a, Liu et al., 2005b.
Section snippets
Overview
The computational framework of 76-space analysis of GM diffusivity is composed of seven steps, as summarized in Fig. 1. The first two steps automatically segment the spoiled gradient echo (SPGR) brain image into distinct tissues of CSF, GM and WM (Liu et al., 2004a), and the GM is further parcellated into 76 fine-detailed neuroanatomic regions (Table 1) using the high-dimensional hybrid registration method (Liu et al., 2003, Liu et al., 2004a, Liu et al., 2004b, Liu et al., 2005a, Liu et al.,
Validation
In this section, we describe a series of experiments designed to evaluate and validate the computational components of the framework of 76-space analysis. Both computerized and manual labeling results are used to demonstrate the performance of the proposed method in measuring GM diffusivity.
Application
As discussed, quantitative diffusivity analysis of neuroanatomic structures has important implications in study of normal aging and neurodegenerative disease. The authors have an approved IRB protocol: Clinical Image Based Computer Aided Detection and Diagnosis of Neurodegeneration and other Neurological Disease, from the institutional review board of BWH, Partners Human Research Committee. The approved IRB enables us retrospectively retrieve structural MRI, DTI and DWI patient and control
Methods
We have demonstrated that more accurate measurement of GM diffusivity could be achieved by removing heterogeneous tissues via multichannel fusion. However, a fundamental assumption in this paper is that we can accurately measure GM diffusivity although we use only part of GM tissue, that is, the intersection of the GM obtained in SPGR space and that in DWI/DTI space. One could attempt to make the GM maps in two spaces overlap as much as possible, e.g., via the reduction of geometric distortion
Conclusion
We proposed a new computational framework for automated 76-space analysis of GM diffusivity for study of normal brains and neurodegenerative diseases. In this framework, structural information in SPGR image and diffusivity information in DWI/DTI images are integrated and explored through three enabling technologies: high-dimensional hybrid registration, tissue segmentation based on DWI/DTI data and multichannel fusion. The framework has been applied to study data from normal volunteers and CJD
Acknowledgments
This research work is supported by a grant to Dr. Wong from the Harvard Center for Neurodegeneration and Repair (HCNR), Harvard Medical School. The normal control datasets are from the NIH sponsored NAMIC (National Alliance of Medical Image Computing) data-repository and are provided by the Laboratory of Neuroscience, Department of Psychiatry, Boston VA Healthcare System and Harvard Medical School, which is supported by following grants: NIMH R01 MH50740 (Shenton), NIH K05 MH01110 (Shenton),
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