MRI & Neuroimaging
The brain is permeated by a network of nerve fibers, including major pathways that connect distant parts of the brain. White matter fiber tractography by means of diffusion tensor MRI is the only non-invasive method that can provide estimates of this structural connectivity. In tractography estimates of fiber orientation are obtained from diffusion tensor imaging (DTI) in each voxel, and white matter paths are constructed that connect brain regions. This is possible by starting from one region and following the fiber orientation vectors voxel by voxel. The resulting paths are interpreted as representations of the underlying white matter fiber system. Tractography algorithms are in general sensitive to noise, and image artifacts. However, the conventional DTI data acquisition technique used for fiber tractography is based on echo planar imaging (EPI), which suffers from severe geometric distortions due to B0 inhomogeneities, and eddy-current artifacts. Turboprop-DTI (see Propeller MRI below) is relatively immune to such artifacts. Our goal in this project is to investigate the use of Turboprop-DTI in tractography applications. We have recently shown that Turboprop-DTI provides anatomically correct, undistorted fiber-tracts throughout the brain.
PROPELLER imaging is an MRI data acquisition and reconstruction technique with greatly reduced sensitivity to various sources of image artifacts (geometric distortions related to B0-inhomogeneities and eddy currents, motion artifacts). PROPELLER data acquisitions follow a multiple-shot fast spin-echo (FSE) approach in which we acquire several k-space lines in each TR, forming a blade that we then rotate around its center and repeat acquisition to cover k-space (right). However, the imaging time in PROPELLER MRI is considerably longer than in acquisition techniques such as echo-planar imaging (EPI). In the most recent form of PROPELLER imaging, named Turboprop, data acquisition is accelerated by reading out multiple lines of k-space during the spin-echo produced after each 180° pulse, similar to the gradient and spin-echo (GRASE) sequence. In this project we are investigating PROPELLER MRI data acquisition and image reconstruction methods. We have recently studied the effects of k-space under-sampling on the reconstructed PROPELLER images as an alternative method to accelerate PROPELLER MRI. We have shown that the sampling pattern shown at left is both time-efficient (reduces acquisition time by 50% compared to that of a fully sampled case) and results in images with very few artifacts.
Diffusion tensor imaging (DTI), is a noninvasive imaging technique that can be used to probe, in vivo, the intrinsic diffusion properties of deep tissues. The eigenvectors of the diffusion tensor D define the local fiber tract direction field and the eigenvalues are the diffusivities along these directions. Moreover, rotationally invariant scalar quantities can be derived from D, that describe the diffusion characteristics of the tissue. The most commonly used are the trace of the tensor, which measures mean diffusivity, and fractional anisotropy (FA), which characterizes the anisotropy of the fiber structure. DTI has been applied in several studies to infer the microstructural characteristics of the brain, in normal, as well as, in disease conditions, such as cerebral ischemia, acute stroke, multiple sclerosis, schizophrenia and traumatic brain injury. Precision in the estimation of the elements of D, and consequently of the scalar quantities derived from it, is crucial for many DTI studies. For that reason, we are giving special attention to constructing acquisition schemes that provide optimal estimates of D.
Neuroimaging methods, such as fMRI and PET, have become an essential tool in neuroscience. The problem of producing images of brain function is a statistical data mining problem. MIRC faculty are developing new solutions to this problem using the tools of machine learning. For example, we have developed kernel methods based on the generalized likelihood ratio test, using relevance vector machines (RVM) and reversible jump Markov chain Monte Carlo (RJMCMC) methods. Dr. Wernick is involved in a commercial application of neuroimaging, in which this imaging is being used to help pharmaceutical companies narrow their searches for promising new drugs.
Alzheimer's disease (AD) affects 5-10% of the population over the age of 65 and an even higher percentage of the population over 85. The earliest manifestation of AD is typically an impairment of recent memory function and attention. This deficit is followed by a deterioration of language skills, visuospatial orientation, abstract thinking and judgment, and alterations of personality. Even though there are behavioral clinical criteria to diagnose possible or probable AD, the definitive diagnosis is based on post-mortem histopathological confirmation. The neuronal death that accompanies AD is non-reversible. Therefore, possible treatments may benefit the most only those patients who are diagnosed early and have had little loss of neurons. Thus, there is a great need to develop strategies to detect AD at the very early stages, prior to the development of significant impairments of cognition and behavior. Our goal in this project is to develop a non-invasive method to identify the signs of early damage due to AD.
Loss of cell membrane structural integrity typically results from various modes of physical injury to tissues. If membrane resealing does not occur, cellular necrosis will take place within hours. We have shown that electric fields of the magnitude and duration likely to occur in electrical injury result in skeletal muscle electroporation and subsequent tissue necrosis. The goal of this project is to use MR imaging techniques to visualize the effects of electrical injury in muscle. We have shown that electrical injury leads to edema and increased T2 values. Therefore, T2-weighted imaging can be used to localize the injury, and estimate the volume of injured tissue. Images above show a rat leg 60 minutes (A) and 180 minutes (B) after electrical injury.