Magnetic resonance diffusion tensor imaging (DTI) is a new technique that can be used to visualize and measure the diffusion of water in brain tissue; it is particularly useful for evaluating white matter abnormalities. In this paper, we review research studies that have applied DTI for the purpose of understanding neuropsychiatric disorders. We begin with a discussion of the principles involved in DTI, followed by a historical overview of magnetic resonance diffusion-weighted imaging and DTI and a brief description of several different methods of image acquisition and quantitative analysis. We then review the application of this technique to clinical populations. We include all studies published in English from January 1996 through March 2002 on this topic, located by searching PubMed and Medline on the key words "diffusion tensor imaging" and "MRI." Finally, we consider potential future uses of DTI, including fiber tracking and surgical planning and follow-up.
BACKGROUND AND PURPOSE: MR diffusion tensor imaging permits detailed visualization of white matter fiber tracts. This technique, unlike T2-weighted imaging, also provides information about fiber direction. We present findings of normal white matter fiber tract anatomy at high resolution obtained by using line scan diffusion tensor imaging.
METHODS: Diffusion tensor images in axial, coronal, and sagittal sections covering the entire brain volume were obtained with line scan diffusion imaging in six healthy volunteers. Images were acquired for b factors 5 and 1000 s/mm(2) at an imaging resolution of 1.7 x 1.7 x 4 mm. For selected regions, images were obtained at a reduced field of view with a spatial resolution of 0.9 x 0.9 x 3 mm. For each pixel, the direction of maximum diffusivity was computed and used to display the course of white matter fibers.
RESULTS: Fiber directions derived from diffusion tensor imaging were consistent with known white matter fiber anatomy. The principal fiber tracts were well observed in all cases. The tracts that were visualized included the following: the arcuate fasciculus; superior and inferior longitudinal fasciculus; uncinate fasciculus; cingulum; external and extreme capsule; internal capsule; corona radiata; auditory and optic radiation; anterior commissure; corpus callosum; pyramidal tract; gracile and cuneatus fasciculus; medial longitudinal fasciculus; rubrospinal, tectospinal, central tegmental, and dorsal trigeminothalamic tract; superior, inferior, and middle cerebellar peduncle; pallidonigral and strionigral fibers; and root fibers of the oculomotor and trigeminal nerve.
CONCLUSION: We obtained a complete set of detailed white matter fiber anatomy maps of the normal brain by means of line scan diffusion tensor imaging at high resolution. Near large bone structures, line scan produces images with minimal susceptibility artifacts.
OBJECTIVE: Disruptions in connectivity between the frontal and temporal lobes may explain some of the symptoms observed in schizophrenia. Conventional magnetic resonance imaging (MRI) studies, however, have not shown compelling evidence for white matter abnormalities, because white matter fiber tracts cannot be visualized by conventional MRI. Diffusion tensor imaging is a relatively new technique that can detect subtle white matter abnormalities in vivo by assessing the degree to which directionally organized fibers have lost their normal integrity. The first three diffusion tensor imaging studies in schizophrenia showed lower anisotropic diffusion, relative to comparison subjects, in whole-brain white matter, prefrontal and temporal white matter, and the corpus callosum, respectively. Here the authors focus on fiber tracts forming temporal-frontal connections.
METHOD: Anisotropic diffusion was assessed in the uncinate fasciculus, the most prominent white matter tract connecting temporal and frontal brain regions, in 15 patients with chronic schizophrenia and 18 normal comparison subjects. A 1.5-T GE Echospeed system was used to acquire 4-mm-thick coronal line-scan diffusion tensor images. Maps of the fractional anisotropy were generated to quantify the water diffusion within the uncinate fasciculus.
RESULTS: Findings revealed a group-by-side interaction for fractional anisotropy and for uncinate fasciculus area, derived from automatic segmentation. The patients with schizophrenia showed a lack of normal left-greater-than-right asymmetry seen in the comparison subjects.
CONCLUSIONS: These findings demonstrate the importance of investigating white matter tracts in vivo in schizophrenia and support the hypothesis of a disruption in the normal pattern of connectivity between temporal and frontal brain regions in schizophrenia.
The vasculature is of utmost importance in neurosurgery. Direct visualization of images acquired with current imaging modalities, however, cannot provide a spatial representation of small vessels. These vessels, and their branches which show considerable variations, are most important in planning and performing neurosurgical procedures. In planning they provide information on where the lesion draws its blood supply and where it drains. During surgery the vessels serve as landmarks and guidelines to the lesion. The more minute the information is, the more precise the navigation and localization of computer guided procedures. Beyond neurosurgery and neurological study, vascular information is also crucial in cardiovascular surgery, diagnosis, and research. This paper addresses the problem of automatic segmentation of complicated curvilinear structures in three-dimensional imagery, with the primary application of segmenting vasculature in magnetic resonance angiography (MRA) images. The method presented is based on recent curve and surface evolution work in the computer vision community which models the object boundary as a manifold that evolves iteratively to minimize an energy criterion. This energy criterion is based both on intensity values in the image and on local smoothness properties of the object boundary, which is the vessel wall in this application. In particular, the method handles curves evolving in 3D, in contrast with previous work that has dealt with curves in 2D and surfaces in 3D. Results are presented on cerebral and aortic MRA data as well as lung computed tomography (CT) data.
A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated. Magn Reson Med 45:323-330, 2001.
This paper describes a unified approach to the detection of point landmarks-whose neighborhoods convey discriminant information-including multidimensional scalar, vector, and higher-order tensor data. The method is based on the interpretation of generalized correlation matrices derived from the gradient of tensor functions, a probabilistic interpretation of point landmarks, and the application of tensor algebra. Results on both synthetic and real tensor data are presented.
A surgical guidance and visualization system is presented, which uniquely integrates capabilities for data analysis and on-line interventional guidance into the setting of interventional MRI. Various pre-operative scans (T1- and T2-weighted MRI, MR angiography, and functional MRI (fMRI)) are fused and automatically aligned with the operating field of the interventional MR system. Both pre-surgical and intra-operative data may be segmented to generate three-dimensional surface models of key anatomical and functional structures. Models are combined in a three-dimensional scene along with reformatted slices that are driven by a tracked surgical device. Thus, pre-operative data augments interventional imaging to expedite tissue characterization and precise localization and targeting. As the surgery progresses, and anatomical changes subsequently reduce the relevance of pre-operative data, interventional data is refreshed for software navigation in true real time. The system has been applied in 45 neurosurgical cases and found to have beneficial utility for planning and guidance. J. Magn. Reson. Imaging 2001;13:967-975.
OBJECTIVE: A major shortcoming of image-guided navigational systems is the use of preoperatively acquired image data, which does not account for intraoperative changes in brain morphology. The occurrence of these surgically induced volumetric deformations ("brain shift") has been well established. Maximal measurements for surface and midline shifts have been reported. There has been no detailed analysis, however, of the changes that occur during surgery. The use of intraoperative magnetic resonance imaging provides a unique opportunity to obtain serial image data and characterize the time course of brain deformations during surgery.
METHODS: The vertically open intraoperative magnetic resonance imaging system (SignaSP, 0.5 T; GE Medical Systems, Milwaukee, WI) permits access to the surgical field and allows multiple intraoperative image updates without the need to move the patient. We developed volumetric display software (the 3D Slicer) that allows quantitative analysis of the degree and direction of brain shift. For 25 patients, four or more intraoperative volumetric image acquisitions were extensively evaluated.
RESULTS: Serial acquisitions allow comprehensive sequential descriptions of the direction and magnitude of intraoperative deformations. Brain shift occurs at various surgical stages and in different regions. Surface shift occurs throughout surgery and is mainly attributable to gravity. Subsurface shift occurs during resection and involves collapse of the resection cavity and intraparenchymal changes that are difficult to model.
CONCLUSION: Brain shift is a continuous dynamic process that evolves differently in distinct brain regions. Therefore, only serial imaging or continuous data acquisition can provide consistently accurate image guidance. Furthermore, only serial intraoperative magnetic resonance imaging provides an accurate basis for the computational analysis of brain deformations, which might lead to an understanding and eventual simulation of brain shift for intraoperative guidance.
In order to enhance 3D image data from magnetic resonance angiography (MRA), a novel method based on the theory of multidimensional adaptive filtering has been developed. The purpose of the technique is to suppress image noise while enhancing important structures. The method is based on local structure estimation using six 3D orientation selective filters, followed by an adaptive filtering step controlled by the local structure information. The complete filtering procedure requires approximately 3 minutes of computational time on a standard workstation for a 256 x 256 x 64 data set. The method has been evaluated using a mathematical vessel model and in vivo MRA data (both phase contrast and time of flight (TOF)). 3D adaptive filtering results in a better delineation of small blood vessels and efficiently reduces the high-frequency noise. Depending on the data acquisition and the original data type, contrast-to-noise ratio (CNR) improvements of up to 179% (8.9 dB) were observed. 3D adaptive filtering may provide an alternative to prolonging the scan time or using contrast agents in MRA when the CNR is low.
A novel method for resampling and enhancing image data using multidimensional adaptive filters is presented. The underlying issue that this paper addresses is segmentation of image structures that are close in size to the voxel geometry. Adaptive filtering is used to reduce both the effects of partial volume averaging by resampling the data to a lattice with higher sample density and to reduce the image noise level. Resampling is achieved by constructing filter sets that have subpixel offsets relative to the original sampling lattice. The filters are also frequency corrected for ansisotropic voxel dimensions. The shift and the voxel dimensions are described by an affine transform and provides a model for tuning the filter frequency functions. The method has been evaluated on CT data where the voxels are in general non cubic. The in-plane resolution in CT image volumes is often higher by a factor of 3-10 than the through-plane resolution. The method clearly shows an improvement over conventional resampling techniques such as cubic spline interpolation and sinc interpolation.
A new parallel imaging technique was implemented which can result in reduced image acquisition times in MRI. MR data is acquired in parallel using an array of receiver coils and then reconstructed simultaneously with multiple processors. The method requires the initial estimation of the 2D sensitivity profile of each coil used in the receiver array. These sensitivity profiles are then used to partially encode the images of interest. A fraction of the total number of k-space lines is consequently acquired and used in a parallel reconstruction scheme, allowing for a substantial reduction in scanning and display times. This technique is in the family of parallel acquisition schemes such as simultaneous acquisition of spatial harmonics (SMASH) and sensitivity encoding (SENSE). It extends the use of the SMASH method to allow the placement of the receiver coil array around the object of interest, enabling imaging of any plane within the volume of interest. In addition, this technique permits the arbitrary choice of the set of k-space lines used in the reconstruction and lends itself to parallel reconstruction, hence allowing for real-time rendering. Simulated results with a 16-fold increase in temporal resolution are shown, as are experimental results with a 4-fold increase in temporal resolution.
The signal decay with increasing b-factor at fixed echo time from brain tissue in vivo has been measured using a line scan Stejskal-Tanner spin echo diffusion approach in eight healthy adult volunteers. The use of a 175 ms echo time and maximum gradient strengths of 10 mT/m allowed 64 b-factors to be sampled, ranging from 5 to 6000 s/ mm2, a maximum some three times larger than that typically used for diffusion imaging. The signal decay with b-factor over this extended range showed a decidedly non-exponential behavior well-suited to biexponential modeling. Statistical analyses of the fitted biexponential parameters from over 125 brain voxels (15 x 15 x 1 mm3 volume) per volunteer yielded a mean volume fraction of 0.74 which decayed with a typical apparent diffusion coefficient around 1.4 microm2/ms. The remaining fraction had an apparent diffusion coefficient of approximately 0.25 microm2/ms. Simple models which might explain the non-exponential behavior, such as intra- and extracellular water compartmentation with slow exchange, appear inadequate for a complete description. For typical diffusion imaging with b-factors below 2000 s/mm2, the standard model of monoexponential signal decay with b-factor, apparent diffusion coefficient values around 0.7 microm2/ms, and a sensitivity to diffusion gradient direction may appear appropriate. Over a more extended but readily accessible b-factor range, however, the complexity of brain signal decay with b-factor increases, offering a greater parametrization of the water diffusion process for tissue characterization.
Apparent diffusion tensor maps of the human brain were acquired with a magnetic resonance imaging sequence (Gudbjartsson, H., Maier, S.E., Mulkern, R.V., M6rocz, I.A., Patz, S., Jolesz, F.A., Magn. Reson. Med. 36 (1996) 509-519). It was shown that the geometric nature of the apparent diffusion tensors can quantitatively characterize the tissue structure. Display of the orientation and directional uniformity of the water diffusion in the brain demonstrated most of the known major anatomical constituents of human white matter. A comparison of corresponding anatomic regions in the white matter of both hemispheres in 24 healthy volunteers revealed that fiber tracts within the anterior limb of the internal capsule have a significantly higher (P < 0.01) measure of alignment in the right hemisphere. This method offers a unique tool for the in vivo demonstration of neural connectivity in healthy and diseased brain.
As welfare diseases become more common all over the world the demand for angiography examinations is increasing rapidly. The development of advanced medical signal processing methods has with few exceptions been concentrated towards CT and MR while traditional contrast based radiology depend on methods developed for ancient photography techniques despite the fact that angiography sequences are generally recorded in digital form. This article presents a new approach for processing of angiography sequences based on advanced image processing methods. The developed algorithm automatically processes angiography sequences containing motion artifacts that cannot be processed by conventional methods like digital subtraction angiography (DSA) and pixel shift due to non uniform motions. The algorithm can in simple terms be described as an ideal pixelshift filter carrying out shifts of different directions and magnitude according to the local motions in the image. In difference to conventional methods it is fully automatic, no mask image needs to be defined and the manual pixelshift operations, which are extremely time consuming, are eliminated. The algorithm is efficient and robust and is designed to run on standard hardware of a powerful workstation which excludes the need for expensive dedicated angiography platforms. Since there is no need to make additional recordings if the patient moves, the patient is exposed to less amount of radiation and contrast fluid. The most exciting benefits by this method are, however, that it opens up new areas for contrast based angiography that are not possible to process with conventional methods e.g. nonuniform motions and multiple layers of moving tissue. Advanced image processing methods provide significantly better image quality and noise suppression but do also provide the means to compute flow velocity and visualize the flow dynamics in the arterial trees by e.g. using color. Initial tests have proven that it is possible to discriminate capillary blood flow from angiography data which opens up interesting possibilities for estimating the blood flow in the heart muscle without use of nuclear methods.