Diffusion tensor magnetic resonance imaging (DT-MRI) provides information about fiber direction in brain white matter and can be used for neuronal fiber pathways tracking. The purpose of our study is to develop and evaluate a novel approach for tracing anatomical fibers in vivo human brain from 3D DT-MRI tensor fields. The scheme is divided into two steps: regularization of tensor fields and fiber tracking. Firstly, 3D tensor fields are regularized to preserve directional information and discontinuous features, while removing uncorrelated noise from the data. Secondly, initiated from an operator-selected region, the anatomical fibers are bidirectionally traced based on minimizing the tracking cost (MTC) model. The model computes the possible direction of tract propagation, allowing a global trade-off among the entire tensor data, a prior knowledge of low curvature, and tracking inertia, instead of just the major eigenvector. Analysis on simulated data showed that the proposed method is less sensitive to image noise and partial volume effect than tracking using the major eigenvector, and overcomes the problem of fiber crossing successfully. Various estimated tracts obtained from human brain DT-MRI data showed that the proposed approach improves the reliability and robustness of fiber tractography. The proposed approach is effective and reproducible, which is promising for mapping the organizational patterns of white matter in the human brain as well as mapping the relationship between major fiber trajectories and the location and extent of brain lesions.
There has been increasing interest in quantitatively analyzing diffusion anisotropy of ischemic lesions from diffusion tensor magnetic resonance imaging (DT-MRI). In this study, we develop and evaluate a novel method to automatically segment cerebral ischemic lesions from DT-MRI images. The method is a combination of image preprocessing, measures of diffusion anisotropy, multi-scale statistical classification (MSSC), and partial volume reclassification (PVRC). First, non-linear anisotropic diffusion filtering are applied to DT-MRI images to reduce image noise. Then, measures of diffusion anisotropy, such as fractional anisotropy and trace of the diffusion tensor, are calculated to acquire the diffusion properties of different brain tissues. Finally, ischemic lesions are accurately segmented using robust MSSC-PVRC, taking into account spatial information, intensity gradient, radio frequency (RF) inhomogeity and measures of diffusion anisotropy of DT-MRI images. After MSSC, PVRC is applied to overcome partial volume effect (PVE). Analyses of synthetic data and DT-MRI scans of 20 patients with ischemic stroke were carried out. It shows that the method got a satisfied segmentation of ischemic lesions, successfully overcoming the problem of intensity overlapping and reducing PVE, and that the method is robust to varying starting parameters. The results of the automated method are compared with lesion delineations by human experts, showing the rapid identification of ischemic lesion with accuracy and reproducibility. The proposed automatic technique is promising not only to detect the site and size of ischemic lesions in stroke patients but also to quantitatively analyze diffusion anisotropy of lesions for further clinical diagnoses and therapy.
Diffusion weighted imaging (DWI) is the gold standard for imaging of acute stroke. Today, high-field systems operating at 3T become increasingly available in clinical settings. But, with b-value increasing, lesion SNR of DWI image descends, and anisotropy increases significantly. Aim of the study is to develop an automatic volumetric measure method of ischemic lesions on diffusion weighted imaging (DWI) images at high magnetic field, without the disturbance of anisotropy. Using a home-built interactive platform, we rated SNR and anisotropy. The extent of anisotropy was evaluated by the intensity ratio of white matter versus gray matter. Based on this knowledge, we developed an automatic segmentation method, involving firstly non-linear anisotropic diffusion filtering, secondly expert pieces of information applied to determine the scopes of parameters according to different b-value, and finally multi-scale adaptive statistical classification with intensity inhomogeneity correction. Results of the automatic segmentation are compared with lesion delineations by experts, showing the rapid identification of ischemic lesion with accuracy and reproducibility, even in the presence of radio frequency (RF) inhomogeneity. There has been considerable interest in using DWI at 3T to detect ischemic lesion in stroke patients. The proposed method is promising for rapid, accurate, and quantitatively diagnosis of ischemic stroke.
It is important to detect the site and size of infarction volume in stroke patients. An automatic method for segmenting brain infarction lesion from diffusion weighted magnetic resonance (MR) images of patients has been developed. The method uses an integrated approach which employs image processing techniques based on anisotropic filters and atlas-based registration techniques. It is a multi-stage process, involving first images preprocessing, then global and local registration between the anatomical brain atlas and the patient, and finally segmentation of infarction volume based on region splitting and merging and multi-scale adaptive statistical classification. The proposed multi-scale adaptive statistical classification model takes into account spatial, intensity gradient, and contextual information of the anatomical brain atlas and the patient. Application of the method to diffusion weighted imaging (DWI) scans of twenty patients with clinically determined infarction was carried out. It shows that the method got a satisfied segmentation even in the presence of radio frequency (RF) inhomogeneities. The results were compared with lesion delineations by human experts, showing the identification of infarction lesion with accuracy and reproducibility.
To study the application of diffusion weighted imaging and image post processing in the diagnosis of stroke, especially in acute stroke, 205 patients were examined by 1.5 T or 1.0 T MRI scanner and the images such as T1, T2 and diffusion weighted images were obtained. Image post processing was done with "3D Med System" developed by our lab to analyze data and acquire the apparent diffusion coefficient (ADC) map. In acute and subacute stage of stroke, the signal in cerebral infarction areas changed to hyperintensity in T2- and diffusion-weighted images, normal or hypointensity in T1-weighted images. In hyperacute stage, however, the signal was hyperintense just in the diffusion weighted imaes; others were normal. In the chronic stage, the signal in T1- and diffusion-weighted imaging showed hypointensity and hyperintensity in T2 weighted imaging. Because ADC declined obviously in acute and subacute stage of stroke, the lesion area was hypointensity in ADC map. With the development of the disease, ADC gradually recovered and then changed to hyperintensity in ADC map in chronic stage. Using diffusion weighted imaging and ADC mapping can make a diagnosis of stroke, especially in the hyperacute stage of stroke, and can differentiate acute and chronic stroke.