Depression discrimination using fMRI and DTI data by wavelet based fusion scheme

Both functional magnetic resonance imaging （fMRI） and diffusion tensor imaging （DTI） can provide different information of the human brain, so using the wavelet transform method can achieve a fusion of these two types of image data and can effectively improve the depression recognition accuracy. Multi-resolution wavelet decomposition is used to transform each type of images to the frequency domain in order to obtain the frequency components of the images. To each subject, decomposition components of two images are then added up separately according to their frequencies. The inverse discrete wavelet transform is used to reconstruct the fused images. After that, principal component analysis （PCA） is applied to reduce the dimension and obtain the features of the fusion data before classification. Based on the features of the fused images, an accuracy rate of 80. 95 % for depression recognition is achieved using a leave-one-out cross-validation test. It can be concluded that this wavelet fusion scheme has the ability to improve the current diagnosis of depression.

Diffusion tensor imaging of the brain in patients with Alzheimer's disease and cerebrovascular lesions

Background:Recent autopsy study showed a high incidence of cerebrovascular lesions in Alzheimer's disease(AD).To assess the impact of cerebrovascular pathology in AD,we used diffusion tensor imaging(DTI) to study AD patients with and without cerebrovascular lesions.Materials and Methods:Conventional and DTI scans were obtained from 10 patients with probable AD,10 AD/V patients(probable AD with cerebrovascular lesions) and ten normal controls.Mean diffusivity(D) and fractional anisotropy(FA) values of some structures involved with AD pathology were measured.Results:D value was higher in AD patients than in controls in hippocampus and the cingulate gyrus.In AD/V patients,increased D value was found in the same structures and also in the thalamus and basal ganglia compared to controls.There was a significant difference of D value between AD and AD/V patients.FA value reduced in the white matter of left inferior temporal gyrus and in the bilateral middle cingulate gyrus in patients with AD/V compared with controls.The MMSE(mini-mental state examination) score significantly correlated with FA value in the right hippocampus(r=0.639,P<0.019),in the right anterior cingulate gyrus(r=0.587,P<0.035) and in left parahippocampal gyrus(r=0.559,P<0.047).Conclusion:Cerebrovascular pathology had stronger impact on the D value than the AD pathology alone did.Elevated D value in thalamic and basal ganglia may contribute to cognitive decline in AD/V patients.Reduced FA values in AD/V patients may indicate that cerebrovascular pathology induced more severe white matter damage than the AD pathology alone did.

Application of diffusion tensor imaging in spinal cord injury

Diffusion tensor imaging(DTI)technique can detect the dispersion of water molecules in the white matter of the spinal cord,the integrity of the spinal fiber bundle,and the pathological changes after injury.Clinically,DTI is sensitive to acute and chronic spinal cord injuries,and is most commonly used for the diagnosis of cervical spondylotic myelopathy,multiple sclerosis,secondary brain damage after spinal cord injury,and spinal nerve root damage.In animal studies involving rats,monkeys,cattle,cats,pigs,dogs,etc.,DTI could quantitatively analyze the microstructural and pathological changes of the injured spinal cord and provide a powerful auxiliary diagnosis for behavioral evaluation.

EFFECT OF POSITIVE-INDEFINITE MATRIX ON MAGNETIC RESONANCE DIFFUSION TENSOR-DERIVED PARAMETERS

To evaluate the effect of the positive-indefinite matrix on the diffusion tensor-derived parameters, a modified algorithm is proposed for calculating these parameters. Magnetic resonance （MR） diffusion tensor images of five healthy volunteers are collected. The diffusion sensitive gradient magnetic fields are applied along 25 directions and the diffusion weighting value is 1 000 s/mm^2. Many positive-indefinite diffusion tensors can be found in the white matter area, such as the genu and the splenium of corpus callosum. Due to the positive-indefinite matrix, the mean diffusivity （MD） and the fractional anisotropy （FA） are under-estimated and over-estimated by using the conventional algorithm. Thus, the conventional algorithm is modified by using the absolute values of all eigenvalues. Results show that both the robustness and the reliability for deriving these parameters are improved by the modified algorithm.

Modified Wiener method in diffusion weighted image denoising

To denoise the diffusion weighted images （DWls） featured as multi-boundary, which was very important for the calculation of accurate DTIs （diffusion tensor magnetic resonance imaging）, a modified Wiener filter was proposed. Through analyzing the widely accepted adaptive Wiener filter in image denoising fields, which suffered from annoying noise around the edges of DWIs and in turn greatly affected the denoising effect of DWIs, a local-shift method capable of overcoming the defect of the adaptive Wiener filter was proposed to help better denoising DWIs and the modified Wiener filter was constructed accordingly. To verify the denoising effect of the proposed method, the modified Wiener filter and adaptive Wiener filter were performed on the noisy DWI data, respectively, and the results of different methods were analyzed in detail and put into comparison. The experimental data show that, with the modified Wiener method, more satisfactory results such as lower non-positive tensor percentage and lower mean square errors of the fractional anisotropy map and trace map are obtained than those with the adaptive Wiener method, which in turn helps to produce more accurate DTIs.

Application of multishot diffusion tensor imaging in spinal cord tumors

Objective:To explore the usefulness of multishot diffusion tensor imaging(DTI)for evaluating the neurological function of patients with spinal cord tumors Methods:Routine magnetic resonance imaging and multishot DTI were performed in five patients with spinal cord tumors.The values of fractional anisotropy(FA)and radial diffusivity(RD)were analyzed.Results:Multishot DTI of spinal cord tumors allowed for defining the margins of tumors and determining the relationship of tumors with the adjacent white matter structures of the spinal cord.Multishot DTI demonstrated significantly increased RD and decreased FA of spinal cord tumors compared with those of the normal spinal cord.Conclusions:Multishot DTI is a potentially useful modality for differentiating resectable tumors from nonresectable ones based on preoperative imaging alone as well as for differentiating intramedullary tumors from extramedullary ones.Further prospective studies are warranted to confirm these results.

Diffusion tensor interpolation profile control using non-uniform motion on a Riemannian geodesic

Tensor interpolation is a key step in the processing algorithms of diffusion tensor imaging (DTI), such as registration and tractography. The diffusion tensor (DT) in biological tissues is assumed to be positive definite. However, the tensor interpolations in most clinical applications have used a Euclidian scheme that does not take this assumption into account. Several Rie-mannian schemes were developed to overcome this limitation. Although each of the Riemannian schemes uses different metrics, they all result in a ‘fixed’ interpolation profile that cannot adapt to a variety of diffusion patterns in biological tissues. In this paper, we propose a DT interpolation scheme to control the interpolation profile, and explore its feasibility in clinical applications. The profile controllability comes from the non-uniform motion of interpolation on the Riemannian geodesic. The interpolation experiment with medical DTI data shows that the profile control improves the interpolation quality by assessing the reconstruction errors with the determinant error, Euclidean norm, and Riemannian norm.

AIG Based Nonlinear Anisotropic Smoothing Strategy for Vector-Valued Images

The effects of the Rician noise on the calculated tensors are analyzed and an affine invariant gradient (AIG) based nonlinear anisotropic smoothing strategy is presented.The AIG based smoothing strategy is a development of the affine invariant nonlinear anisotropic diffusion(AINAD) restoration model,introduced by Guillermo Sapiro,and adopted to restore vector-valued data.To evaluate the efficiency of the presented AINAD model in accounting for the Rician noise introduced into the vector-valued data,the peak-to-peak signal-to-noise ratio(PSNR),signal-to-mean squared error ratio(SMSE) and Beta(parameter that stands for edge preservation) metrics are used.The experiment results acquired from the synthetic and real data prove the good performance of the presented filter.