Faculty Bibliography

Effective denoising is vital for proper analysis and accurate quantitative measurements from magnetic resonance (MR) images. Even though many methods were proposed to denoise MR images, only few deal with the estimation of true signal from MR images acquired with phased-array coils. If the magnitude data from phased array coils are reconstructed as the root sum of squares, in the absence of noise correlations and subsampling, the data is assumed to follow a non central-χ distribution. However, when the k-space is subsampled to increase the acquisition speed (as in GRAPPA like methods), noise becomes spatially varying. In this note, we propose a method to denoise multiple-coil acquired MR images. Both the non central-χ distribution and the spatially varying nature of the noise is taken into account in the proposed method. Experiments were conducted on both simulated and real data sets to validate and to demonstrate the effectiveness of the proposed method.

An important focus of Huntington Disease (HD) research is the identification of symptom-independent biomarkers of HD neuropathology. There is an urgent need for reproducible, sensitive and specific outcome measures, which can be used to track disease onset as well as progression. Neuroimaging studies, in particular diffusion-based MRI methods, are powerful probes for characterizing the effects of disease and aging on tissue microstructure. We report novel diffusional kurtosis imaging (DKI) findings in aged transgenic HD rats. We demonstrate altered diffusion metrics in the (pre)frontal cerebral cortex, external capsule and striatum. Presence of increased diffusion complexity and restriction in the striatum is confirmed by an increased fiber dispersion in this region. Immunostaining of the same specimens reveals decreased number of microglia in the (pre)frontal cortex, and increased numbers of oligodendrocytes in the striatum. We conclude that DKI allows sensitive and specific characterization of altered tissue integrity in this HD rat model, indicating a promising potential for diagnostic imaging of gray and white matter pathology.

The authors give an algorithm for recovering the centre and view direction of a one-dimensional camera with known principal point but unknown focal distance, by means of one view with four recognised landmarks. The involved algebra is reduced to solving a quadratic equation. This 4-point-method appears to be more robust than the existing 5-point-algorithm for locating a totally uncalibrated camera by means of chasles conics. On the other hand, the authors' method can offer an alternative for the triangulation method if the value of the focal length is unknown or unreliable (e.g. because of autozoom). © 2012 The Institution of Engineering and Technology.

In vivo diffusion tensor imaging (DTI) was performed on the quinolinic acid (QUIN) rat model of Huntington's disease, together with behavioral assessment of motor deficits and histopathological characterization. DTI and histology revealed the presence of a cortical lesion in 53% of the QUIN animals (QUIN(+ctx)). Histologically, QUIN(+ctx) were distinguished from QUIN(-ctx) animals by increased astroglial reaction within a subregion of the caudate putamen and loss of white matter in the external capsula. Although both techniques are complementary, the quantitative character of DTI makes it possible to pick up subtle differences in tissue microstructure that are not identified with histology. DTI demonstrated differential changes of fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD) in the internal and external capsula, and within a subregion of the caudate putamen. It was suggested that FA increased due to a selective loss of the subcortical connections targeted by degenerative processes at the early stage of the disease, which might turn the striatum into a seemingly more organized structure. When tissue degeneration becomes more severe, FA decreased while AD, RD and MD increased.

PURPOSE/OBJECTIVE:To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. MATERIALS AND METHODS/METHODS:The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P<.05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. RESULTS:Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. CONCLUSION/CONCLUSIONS:There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.

Rats are widely used in experimental neurobiological research, and rat brain atlases are important resources for identifying brain regions in the context of experimental microsurgery, tissue sampling, and neuroimaging, as well as comparison of findings across experiments. Currently, most available rat brain atlases are constructed from histological material derived from single specimens, and provide two-dimensional or three-dimensional (3D) outlines of diverse brain regions and fiber tracts. Important limitations of such atlases are that they represent individual specimens, and that finer details of tissue architecture are lacking. Access to more detailed 3D brain atlases representative of a population of animals is needed. Diffusion tensor imaging (DTI) is a unique neuroimaging modality that provides sensitive information about orientation structure in tissues, and is widely applied in basic and clinical neuroscience investigations. To facilitate analysis and assignment of location in rat brain neuroimaging investigations, we have developed a population-averaged three-dimensional DTI atlas of the normal adult Sprague Dawley rat brain. The atlas is constructed from high resolution ex vivo DTI images, which were nonlinearly warped into a population-averaged in vivo brain template. The atlas currently comprises a selection of manually delineated brain regions, the caudate-putamen complex, globus pallidus, entopeduncular nucleus, substantia nigra, external capsule, corpus callosum, internal capsule, cerebral peduncle, fimbria of the hippocampus, fornix, anterior commisure, optic tract, and stria terminalis. The atlas is freely distributed and potentially useful for several purposes, including automated and manual delineation of rat brain structural and functional imaging data.

A computational framework to obtain an accurate quantification of the Gaussian and non-Gaussian component of water molecules' diffusion through brain tissues with diffusion kurtosis imaging, is presented. The diffusion kurtosis imaging model quantifies the kurtosis, the degree of non-Gaussianity, on a direction dependent basis, constituting a higher order diffusion kurtosis tensor, which is estimated in addition to the well-known diffusion tensor. To reconcile with the physical phenomenon of molecular diffusion, both tensor estimates should lie within a physically acceptable range. Otherwise, clinically and artificially significant changes in diffusion (kurtosis) parameters might be confounded. To guarantee physical relevance, we here suggest to estimate both diffusional tensors by maximizing the joint likelihood function of all Rician distributed diffusion weighted images given the diffusion kurtosis imaging model while imposing a set of nonlinear constraints. As shown in this study, correctly accounting for the Rician noise structure is necessary to avoid significant overestimation of the kurtosis values. The performance of the constrained estimator was evaluated and compared to more commonly used strategies during simulations. Human brain data were used to emphasize the need for constrained estimators as not imposing the constraints give rise to constraint violations in about 70% of the brain voxels.

Diffusion tensor imaging (DTI) is increasingly being used to study white matter (WM) degeneration in patients with psychiatric and neurological disorders. In order to compare diffusion measures across subjects in an automated way, voxel-based analysis (VBA) methods were introduced. In VBA, all DTI data are transformed to a template, after which the diffusion measures of control subjects and patients are compared quantitatively in each voxel. Although VBA has many advantages compared to other post-processing approaches, such as region of interest analysis or tractography, VBA results need to be interpreted cautiously, since it has been demonstrated that they depend on the different parameter settings that are applied in the VBA processing pipeline. In this paper, we examine the effect of the template selection on the VBA results of DTI data. We hypothesized that the choice of template to which all data are transformed would also affect the VBA results. To this end, simulated DTI data sets as well as DTI data from control subjects and multiple sclerosis patients were aligned to (i) a population-specific DTI template, (ii) a subject-based DTI atlas in MNI space, and (iii) the ICBM-81 DTI atlas. Our results suggest that the highest sensitivity and specificity to detect WM abnormalities in a VBA setting was achieved using the population-specific DTI atlas, presumably due to the better spatial image alignment to this template.

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion-weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b-value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non-Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b-value-independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusion-weighted rat data, which was acquired with eight different b-values, uniformly distributed in a range of [0,2800 sec/mm(2)]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusion-weighted data will result in an overestimated degree of non-Gaussian diffusion and a b-value-dependent underestimation of diffusivity measures, a Rician noise model was used in this study.

In the field of diffusion weighted imaging (DWI), it is common to fit one of many available models to the acquired data. A hybrid diffusion imaging (HYDI) approach even allows to reconstruct different models and measures from a single dataset. Methods for DWI atlas construction (and registration) are as plenty as the number of available models. Therefore, it would be nice if we were able to perform atlas building before model reconstruction. In this work, we present a method for atlas construction of DWI data in q-space: we developed a new multi-subject multi-channel diffeomorphic matching algorithm, which is combined with a recently proposed DWI retransformation method in q-space. We applied our method to HYDI data of 10 healthy subjects. From the resulting atlas, we also reconstructed some advanced models. We hereby demonstrate the feasibility of q-space atlas building, as well as the quality, advantages and possibilities of such an atlas.