Diffusion-weighted Imaging Datasets Research Articles

470 E-cadherin plasticity in tumor-initiating stem-like cells regulates prostate cancer cell invasion

Diffusion weighted imaging (DWI) is an emerging technique for abdominal MR and usually performed before intravenous contrast injection. Recent studies performed in patients with normal liver function have shown that DWI can be performed after gadolinium administration. Aim of this study was to compare DWI before and after administration of different gadolinium compounds in patients with HCC and liver cirrhosis.15 patients with known HCC and liver cirrhosis underwent liver MRI at 1.5 T (Magnetom Avanto, Siemens) including DWI on day 1 before and after administration of gadobutrol (Gadovist®) and on day 2 after administration of EOB-Gadolinium-DTPA (Primovist®). Signal to noise ratios (SNR) and contrast to noise ratios (CNR) of HCC lesions were determined for all DWI data sets. Furthermore, ADC values were compared using a Wilcoxon test. A p-value <0.05 indicated statistically significant differences.There were no statistically significant differences regarding SNR pre-contrast (mean: 48.1), after gadobutrol (mean: 47.7) or after EOB-Gadolinium-DTPA (mean: 50.0; values for b = 50 s/mm2). Similarly, no significant differences were found for CNR (average values:34.4 vs. 32.3 vs. 30.7; b = 50 s/mm2) nor for ADC-values (mean: 1.5 vs. 1.4 vs. 1.5 × 10−3 mm2/s) of HCC.There is no significant difference regarding DWI in patients with cirrhosis before and after contrast injection. Hence, it is reliable to run DWI after gadolinium either as an alternative for unsuccessful pre-contrast DWI or as a gap filler to spare time in EOB-Gadolinium-DTPA imaging.

Distribution and fibre field similarity mapping of the human anterior commissure fibres by diffusion tensor imaging

The anterior commissure is a critical interhemispheric pathway in animals, yet its connections in humans are not clearly understood. Its distribution has shown to vary greatly between species, and it is thought that in humans it may convey axons from a larger territory than previously thought. The aim was to use an anatomical mapping tool to look at the anterior commissure fibres and to compare the distribution findings with published anatomical understanding. Two different diffusion-weighted imaging data sets were acquired from eight healthy subjects using a 3Tesla MR scanner with 32 gradient directions. Diffusion tensor imaging tractography was performed, and the anterior commissure fibres were selected using three-dimensional regions of interest. Distribution of the fibres was observed by means of registration with T2-weighted images. The fibre field similarity maps were produced for five of the eight subjects by comparing each subject's fibres to the combined map of the five data sets. Fibres were shown to lead into the temporal lobe and towards the orbitofrontal cortex in the majority of subjects. Fibres were also distributed to the parietal or occipital lobes in all five subjects in whom the anterior commissure was large enough for interhemispheric fibres to be tracked through. The fibre field similarity maps highlighted areas where the local distances of fibre tracts were displayed for each subject compared to the combined bundle map. The anterior commissure may play a more important role in interhemispheric communication than currently presumed by conveying axons from a wider territory, and the fibre field similarity maps give a novel approach to quantifying and visualising characteristics of fibre tracts.

Diffusion‐weighted imaging and magnetic resonance spectroscopy findings in a case of neonatal hypoglycaemia

Rachel E Musson MBCHB MRCP, Ruth Batty MBBS MRCP FRCR, Santosh R Mordekar MD MRCP MRCPCH, Iain D Wilkinson PHD ARCP FIPEM, Paul D Griffiths PHD FRCR, Daniel JA Connolly BSC MRCP FRCR 1 Radiology Department, Royal Hallamshire Hospital, Sheffield, UK. 2 Department of Neurology, Sheffield Children's Hospital, Sheffield, UK. 3 Academic Unit of Radiology, University of Sheffield, Sheffield, UK. 4 Radiology Department, Sheffield Children's Hospital, Sheffield, UK. Correspondence to: rachel.musson@sth.nhs.uk

Group analyses of connectivity-based cortical parcellation using repeated k-means clustering

K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting points therefore could lead to different solutions. In this study we explore this issue. We apply k-means clustering a thousand times to the same DWI dataset collected in 10 individuals to segment two brain regions: the SMA–preSMA on the medial wall, and the insula. At the level of single subjects, we found that in both brain regions, repeatedly applying k-means indeed often leads to a variety of rather different cortical based parcellations. By assessing the similarity and frequency of these different solutions, we show that ∼256 k-means repetitions are needed to accurately estimate the distribution of possible solutions. Using nonparametric group statistics, we then propose a method to employ the variability of clustering solutions to assess the reliability with which certain voxels can be attributed to a particular cluster. In addition, we show that the proportion of voxels that can be attributed significantly to either cluster in the SMA and preSMA is relatively higher than in the insula and discuss how this difference may relate to differences in the anatomy of these regions.

Using the Model-Based Residual Bootstrap to Quantify Uncertainty in Fiber Orientations From $Q$-Ball Analysis

Bootstrapping of repeated diffusion-weighted image datasets enables nonparametric quantification of the uncertainty in the inferred fiber orientation. The wild bootstrap and the residual bootstrap are model-based residual resampling methods which use a single dataset. Previously, the wild bootstrap method has been presented as an alternative to conventional bootstrapping for diffusion tensor imaging. Here we present a study of an implementation of model-based residual bootstrapping using q -ball analysis and compare the outputs with conventional bootstrapping. We show that model-based residual bootstrap q-ball generates results that closely match the output of the conventional bootstrap. Both the residual and conventional bootstrap of multifiber methods can be used to estimate the probability of different numbers of fiber populations existing in different brain tissues. Also, we have shown that these methods can be used to provide input for probabilistic tractography, avoiding existing limitations associated with data calibration and model selection.

Diffusion-weighted MRI in diffuse axonal injury of the brain.

The goal of this study was to identify and describe the different types and patterns of tissue injury which are encountered by diffusion-weighted imaging (DWI) in diffuse axonal injury (DAI) of the brain. The DWI data sets of 98 patients who suffered from a closed-head injury were retrospectively evaluated. Medical records were reviewed to rule out pre-existing neurological diseases. Lesions were studied for their DWI signal characteristics and lesion size or extension. Traumatic lesions were classified into three categories depending on their signal characteristica on DWI and apparent diffusion coefficient (ADC) maps: type 1, DWI- and ADC-hyperintense most likely representing lesions with vasogenic edema; type 2, DWI-hyperintense, ADC-hypointense indicating cytotoxic edema; type 3, central hemorrhagic lesion surrounded by an area of increased diffusion. According to the size and extent of lesions, injury was classified into three groups: group A, focal injury; group B, regional/confluent injury; and group C, extensive/diffuse injury. Our study showed that diffusion-weighted imaging differentiates between lesions with decreased and increased diffusion in patients with DAI. Different degrees of tissue injury extent were noticed. Future prospective studies should study if this additional information can be used as a predictor of injury reversibility, final outcome and prognosis.

A model for multiparametric mri tissue characterization in experimental cerebral ischemia with histological validation in rat: part 1.

After stroke, brain tissue undergoes time-dependent heterogeneous histopathological change. These tissue alterations have MRI characteristics that allow segmentation of ischemic from nonischemic tissue. Moreover, MRI segmentation generates different zones within the lesion that may reflect heterogeneity of tissue damage. A vector tissue signature model is presented that uses multiparametric MRI for segmentation and characterization of tissue. An objective (unsupervised) computer segmentation algorithm was incorporated into this model with the use of a modified version of the Iterative Self-Organizing Data Analysis Technique (ISODATA). The ability of the model to characterize ischemic tissue after permanent middle cerebral ischemia occlusion in the rat was tested. Multiparametric ISODATA measurements of the ischemic tissue were compared with quantitative histological characterization of the tissue from 4 hours to 1 week after stroke. The ISODATA segmentation of tissue identified a gradation of cerebral tissue damage at all time points after stroke. The histological scoring of ischemic tissue from 4 hours to 1 week after stroke on all the animals was significantly correlated with ISODATA segmentation (r=0.78, P<0.001; n=20) when a multiparametric (T2-, T1-, diffusion-weighted imaging) data set was used, less correlated (r=0.70, P<0.01; n=20) when a T2- and T1-weighted data set was used, and not correlated (r=-0.12, P>0.47; n=20) when only a diffusion-weighted imaging data set was used. Our data indicate that an integrated set of MRI parameters can distinguish and stage ischemic tissue damage in an objective manner.