White matter connectivity in the human brain can be mapped by diffusion tensor magnetic resonance imaging (DTI). After reconstruction, the diffusion tensors, the diffusion amplitude and the diffusion direction can be displayed on a morphological background. Consequently, diffusion tensor fibre tracking can be applied as a non-invasive in vivo technique for the delineation and quantification of specific white matter pathways. The aim of this study was to show that normalization to the Montreal Neurological Institute (MNI) stereotaxic standard space preserves specific diffusion features. Therefore, techniques for tensor imaging and fibre tracking were applied to the normalized brains as well as to the group averaged brain data. A normalization step of individual data was included by registration to a scanner- and sequence-specific DTI template data set which was created from a normal database transformed to MNI space. The algorithms were tested and validated for a group of 13 healthy controls.
Movement artifacts and other sources of noise are a matter of concern particularly in the neuroimaging research of movement disorders such as Huntington’s disease (HD). Using diffusion weighted imaging (DWI) and fractional anisotropy (FA) as a compound marker of white matter integrity, we investigated the effect of movement on HD specific changes in magnetic resonance imaging (MRI) data and how post hoc compensation for it affects the MRI results. To this end, we studied by 3T MRI: 18 early affected, 22 premanifest gene-positive subjects, 23 healthy controls (50 slices of 2.3 mm thickness per volume, 64 diffusion-weighted directions (b = 1000 s/mm2), 8 minimal diffusion-weighting (b = 100 s/mm2)); and by 1.5 T imaging: 29 premanifest HD, 30 controls (40 axial slices of 2.3 mm thickness per volume, 61 diffusion-weighted directions (b = 1000 s/mm2), minimal diffusion-weighting (b = 100 s/mm2)). An outlier based method was developed to identify movement and other sources of noise by comparing the index DWI direction against a weighted average computed from all other directions of the same subject. No significant differences were observed when separately comparing each group of patients with and without removal of DWI volumes that contained artifacts. In line with previous DWI-based studies, decreased FA in the corpus callosum and increased FA around the basal ganglia were observed when premanifest mutation carriers and early affected patients were compared with healthy controls. These findings demonstrate the robustness of the FA value in the presence of movement and thus encourage multi-center imaging studies in HD.
The spatial distribution of high-frequency components in magnetic signals during the QRS complex of the human heartbeat was investigated. Cardiomagnetic signals were recorded simultaneously using 49 first-order magnetogradiometer channels of a multi-SQUID system with a low noise power density. The QRS fragmentation score S, as a measure of the fragmentation of the bandpass-filtered QRS complex, was examined for its sensitivity and specificity to discriminate 34 healthy volunteers, 42 post-myocardial infarction patients and 43 patients with coronary heart disease and with a history of malignant sustained ventricular tachycardia or ventricular fibrillation. The multichannel information was visualized by two-dimensional mapping of the score values of the single channels. By averaging the score values for the seven central channels, S7, the score values of all 49 channels, S49, and calculating the standard deviation for all 49 channels, D49, a higher sensitivity and specificity for detecting patients with ventricular tachycardia (VT) or ventricular fibrillation (VF) was reached than by analysis of a single channel. Combination of these parameters furnishes a sensitivity of 90% and a specificity of 70% for identifying patients prone to VT/VF. The results were compared with diagnostic information obtained from the QRS duration of the signal as well as with results obtained by modified QRS integral mapping.
Performing signal averaging in an efficient and correct way is indispensable since it is a prerequisite for a broad variety of magnetocardiographic (MCG) analysis methods. One of the most common procedures for performing the signal averaging to increase the signal-to-noise ratio (SNR) in magnetocardiography, as well as in electrocardiography (ECG), is done by means of spatial or temporal techniques. In this paper, an improvement of the temporal averaging method is presented. In order to obtain an accurate signal detection, temporal alignment methods and objective classification criteria are developed. The processing technique based on hierarchical clustering is introduced to take into account the non-stationarity of the noise and, to some extent, the biological variability of the signals reaching the optimum SNR. The method implemented is especially designed to run fast and does not require any interaction from the operator. The averaging procedure described in this work is applied to the averaging of MCG data as an example, but with its intrinsic properties it can also be applied to the averaging of ECG recording, averaging of body-surface-potential mapping (BSPM) and averaging of magnetoencephalographic (MEG) or electroencephalographic (EEG) signals.
In patients with CAD and with a history of VT/VF, intra-QRS fragmentation is increased and the area of high fragmentation in 2-D contour maps is enlarged. These findings may be helpful in identifying patients with CAD at risk for malignant tachyarrhythmias.
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