Publications

Genuine heteroleptic neodymium and samarium complexes of formula [Cp*’Ln[(p-tol)NN](BH4)] (Cp*’ = C5Me4(nPr), (p-tol)NN = (p-tol)NC(Me)CHC(Me)N(p-tol), Ln = Sm: 1a, Ln = Nd: 1b) have been synthesized for the first time. These unprecedented homologues of early lanthanocenes are prepared by a metathetic reaction between their monocyclopentadienylbisborohydrido precursors with the corresponding potassium diketiminate. Both complexes were obtained in good yields and were characterized by 1H NMR spectroscopy and elemental analysis. Complex 1 a has an non-solvated dimeric structure, as indicated by its crystallographic data. The chloroneodymium analogue [Cp*’Nd[(p-tol)NN](Cl)] (2b) was only obtained as a part of a mixture. Analysis of crystals of 2b by X-ray diffraction revealed a molecular structure very similar to that of 1a. Preliminary isoprene polymerization experiments were carried out with 1 b in the presence of an alkylmagnesium coactivator. The resulting bimetallic Nd/Mg system behaves as an efficient and highly stereospecific catalyst with the synthesis of trans-1,4-polyisoprene with more than 98% regularity. The control of the polymer structure is related to the steric hindrance around the lanthanide atom.

We created a spatial probability atlas of schizophrenia to provide information about the neuroanatomic variability of brain regions of patients with the disorder. Probability maps of 16 regions of interest (ROIs) were constructed by taking manually parcellated ROIs from subjects’ magnetic resonance images (MRIs) and linearly transforming them into Talairach space using the Montreal Neurological Institute (MNI) template. ROIs included temporal, parietal, and prefrontal cortex subregions, with a principal focus on temporal lobe structures. Subject Ns ranged from 11 to 28 for the different ROIs. Our global measure of the spatial distribution of the transformed ROI was the sum of voxels with 50% overlap among subjects. The superior temporal gyrus (STG) and fusiform gyrus (FG) had lower values for schizophrenic subjects than for normal controls, suggestive of greater spatial variability for these ROIs in schizophrenic subjects. For the computation of statistical significance of group differences in portions of the ROI, we used voxel-wise comparisons and Fisher’s exact test. First-episode schizophrenic patients compared with controls showed lower probability (P 0.05) at dorso-posterior areas of planum temporale and Heschl’s gyrus, lateral and anterior regions in the left hippocampus (HIPP), and dorsolateral regions of fusiform gyrus. Importantly, most ROIs of schizophrenic subjects showed a significantly lower spatial overlap than controls, even after nonlinear spatial normalization, suggesting a greater heterogeneity in the spatial distribution of ROIs. There is consequently a need for caution in neuroimaging studies where data from schizophrenic subjects are normalized to a particular stereotaxic coordinate system based on healthy controls. Apparent group differences in activation may simply reflect a greater heterogeneity of spatial distribution in schizophrenia.

BACKGROUND: Using laparoscopic ultrasonography (LUS) is challenging for both novice and experienced ultrasonographers. The major difficulty surgeons experience is understanding the orientation of the ultrasonography image. The purpose of this study was to assess whether providing surgeons with orientation information improves their ability to interpret LUS images. METHODS: We performed a LUS examination on a 25-kg pig and simultaneously digitized video from the laparoscopic camera, the LUS, and a novel orientation system. From the video recordings, 12 different clips of intra-abdominal anatomy were prepared. Twenty surgeons (18 staff, 2 fellows) volunteered to participate in an experimental crossover study. Test subjects reviewed the LUS clips along with the laparoscopic video images and the orientation display. Controls reviewed the LUS clips with only the laparoscopic video images. Diagnostic accuracy was compared by using the odds ratio.

Conventional transvenous defibrillation is performed with an ICD using a dual current pathway. The defibrillation energy is delivered from the RV electrode to the superior vena cava (SVC) electrode and the metallic case (CAN) of the ICD. Biventricular defibrillation uses an additional electrode placed in the LV free wall with sequential shocks to create an additional current vector. Clinical studies of biventricular defibrillation have reported a 45% reduction in mean defibrillation threshold (DFT) energy. The aim of the study was to use computational methods to examine the biventricular defibrillation fields together with their corresponding DFTs in a variety of patient derived models and to compare them to simulations of conventional defibrillation. A library of thoracic models derived from nine patients was used to solve for electric field distributions. The defibrillation waveform consisted of a LV —> SVC + CAN monophasic shock followed by a biphasic shock delivered via the RV —> SVC + CAN electrodes. When the initial voltage of the two shocks is the same, the simulations show that the biventricular configuration reduces the mean DFT by 46% (3.5 +/- 1.3 vs 5.5 +/- 2.7 J, P = 0.005). When the leading edge of the biphasic shock is equal to the trailing edge of the monophasic shock, there is no statistically significant difference in the mean DFT (4.9 +/- 1.9 vs 5.5 +/- 2.7 J, P > 0.05) with the DFT decreasing in some patients and increasing in others. These results suggest that patient-specific computational models may be able to identify those patients who would most benefit from a biventricular configuration.

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series.

BACKGROUND: Findings from postmortem studies suggest reduced prefrontal cortical thickness in schizophrenia; however, cortical thickness in first-episode schizophrenia has not been evaluated using magnetic resonance imaging (MRI).

A methodology for chemical shift resolved molecular self-diffusion measurements in time-independent static and radiofrequency field gradients is demonstrated. Diffusion encoding is provided by a stimulated echo sequence with additional z-storage that allows for a change of diffusion time without affecting the relaxation weighting. The signal is acquired stroboscopically between the pulses of a train of adiabatic double passages that induces a z-rotation counteracting the phase spread resulting from precession in the inhomogeneous static field, as demonstrated in recent approaches to the goal of high-resolution "ex situ" NMR. Simulations of the pulse sequence show that the acquired signal results from the desired coherence pathway. Successful demonstrations of the experiment were performed on a mixture of water and isopropanol.

RATIONALE AND OBJECTIVES: To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. MATERIALS AND METHODS: The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). RESULTS: Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899). CONCLUSION: The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.

The validity of brain tumour segmentation is an important issue in image processing because it has a direct impact on surgical planning. We examined the segmentation accuracy based on three two-sample validation metrics against the estimated composite latent gold standard, which was derived from several experts’ manual segmentations by an EM algorithm. The distribution functions of the tumour and control pixel data were parametrically assumed to be a mixture of two beta distributions with different shape parameters. We estimated the corresponding receiver operating characteristic curve, Dice similarity coefficient, and mutual information, over all possible decision thresholds. Based on each validation metric, an optimal threshold was then computed via maximization. We illustrated these methods on MR imaging data from nine brain tumour cases of three different tumour types, each consisting of a large number of pixels. The automated segmentation yielded satisfactory accuracy with varied optimal thresholds. The performances of these validation metrics were also investigated via Monte Carlo simulation. Extensions of incorporating spatial correlation structures using a Markov random field model were considered.

We present a set of techniques for embedding the physics of the imaging process that generates a class of magnetic resonance images (MRIs) into a segmentation or registration algorithm. This results in substantial invariance to acquisition parameters, as the effect of these parameters on the contrast properties of various brain structures is explicitly modeled in the segmentation. In addition, the integration of image acquisition with tissue classification allows the derivation of sequences that are optimal for segmentation purposes. Another benefit of these procedures is the generation of probabilistic models of the intrinsic tissue parameters that cause MR contrast (e.g., T1, proton density, T2*), allowing access to these physiologically relevant parameters that may change with disease or demographic, resulting in nonmorphometric alterations in MR images that are otherwise difficult to detect. Finally, we also present a high band width multiecho FLASH pulse sequence that results in high signal-to-noise ratio with minimal image distortion due to B0 effects. This sequence has the added benefit of allowing the explicit estimation of T2* and of reducing test-retest intensity variability.