Quantitative BOLD: the effect of diffusion.

PURPOSE: To make the quantitative blood oxygenation level-dependent (qBOLD) method more suitable for clinical application by accounting for proton diffusion and reducing acquisition times. MATERIALS AND METHODS: Monte Carlo methods are used to simulate the signal from diffusing protons in the presence of a blood vessel network. A diffusive qBOLD model was then constructed using a lookup table of the results. Acquisition times are reduced by parallel imaging and by employing an integrated fieldmapping method, rather than running an additional sequence. RESULTS: The addition of diffusion to the model is shown to have a significant impact on predicted signal formation. This is found to affect all fitted parameters when the model is applied to real data. Parallel imaging and integrated fieldmapping allowed the GESSE (gradient echo sampling of a spin echo) acquisition to be made in less than 10 minutes while maintaining high signal-to-noise ratio (SNR). CONCLUSION: By incorporating integrated field mapping and parallel imaging techniques, GESSE data were acquired within clinically acceptable acquisition times. These data fit closely to the diffusive qBOLD model, providing more realistic and robust measurements of T(2) and blood oxygenation than the static model.

Potential heating caused by intraparenchymal intracranial pressure transducers in a 3-tesla magnetic resonance imaging system using a body radiofrequency resonator: assessment of the Codman MicroSensor Transducer.

Magnetic resonance imaging and spectroscopy may provide important clinical information in the acute stages of brain injury. For this to occur it must be ensured that intracranial pressure (ICP) monitoring devices are safe to bring into the MR imaging suite. The authors tested a Codman MicroSensor ICP Transducer (Codman & Shurtleff, Inc.) within a 3-T MR imaging system using the transmit body coil and receive-only coils and the transmit-and-receive head coil. Extreme and rapid heating of 64 degrees C was noted with the transducer wire in certain positions when using the transmit body coil and receive-only head coil. This is consistent with the phenomenon of resonance, and the probe was shown to have a distinct resonant response when coupled to HP 4195A Network Analyzer (Hewlett Packard). Coiling some of the transducer wire outside of the receive-only head coil reduced the generated current and so stopped the thermogenesis. This may be due to the introduction of a radiofrequency choke. The ICP transducer performed within clinically acceptable limits in both the static magnetic field and during imaging with high radiofrequency power when the excess wire was in this configuration. No heating was observed when a transmit-and-receive head coil was used. This study has shown when using a high-field magnet, the Codman ICP probe is MR conditional. That is, in the authors' system, it can be safely used with the transmit-and-receive head coil, but when using the transmit body coil the transducer wire must be coiled into concentric loops outside of the receive-only head coil.

Application of a probabilistic double-fibre structure model to diffusion-weighted MR images of the human brain.

A Markov chain Monte Carlo (MCMC) algorithm has been reported which is capable of determining the probabilistic orientation of two-fibre populations from high angular resolution diffusion-weighted data (HARDI). We present and critically discuss the application of this algorithm to in vivo human datasets acquired in clinically realistic times. We show that by appropriate model selection areas of multiple fibre populations can be identified that correspond with those predicted from known anatomy. Quantitative maps of fibre orientation probability are derived and shown for one- and two-fibre models of neural architecture. Fibre crossings in the pons, the internal capsule and the corona radiata are shown. In addition, we demonstrate that the relative proportion of anisotropic signal may be a more appropriate measure of anisotropy than summary measures derived from the tensor model such as fractional anisotropy in areas with multi-fibre populations.

Concordant biology underlies discordant imaging findings: diffusivity behaves differently in grey and white matter post acute neurotrauma.

BACKGROUND: Cerebral edema is a common sequelum post traumatic brain injury (TBI). Quantification of the apparent diffusion coefficient (ADC) using diffusion tensor imaging (DTI) may help to characterize the pathophysiology of brain swelling. METHODS: Twenty-two patients with moderate-to-severe TBI underwent magnetic resonance (MR) imaging, including DTI, within five days of injury. The mean ADCs in whole brain white matter, whole brain grey matter and entire brain were calculated and compared to twenty-five controls. FINDINGS: A significant decrease in the grey matter ADC (p < 0.001), significant increase in the white matter ADC (p < 0.001) and no significant change in the whole brain ADC (p = 0.771) was observed. No significant correlation was found between DTI parameters in any of the three regions of interest (ROI) and GCS, time to scan, intracranial pressure (ICP) before and during the time of the scan, cerebral perfusion pressure at time of scan, or Glasgow Outcome Score (GCS). CONCLUSIONS: The decrease in ADC seen in the grey matter is consistent with cytotoxic edema. The increase in ADC in the white matter indicates damage that has led to an overall less restricted diffusion. This study assists in the interpretation of the ADC by showing that the acute changes are different in the whole brain white and grey matter ROIs post TBI.

Gaussian process modeling for image distortion correction in echo planar imaging.

An enhanced method for correction of image distortion due to B(0)-field inhomogeneities in echo planar imaging (EPI) is presented. The algorithm is based on the measurement of the point spread function (PSF) associated with each image voxel using a reference scan. The expected distortion map in the phase encode direction is then estimated using a nonparametric inference algorithm known as Gaussian process modeling. The algorithm is shown to be robust to the presence of regions of low signal-to-noise in the image and large inhomogeneities.