Multiresolution fuzzy clustering of functional MRI data.

Recent developments in the analysis of functional MRI data reveal a shift from hypothesis-driven statistical tests to unsupervised strategies. One of the most promising approaches is the fuzzy clustering algorithm (FCA), whose potential to detect activation patterns has already been demonstrated. But the FCA suffers from three drawbacks: first the computational complexity, second the higher sensitivity to noise and third the dependence on the random initialization. With the multiresolution approach presented here, these weak points are significantly improved, as is demonstrated in our tests with simulated and real functional MRI data.

Apparent diffusion coefficient mapping of infarcted tissue and the ischaemic penumbra in acute stroke.

MRI assessment of diffusion changes in acute cerebral ischaemia necessitates analysis of the apparent diffusion coefficient (ADC). We used the concept of relative weighted mean ADC (rwmADC) to obtain an accurate estimate of the extent of infarcted tissue. We studied ten patient with of acute ischaemic stroke, using diffusion- and perfusion- weighted MRI. The rwmADC was used to calculate a corrected ADC-lesion volume (DLVR), which was compared with the diffusion-lesion volume (DLV), initial perfusion lesion volumes and the follow-up infarct volume on T2-weighted images. We looked at correlations between the MRI and clinical findings. DLVR was closest to the final infarct size and had the best clinicoradiological correlation (r=0.77). Weighting the mean ADC within the ischaemic and normal parenchyma can give a more correct estimate of the volume of infarcted brain parenchyma, thus improving the definition of the penumbra.