PubMed · 15682027
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Tsukasa Doi. 2005-01-20. [Brain].. https://doi.org/10.6009/jjrt.kj00003326609
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Currently, images acquired via magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) technology are reconstructed using the discrete inverse Fourier transform. While computationally convenient, this approach is not able to filter out noise. This is a serious limitation because the amount of noise in MRI and fMRI can be substantial. In this paper, we propose an alternative approach to reconstruction, based on penalized likelihood methodology. In particular, we focus on non-linear shrinkage estimators and show that this approach achieves a great reduction in integrated mean squared error (IMSE) of the estimated image with respect to the currently used estimator. This approach is extremely fast and easy to implement computationally. In addition, it can be combined with various alternative approaches to MR image reconstruction and can be easily adapted to other, non-MRI contexts, in which the observed data and the quantities of interest are related via a linear transform.
Mucormycosis is a rare and acute fungal infection which is frequently lethal, usually observed in non-controlled diabetic patients. The infection usually begins in the nose but it can invade the lung, the digestive tract, and the skin. Rhinocerebral mucormycosis accounts for 40 to 49% of mucormycosis cases. We report the case of a 53-year-old diabetic man, with chronic renal failure, presenting with rhinocerebral mucormycosis. Our patient was treated by an association of amphotericin B and surgical debridement. This observation provides on opportunity to recall clinical, histopathological, and therapeutic aspects of rhinocerebral mucormycosis.
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