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Stefan Kuczera

Stephan Ernst Maier

An approach to estimate noise, Rician signal bias and true signal in magnitude signal data obtained with magnetic resonance imaging. The method uses multiple measurements at different scan parameter settings, also referred to as weightings, and an iterative algorithm to estimate noise, expected signal and associated Rician signal bias. Measurements at all measured weighting levels contribute to the ultimate estimation of the bias-free signal decay function. Therefore, of the so processed magnetic resonance image data, weighted signals can be computed at arbitrary weighting levels and with considerably better signal-to-noise ratio than the originally obtained data at corresponding weightings. Bias-free weighted image data at desired weighting levels, maps of the decay function fit parameters, or maps of a combination of such decay function parameters can be used for rapid and highly sensitive tissue characterization.

Method for optimized bias and signal inference in magnetic resonance image analysis

The present invention relates to parameter quantification for reproducible characterization of measured magnetic resonance signal variations in biological tissues due to variation in contrast-weighting levels. The method uses comprehensive sampling and higher-order model analysis to attain a more complete description of the signal variation at high signal-to-noise ratio. The signal variation described by the higher-order model fit is subsequently used for retrospective fit analysis based on a more basic model and a flexible sampling pattern. This approach greatly facilitates reproducibility of parameter quantification, since sampling inconsistencies can readily be accounted for.

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Pixbo, Sweden

Stefan Kuczera