We consider the problem of statistical inference for functional and dynamic magnetic resonance imaging (MRI). A new approach is proposed which extends the adaptive weights smoothing procedure of Polzehl and Spokoiny that was originally designed for image denoising. We demonstrate how the adaptive weights smoothing method can be applied to time series of images, which typically occur in functional and dynamic MRI. It is shown how signal detection in functional MRI and the analysis of dynamic MRI can benefit from spatially adaptive smoothing. The performance of the procedure is illustrated by using real and simulated data.
|Number of pages||17|
|Journal||Journal of the Royal Statistical Society. Series C: Applied Statistics|
|Publication status||Published - 2001|
- Adaptive weights smoothing
- Functional and dynamic magnetic resonance imaging
- Signal detection
- Spatial adaptation