Respiration plays a critical role in the MR environment, where it may not only be a confounding factor, but also a source of related artifacts. It can be linked to movement artifacts, physiological alterations, induced field inhomogeneity, or interference with the experimental paradigm. Therefore respiratory effects cannot be ignored (see e.g. Thomason et al. 2005).
Even if advances in data analysis techniques can provide better results at the cost of greater complexity, these results are considerably improved by parallel dedicated measurements of the sources of the artifacts. An efficient method which exploits parallel measurements for artifact correction uses acquired respiratory signals to create a principal regressor, along with other derived regressors obtained with a higher order analysis of the signal itself. This approach is known as RETROICOR (Glover et al., 2000). It is clear that a higher quality and sensitivity of acquired respiratory data will lead to an improved quality of all the regressors and finally to a higher quality of artifact correction and final denoised data, independent of the strategy adopted to correct for respiratory artifacts.
With the aim of obtaining the best data quality and the optimal method of artifact correction we have developed the Respiration Belt MR, a sensor for the acquisition of respiratory signals within MR environments.