Cardinal series to sort out defective samples in magnetic resonance data sets.

NMR signals are unavoidably impaired with noise stemming from the electronic circuits of the spectrometer. This noise is most often white and Gaussian and can be greatly reduced by applying low pass analogue and digital filters. Nevertheless, extra noise with other statistics than Gaussian may interfere with the signal, e.g. when auxiliary electrical devices are placed near the magnet of the NMR spectrometer. This paper reports on how one can make use of this difference in statistics to remove the noise caused by electrical devices before any further data processing. The algorithm is based on the use of a new linear low pass filter, which consists in fitting NMR data in the time domain with a cardinal series and whose spectral width can be controlled. Over other filtering methods such filter has the advantage of not distorting the signal neither at the beginning nor the end of the acquisition period. The performance of the method is demonstrated by applying it to a data set collected in a flow PGSE experiment and impaired with noise emanated from a brushed DC electric motor.