Bayesian analysis of time-domain magnetic resonance signals

A Bayesian approach to characterising multi-phase flows using magnetic resonance: Application to bubble flows

“…The likelihood was defined by Eqs. (6) and (7). Analysis was performed using 128 data points in k-space.…”

“…It has been shown to improve the recovery of an MR spectrum from noisy data [7] and to improve the accuracy of flow measurements by enabling a sparse sampling procedure to be used [9]. In this work we exploit both these advantages of Bayesian analysis to enable measurements of the bubble size distribution in a dynamic system.…”

“…Bayesian analysis has previously been used in a variety of MR applications [7][8][9][10]. It has been shown to improve the recovery of an MR spectrum from noisy data [7] and to improve the accuracy of flow measurements by enabling a sparse sampling procedure to be used [9].…”

Extraction of T2 from NMR linewidths in simple spin systems by use of reference deconvolution

“…1) and therefore a time domain data analysis is necessary. We selected two methods which best fulfill our tasks and used them in combination: (i) the Bayesian method 33 (implemented in Bruker's XWINNMR program) in the 'parameter optimization' regime; (ii) the filter diagonalization method (FDM). 34 It should be noted that methods for time domain data analysis model the FID as a mixture of exponentially damped sinusoids plus white noise, i.e.…”

Extending the use of Earth’s Field NMR using Bayesian methodology: Application to particle sizing

“…Bayesian analysis has previously been used to reconstruct spectral data in NMR [19][20][21], estimate velocity and diffusion during pulsatile flow [21], and to obtain measurements of bubble size distributions in gas-liquid flows [22]. In the Bayesian approach to data analysis, we seek to identify a particular parameter of interest, and do not necessarily require the reconstruction of an entire image.…”