Accelerating multidimensional NMR and MRI experiments using iterated maps

Abstract

Techniques that accelerate data acquisition without sacrificing the advantages of fast Fourier transform (FFT) reconstruction could benefit a wide variety of magnetic resonance experiments. Here we discuss an approach for reconstructing multidimensional nuclear magnetic resonance (NMR) spectra and MR images from sparsely-sampled time domain data, by way of iterated maps. This method exploits the computational speed of the FFT algorithm and is done in a deterministic way, by reformulating any a priori knowledge or constraints into projections, and then iterating. In this paper we explain the motivation behind this approach, the formulation of the specific projections, the benefits of using a ‘QUasi-Even Sampling, plus jiTter’ (QUEST) sampling schedule, and various methods for handling noise. Applying the iterated maps method to real 2D NMR and 3D MRI of solids data, we show that it is flexible and robust enough to handle large data sets with significant noise and artifacts.

Fast multi-dimensional NMR acquisition and processing using the sparse FFT

“…The selected hyper-complex data points were converted to the complex virtual echo representation (Frey et al 2013;Mayzel et al 2014), which contained 174 x 2096 points out of the full complex array with dimensions 174 x 32 x 32…”

“…without producing the complete spectrum at any stage of the procedure. The latter requirement excludes powerful non-parametric NUS processing algorithms designed to reconstruct the full spectrum, such as Maximum Entropy (ME) (Barna et al 1987;Hoch et al 2014), Projection Reconstruction (PR) (Freeman and Kupce 2003), Spectroscopy by Integration of Frequency and Time Domain Information (SIFT) (Frey et al 2013;Matsuki et al 2009), Signal Separation Algorithm (SSA) (Stanek et al 2012), Compressed Sensing (Holland et al 2011;Kazimierczuk and Orekhov 2011), and Low Rank reconstruction (Qu et al 2014). The parametric methods such as Bayesian (Bretthorst 1990), maximum likelihood (Chylla and Markley 1995), and multidimensional decomposition (MDD) approximate the spectrum using a relatively small number of adjustable parameters, and thus are not limited in spectral dimensionality and resolution.…”

Measurement and Characterization of Hydrogen–Deuterium Exchange Chemistry Using Relaxation Dispersion NMR Spectroscopy

“…Even with the advent of modern cold probe technology and high-field spectrometers, data acquisition may prove to be a bottleneck due to the necessity of collecting each point on the dispersion profile as a two-dimensional dataset. This may at least be partially mitigated by leveraging nonuniform sampling schemes , to shorten acquisition times. The application of RD to protein systems at natural abundance may further be hindered through decreased spectral quality owing to the enhanced transverse relaxation due to dipolar interactions.…”

The causality principle in the reconstruction of sparse NMR spectra

“…Without going into details irrelevant to our work, we only note that Nagayama's approach is not suitable for producing quantitative spectra (for details see [11] ). While preparing this paper, we found that a signal representation similar VE was recently used in a new method for NUS spectra reconstruction [12] . However, there the VE was deeply entangled into the methodspecific algorithm and general implications of the VE presentations for NUS spectra reconstruction were not discussed.…”