Enhancing the Throughput of FT Mass Spectrometry Imaging Using Compressed Sensing and Subspace Modeling

Abstract

Mass spectrometry imaging (MSI) allows for untargeted mapping of the chemical compositions of tissues with attomole detection limits. MSI using Fourier transform-based mass spectrometers, such as FT-ion cyclotron resonance (FT-ICR), grants the ability to examine the chemical space with unmatched mass resolution and mass accuracy. However, direct imaging of large tissue samples on FT-ICR is restrictively slow. In this work, we present an approach that combines the subspace modeling of ICR temporal signals with compressed sensing to accelerate high-resolution FT-ICR MSI. A joint subspace and sparsity constrained reconstruction enables the creation of high-resolution imaging data from the sparsely sampled and short-time acquired transients. Simulation studies and experimental implementation of the proposed acquisition in investigation of brain tissues demonstrate a factor of 10 enhancement in throughput of FT-ICR MSI, without the need for instrumental or hardware modifications.