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.
Mass Recalibration of FT-ICR Mass Spectrometry Imaging Data Using the Average Frequency Shift of Ambient Ions
