Image resolution in mass spectrometry imaging (MSI) is governed by the sampling probe, the motion of the stage relative to the probe, and the noise inherent for the sample and instrumentation employed. A new image formation model accounting for these variables is presented here. The model shows that the size of the probe, stage velocity, and the rate at which the probe consumes material from the surface govern the amount of blur present in the image. However, the main limiting factor for resolution is the signal-to-noise ratio (SNR). To evaluate blurring and noise effects, a new computational method for measuring lateral resolution in MSI is proposed. A spectral decomposition of the observed image signal and noise is used to determine a resolution number. To evaluate this technique, a silver step edge was prepared. This device was imaged at different pixels sizes using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). A modulation transfer function (MTF) and a noise power spectrum (NPS) were computed for each single-ion image, and resolution was defined as the point of intersection between the MTF and the NPS. Finally, the algorithm was also applied to a MALDI MSI tissue data set.

Mass spectrometry imaging (MSI) has been a key driver of groundbreaking discoveries in a number of fields since its inception more than 50 years ago. Recently, MSI development trends have shifted towards ambient MSI (AMSI) as the removal of sample-preparation steps and the possibility of analysing biological specimens in their natural state have drawn the attention of multiple groups across the world. Nevertheless, the lack of spatial resolution has been cited as one of the main limitations of AMSI. While significant research effort has presented hardware solutions for improving the resolution, software solutions are often overlooked, although they can usually be applied in a cost-effective manner after image acquisition. In this vein, we present two computational methods that we have developed to directly enhance the image resolution post-acquisition. Robust and quantitative resolution improvement is demonstrated for 12 cases of openly accessible datasets across laboratories around the globe. Using the same universally applicable Fourier imaging model, we discuss the possibility of true super-resolution by software for future studies.