  Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning,
takes hours to days for high spatial resolution acquisitions. Given that most
pixels within a sample's field of view are often neither relevant to underlying
biological structures nor chemically informative, MSI presents as a prime
candidate for integration with sparse and dynamic sampling algorithms. During a
scan, stochastic models determine which locations probabilistically contain
information critical to the generation of low-error reconstructions. Decreasing
the number of required physical measurements thereby minimizes overall
acquisition times. A Deep Learning Approach for Dynamic Sampling (DLADS),
utilizing a Convolutional Neural Network (CNN) and encapsulating molecular mass
intensity distributions within a third dimension, demonstrates a simulated 70%
throughput improvement for Nanospray Desorption Electrospray Ionization
(nano-DESI) MSI tissues. Evaluations are conducted between DLADS and a
Supervised Learning Approach for Dynamic Sampling, with Least-Squares
regression (SLADS-LS) and a Multi-Layer Perceptron (MLP) network (SLADS-Net).
When compared with SLADS-LS, limited to a single m/z channel, as well as
multichannel SLADS-LS and SLADS-Net, DLADS respectively improves regression
performance by 36.7%, 7.0%, and 6.2%, resulting in gains to reconstruction
quality of 6.0%, 2.1%, and 3.4% for acquisition of targeted m/z.
