# Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging

arXiv: https://arxiv.org/abs/2210.13415 · HTML: https://mlsanity.com/paper/2210.13415/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 5 of 5)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A Framework for Dynamic Image Sampling Based on Supervised Learning (SLADS)](https://mlsanity.com/paper/1703.04653/) (2017)
- [SLADS-Net: Supervised Learning Approach for Dynamic Sampling using Deep Neural Networks](https://mlsanity.com/paper/1803.02972/) (2018)
- [Reduced Electron Exposure for Energy-Dispersive Spectroscopy using Dynamic Sampling](https://mlsanity.com/paper/1707.03848/) (2017)
- [U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling](https://mlsanity.com/paper/1807.02233/) (2018)

## Abstract (arXiv)

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.