Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging

TL;DR

This paper introduces DLADS, a deep learning-based dynamic sampling method for MSI that significantly reduces acquisition time while maintaining high-quality molecular imaging, outperforming previous methods in efficiency and accuracy.

Contribution

The paper presents a novel CNN-based dynamic sampling approach for MSI that improves throughput by 70% and enhances reconstruction quality over existing supervised learning methods.

Findings

DLADS achieves a 70% throughput improvement in simulated MSI acquisitions.

DLADS improves regression performance by up to 36.7% over SLADS-LS.

Reconstruction quality increases by up to 6.0% with DLADS.

Abstract

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.