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Volume: 33 | Article ID: art00007
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Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imaging
  DOI :  10.2352/ISSN.2470-1173.2021.15.COIMG-290  Published OnlineJanuary 2021
Abstract

A Supervised Learning Approach for Dynamic Sampling (SLADS) addresses traditional issues with the incorporation o stochastic processes into a compressed sensing method. Statistical features, extracted from a sample reconstruction, estimate entropy reduction with regression models, in order to dynamically determine optimal sampling locations. This work introduces an enhanced SLADS method, in the form of a Deep Learning Approach for Dynamic Sampling (DLADS), showing reductions in sample acquisition times for high-fidelity reconstructions between ˜ 70–80% over traditional rectilinear scanning. These improvements are demonstrated for dimensionally asymmetric, high-resolution molecular images of mouse uterine and kidney tissues, as obtained using Nanospray Desorption Electro- Spray Ionization (nano-DESI) Mass Spectrometry Imaging (MSI). The methodology for training set creation is adjusted to mitigate stretching artifacts generated when using prior SLADS approaches. Transitioning to DLADS removes the need for feature extraction, further advanced with the employment of convolutional layers to leverage inter-pixel spatial relationships. Additionally, DLADS demonstrates effective generalization, despite dissimilar training and testing data. Overall, DLADS is shown to maximize potential experimental throughput for nano-DESI MSI.

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David Helminiak, Hang Hu, Julia Laskin, Dong Hye Ye, "Deep Learning Approach for Dynamic Sparse Sampling for High-Throughput Mass Spectrometry Imagingin Proc. IS&T Int’l. Symp. on Electronic Imaging: Computational Imaging XIX,  2021,  pp 290-1 - 290-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.15.COIMG-290

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