A patch-based super resolution algorithm for improving image resolution in clinical mass spectrometry

Scientific Reports
Klára ŠčupákováRon M A Heeren

Abstract

Mass spectrometry imaging (MSI) and histology are complementary analytical tools. Integration of the two imaging modalities can enhance the spatial resolution of the MSI beyond its experimental limits. Patch-based super resolution (PBSR) is a method where high spatial resolution features from one image modality guide the reconstruction of a low resolution image from a second modality. The principle of PBSR lies in image redundancy and aims at finding similar pixels in the neighborhood of a central pixel that are then used to guide reconstruction of the central pixel. In this work, we employed PBSR to increase the resolution of MSI. We validated the proposed pipeline by using a phantom image (micro-dissected logo within a tissue) and mouse cerebellum samples. We compared the performance of the PBSR with other well-known methods: linear interpolation (LI) and image fusion (IF). Quantitative and qualitative assessment showed advantage over the former and comparability with the latter. Furthermore, we demonstrated the potential applicability of PBSR in a clinical setting by accurately integrating structural (i.e., histological) and molecular (i.e., MSI) information from a case study of a dog liver.

References

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Feb 16, 2017·Frontiers in Neuroscience·Saurabh JainDirk Smeets

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Citations

Oct 31, 2019·Clinical Chemistry and Laboratory Medicine : CCLM·Klára ŠčupákováGökhan Ertaylan
Nov 10, 2020·Mass Spectrometry Reviews·Kellen DeLaneyLingjun Li
Jun 16, 2021·Expert Review of Proteomics·Peggi M AngelRichard Drake

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Methods Mentioned

BETA
biopsy
electron microscopy
irradiated

Software Mentioned

ChemomeTricks
HistDistGUI
SCiLS
Slicer
Elastix
PBSR
MATLAB

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