MOESIopt: An Integrated Computational Framework for Automated Restoration of Suboptimal Mass Spectrometry and Optical Emission Spectrometry Images

Mass
spectrometry imaging (MSI) and optical emission
spectrometry
imaging (OESI) are powerful label-free techniques for mapping molecular
and elemental distributions in biological and chemical samples. However,
these methods often suffer from artifacts such as pixel misalignment,
streaking, tailing, and high-frequency noise, which compromise image
quality and hinder accurate interpretation. Existing solutions predominantly
rely on hardware improvements or manual curation, which are costly
and not easily scalable. To address these challenges, we introduce
MOESIopt, a modular computational framework that automates the restoration
of suboptimal MS and OES images without requiring hardware modifications
or high-resolution optical references. MOESIopt incorporates a series
of tailored algorithms for low- and high-frequency feature separation,
pixel alignment, destreaking, tailing correction, and adaptive filtering.
The framework supports common data formats (TXT, CSV) and includes
a user-friendly graphical interface for seamless integration into
existing workflows. We demonstrate its effectiveness on diverse data
sets from LA-DBD-OESI/MSI, IR-MALDESI, nano-DESI, and other platforms,
showing significant enhancements in image clarity and structural coherence.
As an open-source tool, MOESIopt offers a versatile, open-source solution
to advance the reproducibility and accessibility of high-quality chemical
imaging.