M²aia (MSI applications for interactive analysis in MITK) is a software tool enabling interactive signal processing and visualisation of mass spectrometry imaging (MSI) datasets. M²aia extends the open source Medical Imaging and Interaction Toolkit (MITK) [1,2] and provides powerful methods that the MSI community can adopt, exploit and improve further.

M²aia provides features for

• Read/write access to the open standard imzML
• Image interaction and visualization
• Signal processing: Normalization, baseline correction, smoothing and peak picking
• Image segmentation
• Dimensionality reduction
• Multi-modal 2D registration
• 3D MSI reconstruction

Downloads

M²aia	Packages
v2024.01 Github	ubuntu-20.04 ubuntu-22.04 windows-installer windows-zip
archive	http://data.jtfc.de/latest/

Install elastix for image-based registration utilities: elastix 5.0.0

pyM²aia
getting started	Github PyPi Examples

Cite M²aia

Cordes, Jonas, Thomas Enzlein, Christian Marsching, Marven Hinze, Sandy Engelhardt, Carsten Hopf, and Ivo Wolf. “M²aia—Interactive, Fast, and Memory-Efficient Analysis of 2D and 3D Multi-Modal Mass Spectrometry Imaging Data.” GigaScience 10, no. 7 (July 20, 2021): giab049. https://doi.org/10.1093/gigascience/giab049.

Cordes, Jonas, Thomas Enzlein, Carsten Hopf, and Ivo Wolf. “pyM2aia: Python Interface for Mass Spectrometry Imaging with Focus on Deep Learning.” Bioinformatics 40, no. 3 (March 1, 2024): btae133. https://doi.org/10.1093/bioinformatics/btae133.

biotools:m2aia

RRID:SCR_019324

Multi-modal 3D mouse brain data

Cordes J; Enzlein T; Marsching C; Hinze M; Engelhardt S; Hopf C; Wolf I (2021): Supporting data for “M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data” GigaScience Database. http://dx.doi.org/10.5524/100909

Supporting Material

Supporting protocol for use-case 1: N-linked glycan m/z candidate detection in “M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data” dx.doi.org/10.17504/protocols.io.brw2m7ge

Supporting protocol for use-case 1: Dimensionality reduction in “M2aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data” https://dx.doi.org/10.17504/protocols.io.bwybpfsn

Supporting capsule for use-case 1: R-based processing in “M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data”doi.org/10.24433/CO.2384502.v1

Supporting capsule for use-case 1: Command-line application based pre-processing in “M²aia - Interactive, fast and memory efficient analysis of 2D and 3D multi-modal mass spectrometry imaging data” doi.org/10.24433/CO.7662658.v1

External references

[1] Nolden, M., et al. 2013. “The Medical Imaging Interaction Toolkit: Challenges and Advances.” International Journal of Computer Assisted Radiology and Surgery 8 (4): 607–20. doi:10.1007/s11548-013-0840-8.

[2] Wolf, I., et al. 2005. “The Medical Imaging Interaction Toolkit.” Medical Image Analysis 9 (6): 594–604. doi:10.1016/j.media.2005.04.005.

[3] https://github.com/SuperElastix/elastix (v5.0.0)