Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data
Open accessAbstract
Multimodal integration between mass spectrometry imaging (MSI) and radiology-established modalities such as magnetic resonance imaging (MRI) would allow the investigations of key questions in complex biological systems such as the central nervous system. Such integration would provide complementary multiscale data to bridge the gap between molecular and anatomical phenotypes, potentially revealing new insights into molecular mechanisms underlying anatomical pathologies presented on MRI. Automatic coregistration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and nonlinear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to nonlinearly align 3D MSI to MRI data, identify and reconstruct biologically relevant molecular patterns in 3D, and fuse the MSI datacube to the MRI space. We demonstrate our method using multimodal high-spectral resolution matrix-assisted laser desorption ionization (MALDI) 9.4 T MSI and 7 T in vivo MRI data, acquired from a patient-derived, xenograft mouse brain model of glioblastoma following administration of the EGFR inhibitor drug of Erlotinib. Results show the distribution of some identified molecular ions of the EGFR inhibitor erlotinib, a phosphatidylcholine lipid, and cholesterol, which were reconstructed in 3D and mapped to the MRI space. The registration quality was evaluated on two normal mouse brains using the Dice coefficient for the regions of brainstem, hippocampus, and cortex. The method is generic and can therefore be applied to hyperspectral images from different mass spectrometers and integrated with other established in vivo imaging modalities such as computed tomography (CT) and positron emission tomography (PET).
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Breast cancer tumors developed in mice were investigated by CA-MSI using Gadoteridol, revealing tumor margins and vasculature by localizing contrast agents.
“Breast cancer tumors developed in mice were investigated by CA-MSI using Gadoteridol, revealing tumor margins and vasculature by localizing the contrast agents.”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [146]
The Materials and Methods and Results describe a mouse brain glioblastoma model studied with MALDI MSI and MRI after administration of erlotinib—not breast tumors investigated with Gadoteridol. The Results map drug and tumor-associated molecular signals but do not report localizing a contrast agent to reveal tumor margins or vasculature. Page numbers were not provided in our copy. (LLM, conf=0.88)
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Experimental — accuracy not yet measuredCombination of MSI with other techniques is prospective for bridging gaps between unalike scales.
“Thus, a combination of MSI with other techniques is prospective for bridging the gaps between unalike scales [145,146].”
From Approaches to Improve EPR-Based Drug Delivery for Cancer Therapy and Diagnosis (2023) · cites this paper as [146]
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- Authors
- Abdelmoula, W. M., Regan, M. S., Lopez, B. G. C., Randall, E. C., Lawler, S., Mladek, A. C., Nowicki, M. O., Marin, B. M., Agar, J. N., Swanson, K. R., Kapur, T., Sarkaria, J. N., Wells, W., & Agar, N. Y. R.
- Affiliations
- Brigham and Women's Hospital (US)
- Mayo Clinic (US)
- Northeastern University (US)
- Corresponding author
- Agar, Nathalie Y. R.
- ORCID
- Venue
- Analytical Chemistry
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-04-01
- Volume
- 91
- Issue
- 9
- Pages
- 6206-6216
- ISSN
- 0003-2700 (ISSN-L)
- 1520-6882
- PMC
- PMC6691508
- OpenAlex
- W2931159242
- Cited by
- 62
- References
- 58
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 7 · 2023: 6 · 2022: 8 · 2021: 16 · 2020: 11 · 2019: 5
- FWCI
- 3.99
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Analytical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute of Biomedical Imaging and Bioengineering (P41EB015896)
- Pediatric Low Grade Astrocytoma Foundation
- National Cancer Institute (U54 CA210180, R25 CA-89017)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- mass spectrometry imaging, magnetic resonance imaging, multimodal data integration, stochastic neighbor embedding, MALDI mass spectrometry imaging, glioblastoma, erlotinib, phosphatidylcholine, cholesterol, data fusion, central nervous system
- MeSH terms
- Automation, Magnetic Resonance Imaging, Tomography, X-Ray Computed, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization, Imaging, Three-Dimensional, Positron-Emission Tomography
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