High-resolution near-seabed velocity and sediment transport profiling

Unen, R.F. van; Ruiten, K.J.M. van; Bosman, J.J.

doi:10.1109/oceans.1997.634412

Cited by 2 publications

(1 citation statement)

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“…Recently, Abdelmoula et al presented an efficient computational method based on hierarchical stochastic neighbor embedding (HSNE) 30 to analyze 3D MSI data and identify multi-scale molecular patterns with manageable computational complexities 31 . HSNE is a scalable extension of t-SNE used for unsupervised non-linear dimensionality reduction of large and complex datasets 32 , and it is applied here to identify molecular patterns from the 3D MALDI MSI data and fuse it to the MRI.…”

Section: Introductionmentioning

confidence: 99%

Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data

et al. 2019

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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 co-registration between 3D MSI/MRI is a computationally challenging process due to dimensional complexity, MSI data sparsity, lack of direct spatial-correspondences, and non-linear tissue deformation. Here, we present a new computational approach based on stochastic neighbor embedding to non-linearly 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 MALDI 9.4 Tesla MSI and 7 Tesla 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

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Section: Introductionmentioning

confidence: 99%