Multimodal Preprocessing
Multimodal Preprocessing - Related Videos
11:28
Biofunctionalized Prussian Blue Nanoparticles for Multimodal Molecular Imaging Applications
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Cited by 17 •
2015
This protocol describes the synthesis of biofunctionalized Prussian blue nanoparticles and their use as multimodal, molecular imaging agents. The nanoparticles have a core-shell design where gadolinium or manganese ions within the nanoparticle core generate MRI contrast. The biofunctional shell contains fluorophores for fluorescence imaging and targeting ligands for molecular targeting.
19:32
Preterm EEG: A Multimodal Neurophysiological Protocol
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Cited by 39 •
2012
This video explains the background theory of the neonatal EEG activity and the sensory responses, followed by a live demonstration of their recording in neonatal intensive care unit.
06:48
A Multimodal Wide-Field Fourier-Transform Raman Microscope
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2025
A wide-field Fourier-transform microscope, based on a compact and ultra-stable birefringent interferometer, allows the parallel acquisition of spectra for all pixels of a 2D detector. The time-domain approach enables the disentanglement of photoluminescence and Raman signals, and allows rapid Raman mapping (~5 ms/pixel) with ~1-µm spatial and 23-cm-1 spectral resolution.
06:33
Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement
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Cited by 1 •
2013
This paper describes in-house procedures of constructing a preclinical multimodality phantom made of tissue-mimicking (TM) materials for quality assurance (QA) of tumor size measurement in animal imaging modalities such as ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI).
12:38
4D Multimodality Imaging of Citrobacter rodentium Infections in Mice
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Cited by 19 •
2013
Multi-modality imaging is a valuable approach for studying bacterial colonization in small animal models. This protocol outlines infection of mice with bioluminescent Citrobacter rodentium and the longitudinal monitoring of bacterial colonization using composite 3D diffuse light imaging tomography with μCT imaging to create a 4D movie of C. rodentium infection.
FAQs
Multimodal preprocessing creates compatibility by transforming unlike inputs into representations that can be analyzed together. Cleaning removes noise, and correction addresses errors, while normalization reduces differences in scale. Feature extraction can then retain informative characteristics while reducing the amount of data a model must process. These operations help downstream systems compare signals without discarding relationships among modalities.
Alignment and synchronization preserve the relationship between measurements rather than treating each source as an independent stream. This matters when images, audio, video, text, or engineering sensors differ in resolution or sampling rate. Matching their timing or correspondence allows combined analysis to associate observations that belong to the same event, supporting more meaningful sensor fusion and system interpretation.
Normalization places measurements on more comparable scales, while feature extraction converts raw inputs into characteristics suitable for combined analysis. Together, they can reduce unnecessary computational demands and help models use relevant information from different modalities. Their choices also affect whether important relationships are preserved, so preprocessing should balance compact representations with fidelity to the original engineering measurements.
Before joint analysis, a workflow typically addresses noise and errors, then makes the data more comparable through normalization. It may also extract features and align or synchronize measurements when sources differ in format, resolution, or sampling rate. The resulting representations are intended to support combined analysis while retaining relationships among sensor, visual, acoustic, or textual inputs.
An engineering workflow may combine images, text, audio, video, and sensor measurements, each with its own format, resolution, or sampling rate. These differences determine whether the inputs can be compared directly or need correction, normalization, feature extraction, or synchronization. Recognizing the mismatch early helps the workflow produce compatible data for sensor fusion and other combined analyses.
It is especially useful when an engineering system must combine physical measurements with visual, acoustic, or textual information. The approach supports sensor fusion, robotics, industrial monitoring, autonomous systems, and human-machine interaction. In these settings, careful preparation can improve model accuracy, reduce computational demands, and preserve cross-modal relationships that would otherwise be weakened by incompatible inputs.