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Neuroimaging Field Methods Using Functional Near Infrared Spectroscopy NIRS Neuroimaging to Study Global Child Development: Rural Sub-Saharan Africa

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Roadmap for an imaging and modelling paediatric study in rural NZ.

Haribalan Kumar^1,2,3, Robby Green¹, Daniel M Cornfeld^1,4

¹Mātai Medical Research Institute, Gisborne, New Zealand.

Frontiers in Physiology

|March 27, 2023

Summary

This study introduces a novel organ-system imaging approach for children, combining magnetic resonance imaging and computational modeling. It provides a foundation for better understanding pediatric health and disease through integrated data and personalized models.

Keywords:

MRI image analysis child health and development computational modelling neuroimage analysis radiology

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Whole-body PET/MRI of Pediatric Patients: The Details That Matter

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Published on: December 19, 2017

Area of Science:

Biomedical Engineering
Pediatric Imaging
Computational Physiology

Background:

Existing imaging studies often focus on single organs, creating data silos.
There are significant knowledge gaps in pediatric anatomical structure and physiological function.
A lack of representative pediatric imaging data exists, particularly in New Zealand.

Purpose of the Study:

To develop an integrated organ-system imaging and computational modeling approach for pediatric research.
To address limitations in current pediatric imaging by scanning multiple organ systems simultaneously.
To create minimally disruptive imaging protocols and advanced computational models for children.

Main Methods:

Utilized magnetic resonance imaging (MRI) across multiple organ systems including brain, lungs, heart, muscle, bones, and vascular systems.
Employed advanced image processing algorithms to analyze imaging data.
Developed personalized computational models using integrated imaging and physiological data.

Main Results:

Successfully pilot-tested a minimally disruptive, multi-organ imaging protocol for children.
Demonstrated state-of-the-art image processing techniques for pediatric data.
Generated child-specific measurements and personalized computational models from imaging data.

Conclusions:

The study highlights the necessity of an organ-system approach in pediatric imaging research.
This work represents a significant first step towards integrating imaging and modeling for improved understanding of pediatric health and disease.
The developed methods offer a novel pathway for creating personalized computational models in pediatric populations.