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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

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Artificial Intelligence for Neuroimaging in Pediatric Cancer.

Josue Luiz Dalboni da Rocha¹, Jesyin Lai¹, Pankaj Pandey¹

¹Department of Radiology, St. Jude Children's Research Hospital, Memphis, TN 38105, USA.

|February 26, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise in pediatric neuroimaging for cancer, improving diagnosis and treatment. Overcoming challenges like limited data and ethical concerns is key to realizing AI

Keywords:

artificial intelligence cancer craniopharyngioma deep learning low-grade glioma machine learning medulloblastoma neuroimaging

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Area of Science:

Neuroimaging
Artificial Intelligence
Pediatric Oncology

Background:

AI applications in neuroimaging are expanding, but its use in pediatric cancer is limited.
Pediatric neuroimaging for cancer presents unique challenges and requirements.
This review focuses on AI's current role and future potential in this specialized field.

Purpose of the Study:

To review the current state of AI in pediatric neuroimaging for cancer.
To explore potential applications of AI in this domain.
To identify and analyze the challenges hindering AI adoption.

Main Methods:

Comprehensive literature review on AI in pediatric neuroimaging.
Focus on AI for accelerated image acquisition, reduced radiation, and enhanced tumor detection.
Analysis of AI techniques like convolutional neural networks and radiomics, alongside associated challenges.

Main Results:

AI demonstrates potential to improve imaging quality, reduce scan times, and increase diagnostic accuracy.
AI aids in tumor segmentation and outcome prediction for pediatric cancer patients.
Scarcity of pediatric datasets, data sharing issues, and ethical concerns impede progress.

Conclusions:

Future research must prioritize building robust pediatric datasets.
Multi-institutional collaborations are crucial for effective data sharing.
Development of interpretable AI models aligned with clinical and ethical standards is essential.