# A Structural Graph-Based Method for MRI Analysis

## TL;DR

This paper introduces a novel graph-based method for analyzing pediatric MRI scans, addressing unique challenges such as anatomical development and noise, with preliminary results indicating its potential effectiveness.

## Contribution

A new structural relational graph-based technique tailored for pediatric MRI analysis, focusing on robustness against developmental variability and noise.

## Key findings

- Preliminary results demonstrate viability on liver MRI data.
- Method shows promise for pediatric liver and brain tumor segmentation.
- Future work will extend to detailed segmentation tasks.

## Abstract

The importance of imaging exams, such as Magnetic Resonance Imaging (MRI), for the diagnostic and follow-up of pediatric pathologies and the assessment of anatomical structures' development has been increasingly highlighted in recent times. Manual analysis of MRIs is time-consuming, subjective, and requires significant expertise. To mitigate this, automatic techniques are necessary. Most techniques focus on adult subjects, while pediatric MRI has specific challenges such as the ongoing anatomical and histological changes related to normal development of the organs, reduced signal-to-noise ratio due to the smaller bodies, motion artifacts and cooperation issues, especially in long exams, which can in many cases preclude common analysis methods developed for use in adults. Therefore, the development of a robust technique to aid in pediatric MRI analysis is necessary. This paper presents the current development of a new method based on the learning and matching of structural relational graphs (SRGs). The experiments were performed on liver MRI sequences of one patient from ICr-HC-FMUSP, and preliminary results showcased the viability of the project. Future experiments are expected to culminate with an application for pediatric liver substructure and brain tumor segmentation.