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A New Approach to Perfusion-Weighted Image Analysis: Measurement via Red-Green-Blue Information Inversion.

Taeyeon Kim¹, Youngsoo Kim², Youjin Lee¹

¹Medical Research Institute, Pohang Stroke and Spine Hospital, Pohang, South Korea.

Studies in Health Technology and Informatics

|August 8, 2025

Summary

This summary is machine-generated.

A new Python program analyzes red-green-blue (RGB)-reconstructed perfusion-weighted imaging (PWI) data for region of interest mean values, offering improved accessibility and reduced costs. While effective for some metrics, further validation is needed for others like mean transit time.

Keywords:

RGB reconstruction perfusion-weighted imaging region of interest

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Area of Science:

Medical Imaging
Computational Imaging
Neuroimaging Analysis

Background:

Traditional perfusion-weighted imaging (PWI) analysis faces limitations including manufacturer dependency, high storage costs, and restricted accessibility.
Developing accessible and cost-effective tools for PWI analysis is crucial for broader clinical application.

Purpose of the Study:

To develop and validate a Python-based program for analyzing region of interest (ROI) mean values from red-green-blue (RGB)-reconstructed PWI data.
To overcome the limitations associated with conventional source-image-based analysis methods.

Main Methods:

A novel Python program was created, featuring a user-friendly graphical user interface (GUI) and executable distribution.
The program analyzes RGB-reconstructed PWI data to calculate ROI mean values.
Intraclass correlation coefficient (ICC) was used to assess the reliability of the program's outputs.

Main Results:

High ICC values were observed for cerebral blood volume (CBV) and time to peak (TTP), indicating the feasibility of the RGB-reconstructed PWI analysis approach.
Lower ICC values for mean transit time (MTT) suggest potential discrepancies compared to conventional dynamic susceptibility contrast (DSC)-based data.
The program's design enhances clinical applicability through its user-friendly interface.

Conclusions:

The developed Python program offers a feasible method for analyzing RGB-reconstructed PWI data, particularly for CBV and TTP.
Further algorithmic refinement and validation with multicenter datasets are recommended to address discrepancies in MTT and enhance overall reliability.