Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI

TL;DR

This paper introduces an AI-assisted pixel-level scoring system for lung MRI that significantly improves speed and accuracy in quantifying lung damage in cystic fibrosis, potentially enhancing clinical workflows.

Contribution

The study develops a novel AI-based pixel-level scoring method for lung MRI, demonstrating faster and more accurate quantification compared to previous grid-level approaches.

Findings

Scoring time reduced to 8.2 minutes per subject.

Pixel-level scoring is statistically more accurate (p=0.021).

Strong correlation with grid-level scoring (R=0.973).

Abstract

Lung magnetic resonance imaging (MRI) with ultrashort echo-time (UTE) represents a recent breakthrough in lung structure imaging, providing image resolution and quality comparable to computed tomography (CT). Due to the absence of ionising radiation, MRI is often preferred over CT in paediatric diseases such as cystic fibrosis (CF), one of the most common genetic disorders in Caucasians. To assess structural lung damage in CF imaging, CT scoring systems provide valuable quantitative insights for disease diagnosis and progression. However, few quantitative scoring systems are available in structural lung MRI (e.g., UTE-MRI). To provide fast and accurate quantification in lung MRI, we investigated the feasibility of novel Artificial intelligence-assisted Pixel-level Lung (APL) scoring for CF. APL scoring consists of 5 stages, including 1) image loading, 2) AI lung segmentation, 3) lung-bounded slice sampling, 4) pixel-level annotation, and 5) quantification and reporting. The results shows that our APL scoring took 8.2 minutes per subject, which was more than twice as fast as the previous grid-level scoring. Additionally, our pixel-level scoring was statistically more accurate (p=0.021), while strongly correlating with grid-level scoring (R=0.973, p=5.85e-9). This tool has great potential to streamline the workflow of UTE lung MRI in clinical settings, and be extended to other structural lung MRI sequences (e.g., BLADE MRI), and for other lung diseases (e.g., bronchopulmonary dysplasia).