Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI
Journal:
arXiv
Published Date:
Jun 30, 2025
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).
