Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23506 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913919045468160 |
|---|---|
| author | Xin, Bowen Hickey, Rohan Blake, Tamara Jin, Jin Wainwright, Claire E Benkert, Thomas Stemmer, Alto Sly, Peter Coman, David Dowling, Jason |
| author_facet | Xin, Bowen Hickey, Rohan Blake, Tamara Jin, Jin Wainwright, Claire E Benkert, Thomas Stemmer, Alto Sly, Peter Coman, David Dowling, Jason |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23506 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI Xin, Bowen Hickey, Rohan Blake, Tamara Jin, Jin Wainwright, Claire E Benkert, Thomas Stemmer, Alto Sly, Peter Coman, David Dowling, Jason Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Medical Physics 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). |
| title | Artificial Intelligence-assisted Pixel-level Lung (APL) Scoring for Fast and Accurate Quantification in Ultra-short Echo-time MRI |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Medical Physics |
| url | https://arxiv.org/abs/2506.23506 |