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Abstract #0305

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

Bowen Xin1, Rohan Hickey2, Tamara Blake2,3, Jin Jin4, Claire E Wainwright2,3, Thomas Benkert5, Alto Stemmer5, Peter Sly2,3, David Coman2,3, and Jason Dowling6
1CSIRO, Sydney, Australia, 2University of Queensland, Brisbane, Australia, 3Queensland Children’s Hospital, Brisbane, Australia, 4Siemens Healthcare Pty Ltd, Brisbane, Australia, 5Siemens Healthineers AG, Forchheim, Germany, 6CSIRO, Brisbane, Australia

Synopsis

Keywords: Lung, Lung, AI, UTE, scoring

Motivation: Conventional grid-based lung MRI scoring for quantifying structural lung damage is time-consuming and lacks pixel-level accuracy, which impacts workflow efficiency and subsequent clinical analysis.

Goal(s): Our goal was to reduce scoring time and improve scoring accuracy.

Approach: We developed a new Artificial intelligence-assisted Pixel-level Lung MRI scoring system, leveraging 1) deep learning to automate time-consuming lung segmentation and 2) pixel-level annotation tools to improve accuracy.

Results: Our scoring system successfully reduced scoring time from 17.5 to 8.2 minutes per participant, and our scoring was statistically more accurate than conventional grid-level scoring.

Impact: AI-assisted pixel-level scoring significantly improved the efficiency and accuracy of lung MRI quantification, which has the potential to streamline the clinical workflow of lung MRI analysis for cystic fibrosis patients and be extended to other lung diseases (e.g., bronchopulmonary dysplasia).

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Keywords