Abstract #5178

Choice of Segmentation Algorithm to Determine Ventilated Volume Fraction from Oxygen-Enhanced MRI in Cystic Fibrosis and Healthy Volunteers

Marta Tibiletti¹, Christopher Short^2,3,4,5, Jo Naish^1,6, John C Waterton^1,7, Mary Abkir², Thomas Semple^2,5,8, Simon Padley^2,5,9, Jane C Davies^2,4,5,8, and Geoff JM Parker^1,10

¹Bioxydyn Ltd, Manchester, United Kingdom, ²National Heart & Lung Institute, Imperial College London, London, United Kingdom, ³Royal Brompton & Harefield Hospitals, part of Guys and St Thomas’ Trust, London, United Kingdom, ⁴European Cystic Fibrosis Society, Lung Clearance Index Core Facility, London, United Kingdom, ⁵NIHR Imperial Biomedical Research Centre, Imperial College London, London, United Kingdom, ⁶MCMR, , Manchester University NHS Foundation Trust, Wythenshawe, Manchester, United Kingdom, ⁷Centre for Imaging Sciences, University of Manchester, Manchester, United Kingdom, ⁸Royal Brompton Hospital, Guy's & St Thomas’ Trust, London, United Kingdom, ⁹2 Royal Brompton & Harefield Hospitals, part of Guys and St Thomas’ Trust, London, United Kingdom, ¹⁰UCL Hawkes Institute, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom

Synopsis

Keywords: Lung, Lung

Motivation: We aim to refine lung ventilation quantification using R₂* based oxygen-enhanced MRI (OE-MRI) in cystic fibrosis (CF).

Goal(s): This study seeks to evaluate various segmentation methods for calculating ventilated volume fraction (VVF) from OE-MRI and to assess their correlation with lung function metrics in CF and healthy volunteers.

Approach: We applied multiple segmentation algorithms and compared their repeatability in healthy volunteers, and correlation with the lung clearance index (LCI_2.5) in CF patients.

Results: Several methods studied demonstrated good repeatability and good correlation with LCI_2.5. Only one method found a significant difference in VVF between CF and HV.

Impact: This study provides a direct comparison of segmentation methods for quantifying ventilated lung volume in cystic fibrosis from dynamic oxygen-enhanced MRI. Our results could improve the assessment of lung function in patients, by improving repeatability and sensitivity to disease.

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