@inproceedings{503a4e62bb054e06b9472d4424f1759b,
title = "Deep Learning-Based Air Trapping Quantification Using Paired Inspiratory-Expiratory Ultra-low Dose CT",
abstract = "Air trapping (AT) is a frequent finding in early cystic fibrosis (CF) lung disease detectable by imaging. The correct radiographic assessment of AT on paired inspiratory-expiratory computed tomography (CT) scans is laborious and prone to inter-reader variation. Conventional threshold-based methods for AT quantification are primarily designed for adults and less suitable for children. The administered radiation dose, in particular, plays an important role, especially for children. Low dose (LD) CT is considered established standard in pediatric lung CT imaging but also ultra-low dose (ULD) CT is technically feasible and requires comprehensive validation. We investigated a deep learning approach to quantify air trapping on ULDCT in comparison to LDCT and assessed structure-function relationships by cross-validation against multiple breath washout (MBW) lung function testing. A densely connected convolutional neural network (DenseNet) was trained on 2-D patches to segment AT. The mean threshold from radiographic assessments, performed by two trained radiologists, was used as ground truth. A grid search was conducted to find the best parameter configuration. Quantitative AT (QAT), defined as the percentage of AT in the lungs detected by our DenseNet models, correlated strongly between LD and ULD. Structure-function relationships were maintained. The best model achieved a patch-based DICE coefficient of 0.82 evaluated on the test set. AT percentages correlated strongly with MBW results (LD: $$R = 0.76$$, $$p < 0.001$$ ; ULD: $$R = 0.78$$, $$p < 0.001$$ ). A strong correlation between LD and ULD ($$R = 0.96$$, $$p < 0.001$$ ) and small ULD-LD differences (mean difference $$-1.04 \pm 3.25\%$$ ) were observed.",
keywords = "Air Trapping Quantification, Cystic Fibrosis, Deep Learning",
author = "Muller, {Sarah M.} and Sundaresh Ram and Bayfield, {Katie J.} and Reuter, {Julia H.} and Sonja Gestewitz and Lifeng Yu and Wielp{\"u}tz, {Mark O.} and Kauczor, {Hans Ulrich} and Heussel, {Claus P.} and Robinson, {Terry E.} and Bartholmai, {Brian J.} and Hatt, {Charles R.} and Robinson, {Paul D.} and Galban, {Craig J.} and Oliver Weinheimer",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.; 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",
year = "2023",
doi = "10.1007/978-3-031-43898-1_42",
language = "English (US)",
isbn = "9783031438974",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "432--441",
editor = "Hayit Greenspan and Hayit Greenspan and Anant Madabhushi and Parvin Mousavi and Septimiu Salcudean and James Duncan and Tanveer Syeda-Mahmood and Russell Taylor",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings",
}