In vivo 4D x-ray dark-field lung imaging in mice
Date
2025
Authors
How, Y.Y.
Reyne, N.
Croughan, M.K.
Cmielewski, P.
Batey, D.
Costello, L.F.
Smith, R.
Ahlers, J.N.
Cholewa, M.
Kolodziej, M.
Editors
Advisors
Journal Title
Journal ISSN
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Type:
Journal article
Citation
IEEE Transactions on Medical Imaging, 2025; PP:1-12
Statement of Responsibility
Ying Ying How, Nicole Reyne, Michelle K. Croughan, Patricia Cmielewski, Daniel Batey, Lucy F. Costello, Ronan Smith, Jannis N. Ahlers, Marian Cholewa, Magdalena Kolodziej, Julia Duerr, Marcus A. Mall, Marcus J. Kitchen, Marie-Liesse Asselin-Labat, David M. Paganin, Martin Donnelley, Kaye S. Morgan
Conference Name
Abstract
X-ray dark-field imaging is well-suited to visualizing the health of the lungs because the alveoli create a strong dark-field signal. However, time-resolved and tomographic (i.e., 4D) dark-field imaging is challenging, since most x-ray dark-field techniques require multiple sample exposures, captured while scanning the position of crystals or gratings. Here, we present the first in vivo 4D x-ray dark-field lung imaging in mice. This was achieved by synchronizing the data acquisition process of a single-exposure grid-based imaging approach with the breath cycle. The short data acquisition time per dark-field projection made this approach feasible for 4D x-ray dark-field imaging by minimizing the motion-blurring effect and the total time required. Images were captured from a control mouse and from mouse models of muco-obstructive disease and lung cancer, where a change in the size of the alveoli was expected. This work demonstrates that the 4D dark-field signal provides complementary tomographic information that is inaccessible from conventional attenuation-based CT images, in particular, measurements that indicate changes in the size of the alveoli from different parts of the lungs during the breath cycle, with examples shown across the different models. By quantifying the dark-field signal and relating it to physical properties of the alveoli, this technique could be used to perform functional lung imaging that allows the assessment of both global and regional lung conditions where the size or expansion of the alveoli is affected.
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OnlinePubl
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/