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. 2022 Oct 25;26:328. doi: 10.1186/s13054-022-04200-z

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 2 — Dual-energy computed tomography image analysis. Pulmonary gas volume maps were characterized by a HU range of − 1000 to + 100 HU. Pulmonary blood volume maps were characterized by a HU range of − 100 to + 150 HU. To assess hypoxic pulmonary vasoconstriction, pulmonary blood volume distribution was assessed for the whole lung and for the hypoinflated lung, the latter characterized by poorly inflated and non-inflated lung regions (HU between − 500 and + 100). A region of interest for hypoinflated lung (Hypoinflation ROI) was obtained extrapolating hypoinflated areas (Hypoinflation Map) from the pulmonary gas volume map. Hence, the hypoinflated ROI was applied to pulmonary blood volume map to analyze blood volume distribution of the hypoinflated areas