{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:12:17Z","timestamp":1760238737979,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In this study, we present a time-efficient protocol for thoracic volume calculation as a proxy for total lung volume. We hypothesize that lung volume can be calculated indirectly from this thoracic volume. We compared the measured thoracic volume with manually segmented and automatically thresholded lung volumes, with manual segmentation as the gold standard. A linear regression formula was obtained and used for calculating the theoretical lung volume. This volume was compared with the gold standard volumes. In healthy animals, thoracic volume was 887.45 mm3, manually delineated lung volume 554.33 mm3 and thresholded aerated lung volume 495.38 mm3 on average. Theoretical lung volume was 554.30 mm3. Finally, the protocol was applied to three animal models of lung pathology (lung metastasis and transgenic primary lung tumor and fungal infection). In confirmed pathologic animals, thoracic volumes were: 893.20 mm3, 860.12 and 1027.28 mm3. Manually delineated volumes were 640.58, 503.91 and 882.42 mm3, respectively. Thresholded lung volumes were 315.92 mm3, 408.72 and 236 mm3, respectively. Theoretical lung volume resulted in 635.28, 524.30 and 863.10.42 mm3. No significant differences were observed between volumes. This confirmed the potential use of this protocol for lung volume calculation in pathologic models.<\/jats:p>","DOI":"10.3390\/jimaging8080204","type":"journal-article","created":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T12:53:45Z","timestamp":1658494425000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lung Volume Calculation in Preclinical MicroCT: A Fast Geometrical Approach"],"prefix":"10.3390","volume":"8","author":[{"given":"Juan Antonio","family":"Camara","sequence":"first","affiliation":[{"name":"Preclinical Therapeutics Core, University of California San Francisco, San Francisco, CA 94158, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8299-3880","authenticated-orcid":false,"given":"Anna","family":"Pujol","sequence":"additional","affiliation":[{"name":"Onna Therapeutics, 08028 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan Jose","family":"Jimenez","sequence":"additional","affiliation":[{"name":"Preclinical Imaging Platform, Vall d\u2019Hebron Institute of Research, 08035 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jaime","family":"Donate","sequence":"additional","affiliation":[{"name":"Preclinical Imaging Platform, Vall d\u2019Hebron Institute of Research, 08035 Barcelona, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4293-7558","authenticated-orcid":false,"given":"Marina","family":"Ferrer","sequence":"additional","affiliation":[{"name":"Gnotobiotics Core Facility, University of California San Francisco, San Francisco, CA 94158, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5633-3993","authenticated-orcid":false,"given":"Greetje","family":"Vande Velde","sequence":"additional","affiliation":[{"name":"Biomedical MRI\/MoSAIC, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven, 3001 Leuven, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17598","DOI":"10.1038\/s41598-019-53876-x","article-title":"Radiosafe micro-computed tomography for longitudinal evaluation of murine disease models","volume":"9","author":"Berghen","year":"2019","journal-title":"Sci. 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