{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T18:25:28Z","timestamp":1763749528122,"version":"3.44.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Spectral detector computed tomography (SDCT), a cutting-edge technique in veterinary medicine, offers various options for characterizing soft tissues. One such option is the virtual non-contrast (VNC) algorithm, which reconstructs pre-contrast images from post-contrast spectral data by identifying and subtracting iodine pixels. This method has demonstrated accuracy in assessing abdominal organs in healthy canine patients. To determine whether this algorithm can be applied in a clinical setting to reduce radiation dose, scan time, and, consequently, the duration of general anesthesia, its accuracy in patients with pathology must be evaluated.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Our study compared the Hounsfield units (HUs) measured in VNC and true unenhanced (TUE) images for splenic pathologies in general, as well as for specific types of pathologies based on their imaging characteristics. All differences were determined through subtraction and evaluated by using cut-off values of \u2264 5, \u2264 10, and \u2264 15 HUs. Differences in HUs of \u2264 10 were deemed negligible, while differences \u2264 15 HUs were considered acceptable. Two one-sided t-tests were performed. Additionally, image quality and iodine subtraction were evaluated using a five-point Likert scale.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For splenic pathologies in general, lesions classified by imaging characteristics and various pathologies, the VNC technique demonstrated high efficiency in contrast medium subtraction and exhibited a strong agreement with TUE images regarding their HUs. In 305 drawn regions of interest (ROIs), the differences in HUs between TUE and VNC were \u2264 15 HUs in 98.0%, \u2264 10 HUs in 93.1%, and at least 66.2% were below the limit of 5 HUs. SDCT images provided better image quality than conventional computed tomography images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Our study suggests that VNC images reconstructed from post-contrast SDCT data may serve as a promising alternative to the standard use of a pre- and post-contrast series. The VNC images provide high quality and reliability in imaging structurally altered splenic tissue, but show a potential weakness in the calculation of some mineralized lesions.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fvets.2025.1645439","type":"journal-article","created":{"date-parts":[[2025,9,26]],"date-time":"2025-09-26T08:53:16Z","timestamp":1758876796000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Spectral detector computed tomography imaging of histologically confirmed splenic pathologies in 30 canine patients: a comparison of virtual non-contrast images and true unenhanced images"],"prefix":"10.3389","volume":"12","author":[{"given":"Alkje M.","family":"van Gemmeren","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lydia K.","family":"Clau\u00dfen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Philipp","family":"Lietz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Meller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adriano","family":"Wang-Leandro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreas","family":"Beineke","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Verena","family":"Nerschbach","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Holger A.","family":"Volk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kristina","family":"Merhof","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"872618","DOI":"10.3389\/fvets.2022.872618","article-title":"A machine learning-based approach for classification of focal splenic lesions based on their CT features","volume":"9","author":"Burti","year":"2022","journal-title":"Front Vet Sci"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"105773","DOI":"10.1016\/j.tvjl.2021.105773","article-title":"Computed tomography features for differentiating malignant and benign focal liver lesions in dogs: a meta-analysis","volume":"278","author":"Burti","year":"2021","journal-title":"Vet 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