{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T02:44:18Z","timestamp":1787021058658,"version":"build-2736575974"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T00:00:00Z","timestamp":1728432000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol Exp"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In liver computed tomography (CT), tailoring the contrast injection to the patient\u2019s specific characteristics is relevant for optimal imaging and patient safety. We evaluated a novel algorithm engineered for personalized contrast injection to achieve reproducible liver enhancement centered on 50\u2009HU.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>From September 2020 to August 31, 2022, CT data from consecutive adult patients were prospectively collected at our multicenter premises. Inclusion criteria consisted of an abdominal CT referral for cancer staging or follow-up. For all examinations, a web interface incorporating data from the radiology information system (patient details and examination information) and radiographer-inputted data (patient fat-free mass, imaging center, kVp, contrast agent details, and imaging phase) were used. Calculated contrast volume and injection rate were manually entered into the CT console controlling the injector. Iopamidol 370\u2009mgI\/mL or Iohexol 350\u2009mgI\/mL were used, and kVp varied (80, 100, or 120) based on patient habitus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We enrolled 384 patients (mean age 61.2 years, range 21.1\u201394.5). The amount of administered iodine dose (gI) was not significantly different across contrast agents (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.700), while a significant increase in iodine dose was observed with increasing kVp (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001) and in males\n                      <jats:italic>versus<\/jats:italic>\n                      females (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001), as expected. Despite the differences in administered iodine load, image quality was reproducible across patients with 72.1% of the examinations falling within the desirable range of 40\u201360\u2009HU.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study validated a novel algorithm for personalized contrast injection in adult abdominal CT, achieving consistent liver enhancement centered at 50\u2009HU.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Relevance statement<\/jats:title>\n                    <jats:p>In healthcare\u2019s ongoing shift towards personalized medicine, the algorithm offers excellent potential to improve diagnostic accuracy and patient management, particularly for the detection and follow-up of liver malignancies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:list list-type=\"bullet\">\n                        <jats:list-item>\n                          <jats:p>The algorithm achieves reproducible liver enhancement, promising improved diagnostic accuracy and patient management in diverse clinical settings.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>The real-world study demonstrates this algorithm\u2019s adaptability to different variables ensuring high-quality liver imaging.<\/jats:p>\n                        <\/jats:list-item>\n                        <jats:list-item>\n                          <jats:p>A personalized algorithm optimizes liver CT, improving the visibility, conspicuity, and follow-up of liver lesions.<\/jats:p>\n                        <\/jats:list-item>\n                      <\/jats:list>\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Graphical Abstract<\/jats:title>\n                  <\/jats:sec>","DOI":"10.1186\/s41747-024-00492-8","type":"journal-article","created":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T09:01:53Z","timestamp":1728464513000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Validation of a multi-parameter algorithm for personalized contrast injection protocol in liver CT"],"prefix":"10.1186","volume":"8","author":[{"given":"Hugues G.","family":"Brat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit","family":"Dufour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Natalie","family":"Heracleous","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pauline","family":"Sastre","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cyril","family":"Thouly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Benoit","family":"Rizk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9340-5098","authenticated-orcid":false,"given":"Federica","family":"Zanca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,9]]},"reference":[{"key":"492_CR1","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1148\/radiol.2015142941","volume":"278","author":"K Awai","year":"2016","unstructured":"Awai K, Kanematsu M, Kim T et al (2016) The optimal body size index with which to determine iodine dose for hepatic dynamic CT: a prospective multicenter study. 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A written informed consent is submitted to every patient upon admission in Groupe 3\u2009R (a Swiss radiology services network, comprising 20 imaging centers) stating, among others, the possible use of anonymized patient data for research purposes. The patient was free to oppose this use and listed as such. Specific written informed consent was therefore waived by the CCER.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"PS is an employee of GE Healthcare. The remaining authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"112"}}