{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T03:42:43Z","timestamp":1790221363987,"version":"4.1.0"},"reference-count":36,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,3]],"date-time":"2025-09-03T00:00:00Z","timestamp":1756857600000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Fat volume fraction (FVF) is an important biomarker for non\u2010alcoholic fatty liver disease. However, current CT\u2010based FVF quantification methods lack sufficient accuracy, particularly at lower FVF values.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We aimed to analyze the relationship between FVF and Hounsfield units (HU) in unenhanced fatty lesions and identify optimal settings to minimize FVF quantification errors by comparing virtual monochromatic imaging (VMI) from dual\u2010energy CT (DECT) with single\u2010energy CT (SECT) across different patient sizes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Six fatty lesions (5%\u201340% FVF) were embedded in an anthropomorphic liver within a medium\u2010sized abdomen\u2010pelvis phantom (25\u00a0cm\u00d732.5\u00a0cm). DECT acquisitions were conducted at CTDI\n                      <jats:sub>vol<\/jats:sub>\n                      of 14.5\u00a0mGy using both fast kV\u2010switching (FKVS) and dual\u2010source CT (DSCT), producing VMI images from 40 to 140\u00a0keV. HU values were measured across all VMI energies for each lesion in three repeated acquisitions. The measured HU values were correlated with the known FVF. For comparison, repeated single\u2010energy images were also acquired at 120\u00a0kV with the same dose, and a similar analysis was performed. To study the impact of the patient size, a large phantom (31\u00a0cm\u00d739\u00a0cm) consisted of an additional soft\u2010tissue equivalent layer to the medium\u2010size phantom was scanned on the FKVS CT with noise matched CTDI\n                      <jats:sub>vol<\/jats:sub>\n                      \u00a0=\u00a021 mGy using DECT and SECT.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      It was found that the validity of the linear relation for FVF\u2010HU is x\u2010ray beam energy and VMI energy\u2010dependent. For the medium\u2010sized phantom, the root\u2010mean\u2010square (RMS) FVF estimation errors from the linearity assumption were slightly higher than 9% with the SECT at 120\u00a0kV and CTDI\n                      <jats:sub>vol<\/jats:sub>\n                      \u00a0=\u00a014.5\u00a0mGy on both units, and the corresponding maximum individual FVF errors were 16.5%\u201323% at FVF\u00a0\u2264\u00a010%. With the FKVS CT, four VMI settings resulted in lower RMS errors than SECT with the smallest RMS. error (\u223c5%) at 90 and 100\u00a0keV, where the maximum individual FVF errors were approximately 10% occurred at FVF\u00a0\u2264\u00a010%. For the DSCT with spectra 80\/Sn 150\u00a0kV, five VMI settings resulted in smaller RMS errors than 9%, with the lowest RMS error (\u223c2.5%) at 120 and 130\u00a0keV, where the maximum individual FVF errors \u22644.4% occurred at 30% FVF, respectively. For the large phantom, however, the linear model at single energy of 140\u00a0kV resulted in the lowest RMS error of 9.2% with the maximum individual FVF error of 20% at 5% FVF, while the smallest RMS error from VMI was 15.3% with the maximum individual FVF error of 27% at 10% FVF.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The validity of linear assumption about the FVF\u2010HU relationship was found to depend on x\u2010ray beam energy, VMI energy, and patient size, which impacts the FVF assessment accuracy. The settings resulting in best accuracy were identified. For the medium\u2010sized phantom, the use of VMI with wider dual energy spectral separation showed the RMS errors \u223c2.5%. For the large phantom, the use of single energy at 140\u00a0kV on the FKVS CT showed the best estimate, albeit with a RMS error of 9.2%.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.18105","type":"journal-article","created":{"date-parts":[[2025,9,4]],"date-time":"2025-09-04T06:02:03Z","timestamp":1756965723000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Liver fat volume fraction assessment in medium and large anthropomorphic phantoms\u2013dual energy virtual monochromatic imaging versus single\u2010energy CT"],"prefix":"10.1002","volume":"52","author":[{"given":"Yifang","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Imaging Cedars\u2010Sinai Medical Center  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Imaging Cedars\u2010Sinai Medical Center  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,3]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/hep.28431"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3748\/wjg.v23.i47.8263"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/hep.29466"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1610570"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.gastro.2005.03.084"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.210736"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.212808"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-13327-5_79"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/rg.266065004"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.14366\/usg.21167"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.23741"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/hep.29797"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2016.02.031"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2010.08.015"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.241171"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1177\/1756283X16656735"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.594199"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jhep.2010.01.008"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.16.16565"},{"issue":"1","key":"e_1_2_9_21_1","first-page":"92","article-title":"Quantitative diagnosis of fatty liver with dual\u2010energy CT. 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