Validating image-derived input functions of dynamic 18F-FDG long axial field-of-view PET/CT studies Article Swipe
Charlotte L. C. Smith
,
Gerben J.C. Zwezerijnen
,
Marijke E den Hollander
,
Henri N. Greuter
,
Nienke R. Gerards
,
Josée M. Zijlstra
,
C. Willemien Menke‐van der Houven van Oordt
,
Idris Bahce
,
Maqsood Yaqub
,
Ronald Boellaard
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnume.2025.1556848
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fnume.2025.1556848
Aim/background Dynamic PET imaging requires an input function typically obtained through blood sampling. Image-derived input functions (IDIFs) of the ascending aorta (AA), aortic arch, descending aorta (DA), or left ventricle (LV) offer non-invasive alternatives, especially with long-axial field-of-view (LAFOV) PET/CT systems enabling whole-body dynamic 1 ⁸F-FDG imaging. This study aimed to validate uncorrected IDIFs derived from the AA, DA, aortic arch, and LV by comparing them to (late) venous whole-blood in patients undergoing LAFOV PET/CT. Methods Eleven oncology patients who underwent 70-min dynamic 18 F-FDG PET/CT scans on a LAFOV PET/CT system after receiving an intravenous bolus injection of 3.0 MBq/kg were included. Seven venous blood samples were collected manually at approximately 5, 10, 15, 25, 35, 45, and 60 min post-injection (pi) and compared to IDIFs derived from the AA, aortic arch, DA, and LV. Bias between IDIFs and venous blood samples was assessed at each time point. Results IDIF accuracy relative to venous blood samples improved over time, with a median percentage bias <10% after 25 min pi. At 60 min pi, the aortic arch showed the smallest bias (median −1.1%, IQR 5.9%), followed by the AA (2.5%, IQR 7.0%), DA (5.1%, IQR 8.6%), and LV (7.4%, IQR 7.6%). Conclusion The high precision of aorta-derived IDIFs suggests that IDIFs are a reliable alternative to manual blood sampling for dynamic 18 F-FDG PET imaging on a LAFOV PET/CT system. Using IDIFs reduces variability, simplifies protocols, minimizes radiation exposure, and enhances patient safety with a non-invasive approach.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnume.2025.1556848
- https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2025.1556848/pdf
- OA Status
- gold
- Cited By
- 1
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408041049
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4408041049Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fnume.2025.1556848Digital Object Identifier
- Title
-
Validating image-derived input functions of dynamic 18F-FDG long axial field-of-view PET/CT studiesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-02-28Full publication date if available
- Authors
-
Charlotte L. C. Smith, Gerben J.C. Zwezerijnen, Marijke E den Hollander, Henri N. Greuter, Nienke R. Gerards, Josée M. Zijlstra, C. Willemien Menke‐van der Houven van Oordt, Idris Bahce, Maqsood Yaqub, Ronald BoellaardList of authors in order
- Landing page
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https://doi.org/10.3389/fnume.2025.1556848Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2025.1556848/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/nuclear-medicine/articles/10.3389/fnume.2025.1556848/pdfDirect OA link when available
- Concepts
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Medicine, Aortic arch, Nuclear medicine, Aorta, Ascending aorta, Ventricle, Blood sampling, Bolus (digestion), Positron emission tomography, Venous blood, Cardiac imaging, Radiology, Cardiology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(pi) | 122 |
| abstract_inverted_index.Bias | 136 |
| abstract_inverted_index.IDIF | 150 |
| abstract_inverted_index.This | 47 |
| abstract_inverted_index.arch | 176 |
| abstract_inverted_index.bias | 164, 180 |
| abstract_inverted_index.each | 146 |
| abstract_inverted_index.from | 55, 128 |
| abstract_inverted_index.high | 203 |
| abstract_inverted_index.left | 28 |
| abstract_inverted_index.over | 158 |
| abstract_inverted_index.that | 209 |
| abstract_inverted_index.them | 65 |
| abstract_inverted_index.time | 147 |
| abstract_inverted_index.were | 101, 107 |
| abstract_inverted_index.with | 35, 160, 243 |
| abstract_inverted_index.(AA), | 21 |
| abstract_inverted_index.(DA), | 26 |
| abstract_inverted_index.F-FDG | 84, 222 |
| abstract_inverted_index.IDIFs | 53, 126, 138, 207, 210, 231 |
| abstract_inverted_index.LAFOV | 73, 89, 227 |
| abstract_inverted_index.Seven | 103 |
| abstract_inverted_index.Using | 230 |
| abstract_inverted_index.after | 92, 166 |
| abstract_inverted_index.aimed | 49 |
| abstract_inverted_index.aorta | 20, 25 |
| abstract_inverted_index.arch, | 23, 60, 132 |
| abstract_inverted_index.blood | 11, 105, 141, 155, 217 |
| abstract_inverted_index.bolus | 96 |
| abstract_inverted_index.input | 6, 14 |
| abstract_inverted_index.offer | 31 |
| abstract_inverted_index.scans | 86 |
| abstract_inverted_index.study | 48 |
| abstract_inverted_index.time, | 159 |
| abstract_inverted_index.(2.5%, | 189 |
| abstract_inverted_index.(5.1%, | 193 |
| abstract_inverted_index.(7.4%, | 198 |
| abstract_inverted_index.(late) | 67 |
| abstract_inverted_index.5.9%), | 184 |
| abstract_inverted_index.7.0%), | 191 |
| abstract_inverted_index.7.6%). | 200 |
| abstract_inverted_index.70-min | 81 |
| abstract_inverted_index.8.6%), | 195 |
| abstract_inverted_index.Eleven | 76 |
| abstract_inverted_index.MBq/kg | 100 |
| abstract_inverted_index.PET/CT | 39, 85, 90, 228 |
| abstract_inverted_index.aortic | 22, 59, 131, 175 |
| abstract_inverted_index.manual | 216 |
| abstract_inverted_index.median | 162 |
| abstract_inverted_index.point. | 148 |
| abstract_inverted_index.safety | 242 |
| abstract_inverted_index.showed | 177 |
| abstract_inverted_index.system | 91 |
| abstract_inverted_index.venous | 68, 104, 140, 154 |
| abstract_inverted_index.(IDIFs) | 16 |
| abstract_inverted_index.(LAFOV) | 38 |
| abstract_inverted_index.(median | 181 |
| abstract_inverted_index.Dynamic | 1 |
| abstract_inverted_index.Methods | 75 |
| abstract_inverted_index.PET/CT. | 74 |
| abstract_inverted_index.Results | 149 |
| abstract_inverted_index.between | 137 |
| abstract_inverted_index.derived | 54, 127 |
| abstract_inverted_index.dynamic | 43, 82, 220 |
| abstract_inverted_index.imaging | 3, 224 |
| abstract_inverted_index.patient | 241 |
| abstract_inverted_index.reduces | 232 |
| abstract_inverted_index.samples | 106, 142, 156 |
| abstract_inverted_index.system. | 229 |
| abstract_inverted_index.systems | 40 |
| abstract_inverted_index.through | 10 |
| abstract_inverted_index.accuracy | 151 |
| abstract_inverted_index.assessed | 144 |
| abstract_inverted_index.compared | 124 |
| abstract_inverted_index.enabling | 41 |
| abstract_inverted_index.enhances | 240 |
| abstract_inverted_index.followed | 185 |
| abstract_inverted_index.function | 7 |
| abstract_inverted_index.imaging. | 46 |
| abstract_inverted_index.improved | 157 |
| abstract_inverted_index.manually | 109 |
| abstract_inverted_index.obtained | 9 |
| abstract_inverted_index.oncology | 77 |
| abstract_inverted_index.patients | 71, 78 |
| abstract_inverted_index.relative | 152 |
| abstract_inverted_index.reliable | 213 |
| abstract_inverted_index.requires | 4 |
| abstract_inverted_index.sampling | 218 |
| abstract_inverted_index.smallest | 179 |
| abstract_inverted_index.suggests | 208 |
| abstract_inverted_index.validate | 51 |
| abstract_inverted_index.⁸F-FDG | 45 |
| abstract_inverted_index.−1.1%, | 182 |
| abstract_inverted_index.approach. | 246 |
| abstract_inverted_index.ascending | 19 |
| abstract_inverted_index.collected | 108 |
| abstract_inverted_index.comparing | 64 |
| abstract_inverted_index.exposure, | 238 |
| abstract_inverted_index.functions | 15 |
| abstract_inverted_index.included. | 102 |
| abstract_inverted_index.injection | 97 |
| abstract_inverted_index.minimizes | 236 |
| abstract_inverted_index.precision | 204 |
| abstract_inverted_index.radiation | 237 |
| abstract_inverted_index.receiving | 93 |
| abstract_inverted_index.sampling. | 12 |
| abstract_inverted_index.typically | 8 |
| abstract_inverted_index.underwent | 80 |
| abstract_inverted_index.ventricle | 29 |
| abstract_inverted_index.Conclusion | 201 |
| abstract_inverted_index.descending | 24 |
| abstract_inverted_index.especially | 34 |
| abstract_inverted_index.long-axial | 36 |
| abstract_inverted_index.percentage | 163 |
| abstract_inverted_index.protocols, | 235 |
| abstract_inverted_index.simplifies | 234 |
| abstract_inverted_index.undergoing | 72 |
| abstract_inverted_index.whole-body | 42 |
| abstract_inverted_index.&lt;10% | 165 |
| abstract_inverted_index.alternative | 214 |
| abstract_inverted_index.intravenous | 95 |
| abstract_inverted_index.uncorrected | 52 |
| abstract_inverted_index.whole-blood | 69 |
| abstract_inverted_index.non-invasive | 32, 245 |
| abstract_inverted_index.variability, | 233 |
| abstract_inverted_index.Image-derived | 13 |
| abstract_inverted_index.alternatives, | 33 |
| abstract_inverted_index.aorta-derived | 206 |
| abstract_inverted_index.approximately | 111 |
| abstract_inverted_index.field-of-view | 37 |
| abstract_inverted_index.Aim/background | 0 |
| abstract_inverted_index.post-injection | 121 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5103126038 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I4210151833, https://openalex.org/I4387152927, https://openalex.org/I865915315 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.83205881 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |