A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET.
Purpose: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor's glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 48; no. 4; pp. 995 - 1005 |
|---|---|
| Autores principales: | , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Apr2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149789242&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149789242 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Apr2021 vid: 48 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 149789242 146239254 10.1007/s00259-020-04991-9 149789242 ppf: 995 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A convolutional neural network for fully automated blood SUV determination to facilitate SUR computation in oncological FDG-PET. aug: au: Nikulin, Pavel Hofheinz, Frank Maus, Jens Li, Yimin Bütof, Rebecca Lange, Catharina Furth, Christian Zschaeck, Sebastian Kreissl, Michael C. Kotzerke, Jörg van den Hoff, Jörg affil: Helmholtz-Zentrum Dresden-Rossendorf, PET Center, Institute of Radiopharmaceutical Cancer Research, Bautzner Landstrasse 400, 01328, Dresden, Germany sug: ab: Purpose: The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor's glucose consumption. The standard uptake ratio (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload, which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT. Methods: Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts or potential spillover from adjacent FDG-avid regions. Performance of the network was assessed using the fractional deviations of automatically and manually derived BSUVs in the test data. Results: The trained U-Net yields BSUVs in close agreement with those obtained from manual delineation. Comparison of manually and automatically derived BSUVs shows excellent concordance: the mean relative BSUV difference was (mean ± SD) = (– 0.5 ± 2.2)% with a 95% confidence interval of [− 5.1,3.8]% and a total range of [− 10.0, 12.0]%. For four test cases, the derived ROIs were unusable (< 1 ml). Conclusion: CNNs are capable of performing robust automatic image-based BSUV determination. Integrating automatic BSUV derivation into PET data processing workflows will significantly facilitate SUR computation without increasing the workload in the clinical setting. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|