ArticleEuropean radiology2014
Ultrafast cone-beam computed tomography imaging and postprocessing data during image-guided therapeutic practice.
Article in European radiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- New concepts in embolotherapy of HCC.Medical oncology (Northwood, London, England) · 2017Review
- Sectional Localization of a Small Hepatocellular Carcinoma in the Right Hepatic Lobe by Computed Tomography: Comparison between the Conventional and Portal Vein Tracing Methods.European radiology · 2016Article
- Interventional Oncology in Hepatocellular Carcinoma: Progress Through Innovation.Cancer journal (Sudbury, Mass.)Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveOur objective was to evaluate ultrafast cone-beam computed tomography (u-CBCT) image data using cross-sectional images, perfusion blood volume (PBV), and image fusion during tumour detection at the course of transarterial chemoembolization.
methodsOne hundred and fifty patients (63 ± 20 years; 33-82) were examined from February to October 2013 with u-CBCT. Tumour delineation and conspicuity were determined using u-CBCT cross-sectional PBV and u-CBCT-magnetic resonance imaging (MRI) fused data sets for hyperenhanced (HYET), heterogeneously enhanced (HEET), and unenhanced (UET) tumour categories. Catheter localisation and tumour feeding vessels were assessed using all data sets. Quantitative and qualitative analyses were performed using appropriate statistical tests.
resultQualitative and quantitative tumour delineation showed significant difference (all P < 0.05) among tumour categories. Mean tumour-liver-contrast was higher in HYET than in HEET, and UET; moreover, differences between tumour categories were statistically significant (all P < 0.0001). Fused data showed higher value with statistical significance (P < 0.05) compared with other data sets during catheter localisation and feeding-vessel identification.
conclusionTumour delineation was clearly possible using u-CBCT cross sections with contrast material. PBV uses color-coded images to increase detection and produces good tumour differentiation. Image fusion helps accurately identify tumour and feeding vessels and locate contrast material injection sites and catheter tips without additional data acquisition. KEY POINTS: • Ultrafast CBCT cross-sectional data provide good tumour delineation with contrast material • Postprocessed PBV using u-CBCT increased detectability and tumour differentiation • u-CBCT cross-sectional PBV and u-CBCT-MRI data helps image guidance during chemoembolization • u-CBCT-MRI can identify tumours and feeding vessels and locate catheter tip accurately.
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