Abstract
Background: To evaluate effectivity of a 3D-motion correction algorithm in C-Arm CTs (CACT) with limited image quality (IQ) during transarterial chemoembolization (TACE). Methods: From 1/2015–5/2021, 644 CACTs were performed in patients during TACE. Of these, 27 CACTs in 26 patients (18 m, 8f; 69.7 years ± 10.7 SD) of limited IQ were included. Post-processing of the original raw-data sets (CACTOrg) included application of a 3D-motion correction algorithm and bone segmentation (CACTMC_no_bone). Four radiologists (R1-4) compared the images by choosing their preferred dataset and recommending repeat acquisition in case of severe IQ-impairment. R1,2 performed additional grading of intrahepatic vessel visualization, presence/extent of movement artifacts, and overall IQ. Results: R1,2 demonstrated excellent interobserver agreement for overall IQ (ICC 0.79,p < 0.01) and the five-point vessel visualization scale before and after post-processing of the datasets (ICC 0.78,p < 0.01). Post-processing caused significant improvement, with overall IQ improving from 2.63 (CACTOrg) to 1.39 (CACTMC_no_bone;p < 0.01) and a decrease in the mean distance of identifiable, subcapsular vessels to the liver capsule by 4 mm (p < 0.01). This proved especially true for datasets with low parenchymal and high hepatic artery contrast. A good interobserver agreement (ICC = 0.73) was recorded concerning the presence of motion artifacts, with significantly less discernible motion after post-processing (CACTOrg:1.31 ± 1.67, CACTMC_no_bone:1.00 ± 1.34, p < 0.01). Of the 27 datasets, ≥ 23 CACTMC_no_bone were preferred, with identical datasets chosen by the readers to show benefit from the algorithm. Conclusion: Application of a 3D-motion correction algorithm significantly improved IQ in diagnostically limited CACTs during TACE, with the potential to decrease repeat acquisitions.
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 37 |
| Zeitschrift | Cancer Imaging |
| Jahrgang | 22 |
| Ausgabenummer | 1 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 12.2022 |
Fördermittel
Open Access funding enabled and organized by Projekt DEAL. The study was funded in parts by personal grants from the “Junge Akademie” and PRACTIS (Program of Hannover Medical School for Clinician Scientists).
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 3 – Gesundheit und Wohlergehen
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SDG 9 – Industrie, Innovation und Infrastruktur
Strategische Forschungsbereiche und Zentren
- Forschungsschwerpunkt: Biomedizintechnik
DFG-Fachsystematik
- 2.22-30 Radiologie
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