Σφακιανάκης Αλέξανδρος
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5 Άγιος Νικόλαος
Κρήτη 72100
00302841026182
00306932607174
alsfakia@gmail.com

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! # Ola via Alexandros G.Sfakianakis on Inoreader

Η λίστα ιστολογίων μου

Τετάρτη 19 Ιουλίου 2017

Interobserver variability of patient positioning using four different CT datasets for image registration in lung stereotactic body radiotherapy

Abstract

Purpose

To assess the impact of different reference CT datasets on manual image registration with free-breathing three-dimensional (3D) cone beam CTs (FB-CBCT) for patient positioning by several observers.

Methods

For 48 patients with lung lesions, manual image registration with FB-CBCTs was performed by four observers. A slow planning CT (PCT), average intensity projection (AIP), maximum intensity projection (MIP), and midventilation CT (MidV) were used as reference images. Couch shift differences between the four reference CT datasets for each observer as well as shift differences between the observers for the same reference CT dataset were determined. Statistical analyses were performed and correlations between the registration differences and the 3D tumor motion and the CBCT score were calculated.

Results

The mean 3D shift difference between different reference CT datasets was the smallest for AIPvsMIP (range 1.1–2.2 mm) and the largest for MidVvsPCT (2.8–3.5 mm) with differences >10 mm. The 3D shifts showed partially significant correlations to 3D tumor motion and CBCT score. The interobserver comparison for the same reference CTs resulted in the smallest ∆3D mean differences and mean ∆3D standard deviation for ∆AIP (1.5 ± 0.7 mm, 0.7 ± 0.4 mm). The maximal 3D shift difference between observers was 10.4 mm (∆MidV). Both 3D tumor motion and mean CBCT score correlated with the shift differences (Rs = 0.336–0.740).

Conclusion

The applied reference CT dataset impacts image registration and causes interobserver variabilities. The 3D tumor motion and CBCT quality affect shift differences. The smallest differences were found for AIP which might be the most appropriate CT dataset for image registration with FB-CBCT.



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