Improving 3D ultrasound prostate localisation in radiotherapy through increased automation of interfraction matching

Alexander Grimwood, Hassan Rivaz, Hang Zhou, Helen A McNair, Klaudiusz Jakubowski, Jeffrey C Bamber, Alison C Tree, Emma J Harris, Alexander Grimwood, Hassan Rivaz, Hang Zhou, Helen A McNair, Klaudiusz Jakubowski, Jeffrey C Bamber, Alison C Tree, Emma J Harris

Abstract

Background and purpose: Daily image guidance is standard care for prostate radiotherapy. Innovations which improve the accuracy and efficiency of ultrasound guidance are needed, particularly with respect to reducing interobserver variation. This study explores automation tools for this purpose, demonstrated on the Elekta Clarity Autoscan®. The study was conducted as part of the Clarity-Pro trial (NCT02388308).

Materials and methods: Ultrasound scan volumes were collected from 32 patients. Prostate matches were performed using two proposed workflows and the results compared with Clarity's proprietary software. Gold standard matches derived from manually localised landmarks provided a reference. The two workflows incorporated a custom 3D image registration algorithm, which was benchmarked against a third-party application (Elastix).

Results: Significant reductions in match errors were reported from both workflows compared to standard protocol. Median (IQR) absolute errors in the left-right, anteroposterior and craniocaudal axes were lowest for the Manually Initiated workflow: 0.7(1.0) mm, 0.7(0.9) mm, 0.6(0.9) mm compared to 1.0(1.7) mm, 0.9(1.4) mm, 0.9(1.2) mm for Clarity. Median interobserver variation was ≪0.01 mm in all axes for both workflows compared to 2.2 mm, 1.7 mm, 1.5 mm for Clarity in left-right, anteroposterior and craniocaudal axes. Mean matching times was also reduced to 43 s from 152 s for Clarity. Inexperienced users of the proposed workflows attained better match precision than experienced users on Clarity.

Conclusion: Automated image registration with effective input and verification steps should increase the efficacy of interfraction ultrasound guidance compared to the current commercially available tools.

Keywords: 3D imaging; Automation; Image-guided radiotherapy; Prostate cancer; Radiotherapy setup error; Ultrasonography.

Conflict of interest statement

Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: A.T. reports support from Elekta as a clinical research fellow working on other projects (not related to this project) and personally has received honoraria and travel grants from Elekta to cover meeting attendance. J.B. reports grants from Cancer Research UK, from the Engineering and Physical Sciences Research Council, from the Biotechnology and Biological Sciences Research Council, and from the National Institute for Health Research, outside the submitted work, and previously acted as a consultant for Elekta, although not during the period of work for this study.

Copyright © 2020 The Authors. Published by Elsevier B.V. All rights reserved.

Figures

Fig. 1
Fig. 1
Flowcharts for the two semi-automated matching workflows: (a) Full workflow and (b) Manually Initiated workflow.
Fig. 2
Fig. 2
Match error (E) violin distributions from all three observers across Clarity, Full and Manually Initiated workflows with Manual Landmark match errors for reference. Significance symbols are shown for paired F-tests between Clarity and algorithm workflows.
Fig. 3
Fig. 3
Histograms of interobserver variation (ΔD) in match results for Clarity, Full and Manually Initiated workflows, with Manual Landmark matches for reference.

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Source: PubMed

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