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Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound-guided liver radiotherapy margins

Research output: Contribution to journalArticlepeer-review

Valeria De Luca, Jyotirmoy Banerjee, Andre Hallack, Satoshi Kondo, Maxim Makhinya, Daniel Nouri, Lucas Royer, Amalia Cifor, Guillaume Dardenne, Orcun Goksel, Mark J. Gooding, Camiel Klink, Alexandre Krupa, Anthony Le Bras, Maud Marchal, Adriaan Moelker, Wiro J. Niessen, Bartlomiej W. Papiez, Alex Rothberg, Julia Schnabel & 4 more Theo van Walsum, Emma Harris, Muyinatu A. Lediju Bell, Christine Tanner

Original languageEnglish
Pages (from-to)4986-5003
Number of pages18
JournalMedical Physics
Volume45
Issue number11
Early online date30 Aug 2018
DOIs
Accepted/In press27 Jul 2018
E-pub ahead of print30 Aug 2018
Published1 Nov 2018

King's Authors

Abstract

Purpose

Compensation for respiratory motion is important during abdominal cancer treatments. In this work we report the results of the 2015 MICCAI Challenge on Liver Ultrasound Tracking and extend the 2D results to relate them to clinical relevance in form of reducing treatment margins and hence sparing healthy tissues, while maintaining full duty cycle.

Methods

We describe methodologies for estimating and temporally predicting respiratory liver motion from continuous ultrasound imaging, used during ultrasound‐guided radiation therapy. Furthermore, we investigated the trade‐off between tracking accuracy and runtime in combination with temporal prediction strategies and their impact on treatment margins.

Results

Based on 2D ultrasound sequences from 39 volunteers, a mean tracking accuracy of 0.9 mm was achieved when combining the results from the 4 challenge submissions (1.2 to 3.3 mm). The two submissions for the 3D sequences from 14 volunteers provided mean accuracies of 1.7 and 1.8 mm. In combination with temporal prediction, using the faster (41 vs 228 ms) but less accurate (1.4 vs 0.9 mm) tracking method resulted in substantially reduced treatment margins (70% vs 39%) in contrast to mid‐ventilation margins, as it avoided non‐linear temporal prediction by keeping the treatment system latency low (150 vs 400 ms). Acceleration of the best tracking method would improve the margin reduction to 75%.

Conclusions

Liver motion estimation and prediction during free‐breathing from 2D ultrasound images can substantially reduce the in‐plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non‐linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath‐hold and gated approaches, and increase treatment efficiency and safety.

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