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Evaluation of 2D and 3D ultrasound tracking algorithms and impact on ultrasound‐guided liver radiotherapy margins
Author(s) -
De Luca Valeria,
Banerjee Jyotirmoy,
Hallack Andre,
Kondo Satoshi,
Makhinya Maxim,
Nouri Daniel,
Royer Lucas,
Cifor Amalia,
Dardenne Guillaume,
Goksel Orcun,
Gooding Mark J.,
Klink Camiel,
Krupa Alexandre,
Le Bras Anthony,
Marchal Maud,
Moelker Adriaan,
Niessen Wiro J.,
Papiez Bartlomiej W.,
Rothberg Alex,
Schnabel Julia,
van Walsum Theo,
Harris Emma,
Lediju Bell Muyinatu A.,
Tanner Christine
Publication year - 2018
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13152
Subject(s) - ultrasound , 3d ultrasound , computer science , radiation therapy , match moving , respiratory compensation , motion compensation , medicine , computer vision , radiology , motion (physics) , anaerobic exercise , physiology
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.