Deep learning for dose assessment in radiotherapy by the super-localization of vaporized nanodroplets in high frame rate ultrasound imaging

Brent Van Der Heyden, Sophie V. Heymans, Bram Carlier, Gonzalo Collado-Lara, Edmond Sterpin, Jan Dhooge*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Objective. External beam radiotherapy is aimed to precisely deliver a high radiation dose to malignancies, while optimally sparing surrounding healthy tissues. With the advent of increasingly complex treatment plans, the delivery should preferably be verified by quality assurance methods. Recently, online ultrasound imaging of vaporized radiosensitive nanodroplets was proposed as a promising tool for in vivo dosimetry in radiotherapy. Previously, the detection of sparse vaporization events was achieved by applying differential ultrasound (US) imaging followed by intensity thresholding using subjective parameter tuning, which is sensitive to image artifacts. Approach. A generalized deep learning solution (i.e. BubbleNet) is proposed to localize vaporized nanodroplets on differential US frames, while overcoming the aforementioned limitation. A 5-fold cross-validation was performed on a diversely composed 5747-frame training/validation dataset by manual segmentation. BubbleNet was then applied on a test dataset of 1536 differential US frames to evaluate dosimetric features. The intra-observer variability was determined by scoring the Dice similarity coefficient (DSC) on 150 frames segmented twice. Additionally, the BubbleNet generalization capability was tested on an external test dataset of 432 frames acquired by a phased array transducer at a much lower ultrasound frequency and reconstructed with unconventional pixel dimensions with respect to the training dataset. Main results. The median DSC in the 5-fold cross validation was equal to 1/40.88, which was in line with the intra-observer variability (=0.86). Next, BubbleNet was employed to detect vaporizations in differential US frames obtained during the irradiation of phantoms with a 154 MeV proton beam or a 6 MV photon beam. BubbleNet improved the bubble-count statistics by 1/430% compared to the earlier established intensity-weighted thresholding. The proton range was verified with a -0.8 mm accuracy. Significance. BubbleNet is a flexible tool to localize individual vaporized nanodroplets on experimentally acquired US images, which improves the sensitivity compared to former thresholding-weighted methods.

Original languageEnglish
Article number115015
JournalPhysics in Medicine and Biology
Issue number11
Publication statusPublished - 27 May 2022

Bibliographical note

Funding Information:
This work has been supported by the European Union’s Horizon 2020 research and innovation program under grant agreement n°766456 (‘AMPHORA’). Co-author BC received a PhD fellowship fundamental research from the Research Foundation Flanders (n°11A9520N). Dr Laurence Delombaerde and Dr Marta Rovituso are acknowledged for their help during the experimental work.

Publisher Copyright:
© 2022 Institute of Physics and Engineering in Medicine.


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