Brice Rauby, Paul Xing, Maxime Gasse et Jean Provost
Article de revue (2024)
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Abstract
Ultrasound Localization Microscopy (ULM) is a novel super-resolution imaging technique that can image the vasculature in vivo at depth with resolution far beyond the conventional limit of diffraction. By relying on the localization and tracking of clinically approved microbubbles injected in the blood stream, ULM can provide not only anatomical visualization but also hemodynamic quantification of the microvasculature of different tissues. Various deep-learning approaches have been proposed to address challenges in ULM including denoising, improving microbubble localization, estimating blood flow velocity or performing aberration correction. Proposed deep learning methods often outperform their conventional counterparts by improving image quality and reducing processing time. In addition, their robustness to high concentrations of microbubbles can lead to reduced acquisition times in ULM, addressing a major hindrance to ULM clinical application. Herein, we propose a comprehensive review of the diversity of deep learning applications in ULM focusing on approaches assuming a sparse microbubbles distribution. We first provide an overview of how existing studies vary in the constitution of their datasets or in the tasks targeted by deep learning model. We also take a deeper look into the numerous approaches that have been proposed to improve the localization of microbubbles since they differ highly in their formulation of the optimization problem, their evaluation, or their network architectures. We finally discuss the current limitations and challenges of these methods, as well as the promises and potential of deep learning for ULM in the future.
Mots clés
deep learning; neural network; super-resolution; ultrasound localization microscopy
Sujet(s): | 3100 Physique > 3100 Physique |
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Département: | Département de génie physique |
Organismes subventionnaires: | NSERC / CRSNG, Institut TransMedTech, Canada Foundation for Innovation, Institut de Valorisation des Données, New Frontiers in Research Fund, Canadian Institutes of Health Research, Fonds de recherche du Québec - Nature et technologies, Réseau de Bio-Imagerie du Québec |
Numéro de subvention: | RGPIN-2019-04982, 38095, NFRFE-2018-01312, 452530 |
URL de PolyPublie: | https://publications.polymtl.ca/59289/ |
Titre de la revue: | IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
Maison d'édition: | IEEE |
DOI: | 10.1109/tuffc.2024.3462299 |
URL officielle: | https://doi.org/10.1109/tuffc.2024.3462299 |
Date du dépôt: | 25 sept. 2024 09:39 |
Dernière modification: | 26 sept. 2024 20:50 |
Citer en APA 7: | Rauby, B., Xing, P., Gasse, M., & Provost, J. (2024). Deep learning in ultrasound localization microscopy : applications and perspectives. IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 3462299 (23 pages). https://doi.org/10.1109/tuffc.2024.3462299 |
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