Authors
Trevor J Chan, Aparna Nair-Kanneganti, Brian Anthony, Alison Pouch
Published in
IEEE transactions on bio-medical engineering. Volume PP. Sep 04, 2025. Epub Sep 04, 2025.
Abstract
Diagnostic ultrasound has long filled a crucial niche in medical imaging thanks to its portability, affordability, and favorable safety profile. Now, multi-view hardware and deep-learning-based image reconstruction algorithms promise to extend this niche to increasingly sophisticated applications, such as volume rendering and long-term organ monitoring. However, progress on these fronts is impeded by the complexities of ultrasound electronics and by the scarcity of high-fidelity radiofrequency data. Evidently, there is a critical need for tools that enable rapid ultrasound prototyping and generation of synthetic data. We meet this need with MUSiK, the first open-source ultrasound simulation library expressly designed for multi-view acoustic simulations of realistic anatomy. This library covers the full gamut of image acquisition: building anatomical digital phantoms, defining and positioning diverse transducer types, running simulations, and reconstructing images. In this paper, we demonstrate several use cases for MUSiK. We simulate in vitro multi-view experiments and compare the resolution and contrast of the resulting images. We then perform multiple conventional and experimental in vivo imaging tasks, such as 2D scans of the kidney, 2D and 3D echocardiography, 2.5D tomography of large regions, and 3D tomography for lesion detection in soft tissue. Finally, we introduce MUSiK's Bayesian reconstruction framework for multi-view ultrasound and validate an original SNR-enhancing reconstruction algorithm. We anticipate that these unique features will seed new hypotheses and accelerate the overall pace of ultrasound technological development. The MUSiK library is publicly available at github.com/norway99/MUSiK.
PMID:
40907042
Bibliographic data and abstract were imported from PubMed on 05 Sep 2025.
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