Enhancing Ultrasound Molecular Imaging: Toward Real-Time RPCA-Based Filtering to Differentiate Bound and Free Microbubbles
Hoda S. Hashemi, Dongwoon Hyun, Nathan Nguyen, Jihye Baek, Arutselvan Natarajan, Farbod Tabesh, Andrew Andrzejek, Ramasamy Paulmurugan, Jeremy J. Dahl

TL;DR
This paper introduces a GPU-accelerated RPCA method for real-time differentiation of bound and free microbubbles in ultrasound molecular imaging, improving accuracy and speed for early cancer detection.
Contribution
The study develops and validates a fast, GPU-based RPCA technique that effectively isolates bound microbubbles from free ones, enabling real-time ultrasound molecular imaging.
Findings
RPCA with 20 frames achieves a Dice score of 0.95 in simulations.
In vivo, RPCA with 20 frames achieves a Dice score of 0.82.
Method enables potential real-time differentiation of microbubbles.
Abstract
Ultrasound molecular imaging (UMI) is an advanced imaging modality that shows promise in detecting cancer at early stages. It uses microbubbles as contrast agents, which are functionalized to bind to cancer biomarkers overexpressed on endothelial cells. A major challenge in UMI is isolating bound microbubble signal, which represents the molecular imaging signal, from that of free-floating microbubbles, which is considered background noise. In this work, we propose a fast GPU-based method using robust principal component analysis (RPCA) to distinguish bound microbubbles from free-floating ones. We explore the method using simulations and measure the accuracy using the Dice coefficient and RMS error as functions of the number of frames used in RPCA reconstruction. Experiments using stationary and flowing microbubbles in tissue-mimicking phantoms were used to validate the method.…
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