bioRxivBioengineeringPreprint
Harmonic Amplitude-Modulated Singular Value Decomposition for Ultrafast Ultrasound Imaging of Gas Vesicles
Ge ZhangiD, Henri LeroyiD, Nabil HaidouriD, Nicolas ZuckeriD, Esteban Rivera, Mohamed Nouhoum, Anatole JimeneziD, Thomas DeffieuxiD, Dina MaloundaiD, Rohit NayakiD, Sophie PezetiD, Mikhail G. ShapiroiD, Mathieu PernotiD, Mickaël TanteriD
Abstract
Ultrafast nonlinear ultrasound imaging of gas vesicles (GV) contrast agents promises high-sensitivity biomolecular visualization with applications such as targeted molecular imaging of tumor markers or real-time tracking of gene expression. However, separating GV-specific signal from tissue remains challenging and requires the implementation of complex transmit schemes. In this work we introduce harmonic amplitude-modulated singular value decomposition (HAM-SVD), which synergizes pulse inversion (PI) with amplitude-modulated singular value decomposition (AM-SVD) to isolate GV-specific second-harmonic signals. In HAM-SVD, single-cycle plane waves at 9.6 MHz and five tilted angles (at a pulse repetition frequency of 2500 Hz) are transmitted under four duty cycles with alternating polarity. Beamformed IQ data are reshaped along a "space x pressure" matrix and decomposed via SVD; tissue background is cancelled by discarding the first and lowest singular modes, yielding an image comprised solely of pressure-dependent second harmonic signals. HAM-SVD sequence enables wide-field, ultrafast imaging without complex transmit sequences. Validation via simulations, in vitro phantoms, and in vivo rat lower limb experiments demonstrates HAM-SVDs outperformance compared to PI and AM-SVD. HAM-SVD is shown to achieve a 19.16 {+/-} 1.63 dB signal-to-background ratio (SBR) in vivo, surpassing PI (14.19 {+/-} 1.41 dB) and AM-SVD (15.79 {+/-} 1.38 dB). HAM-SVD overcomes limitations of conventional nonlinear techniques (e.g., depth restrictions, tissue clutter) by combining PIs harmonic sensitivity with AM-SVDs adaptive clutter filtering of tissue signals. This approach enhances molecular imaging specificity for GVs and holds potential for ultrasound localization microscopy of slow-flowing agents.
- organism: rat
- assay: imaging
- research focus: cell type annotation
- approach: deep learning
- approach: statistical model
- Citations
- 1
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