IEEE Transactions on Medical ImagingBiophysicsPeer-reviewed journal article
Nonlinear Singular Value Decomposition Beamforming for Ultrasound Imaging of Gas Vesicles
Ge ZhangiD, Mathis Vert, Mohamed Nouhoum, Esteban RiveraiD, Nabil HaidouriD, Anatole JimeneziD, Thomas DeffieuxiD, Simon BarraliD, Pascal HerseniD, Sophie PezetiD, Claire RabutiD, Mikhail G. ShapiroiD, Mickaël TanteriD
Abstract
Ultrasound imaging holds significant promise for the observation of molecular and cellular phenomena through the utilization of acoustic contrast agents and acoustic reporter genes. Optimizing imaging methodologies for enhanced detection represents an imperative advancement in this field. Most advanced techniques relying on amplitude modulation scheme such as cross amplitude modulation (xAM) and ultrafast amplitude modulation (uAM) combined with Hadamard encoded multiplane wave transmissions have shown efficacy in capturing acoustic signals of gas vesicles (GVs). Nonetheless, uAM sequence requires odd- or even-element transmissions leading to imprecise amplitude modulation emitting scheme, and the complex multiplane wave transmission scheme inherently yields overlong pulse durations. xAM sequence is limited in terms of field of view and imaging depth. To overcome these limitations, we introduce an innovative ultrafast imaging sequence called nonlinear singular value decomposition (SVD) beamforming. Our method demonstrated a contrast imaging sensitivity comparable to the current gold-standard xAM and uAM, while requiring 4.8 times less pulse transmissions. With similar number of transmit pulses, nonlinear SVD beamforming outperforms xAM and uAM in terms of an improvement in signal-to-background ratio of + 4.78 {+/-} 0.35 dB and + 8.29 {+/-} 3.52 dB respectively. Additionally, our method provides a higher flexibility in terms of the selection of acoustic pressure amplitude compared to the other methods. Furthermore, it shows a significant potential for application in the realm of ultrasound localization microscopy (ULM), where it stands poised to facilitate the more precise extraction of nonlinear signatures originating from contrast agents.
- assay: imaging
- research focus: imputation
- research focus: dimensionality reduction
- approach: deep learning
- approach: statistical model
- Citations
- 5
- FWCI
- 1.14
- Field percentile
- top 27%
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