Deep learning assisted classification of spectral photoacoustic imaging of carotid plaques.

Spectral photoacoustic imaging (sPAI) is an emerging modality that allows real-time, non-invasive, and radiation-free assessment of tissue, benefiting from their optical contrast. sPAI is ideal for morphology assessment in arterial plaques, where plaque composition provides relevant information on plaque progression and its vulnerability. However, since sPAI is affected by spectral coloring, general spectroscopy unmixing techniques cannot provide reliable identification of such complicated sample composition. In this study, we employ a convolutional neural network (CNN) for the classification of plaque composition using sPAI. For this study, nine carotid endarterectomy plaques were imaged and were then annotated and validated using multiple histological staining. Our results show that a CNN can effectively differentiate constituent regions within plaques without requiring fluence or spectra correction, with the potential to eventually support vulnerability assessment in plaques.

Blind spectral unmixing for characterization of plaque composition based on multispectral photoacoustic imaging.

To improve the assessment of carotid plaque vulnerability, a comprehensive characterization of their composition is paramount. Multispectral photoacoustic imaging (MSPAI) can provide plaque composition based on their absorption spectra. However, although various spectral unmixing methods have been developed to characterize different tissue constituents, plaque analysis remains a challenge since its composition is highly complex and diverse. In this study, we employed an adapted piecewise convex multiple-model endmember detection method to identify carotid plaque constituents. Additionally, we explore the selection of the imaging wavelengths in linear models by conditioning the coefficient matrix and its synergy with our unmixing approach. We verified our method using plaque mimicking phantoms and performed ex-vivo MSPAI on carotid endarterectomy samples in a spectral range from 500 to 1300 nm to identify the main spectral features of plaque materials for vulnerability assessment. After imaging, the samples were processed for histological analysis to validate the photoacoustic decomposition. Results show that our approach can perform spectral unmixing and classification of highly heterogeneous biological samples without requiring an extensive fluence correction, enabling the identification of relevant components to assess plaque vulnerability.

Multiperspective Photoacoustic Imaging Using Spatially Diverse CMUTs.

Photoacoustic imaging (PAI) is a promising technique to assess different constituents in tissue. In PAI, the propagating waves are low-amplitude, isotropic, and broadband. A common approach in PAI is the use of a single linear or curved piezoelectric transducer array to perform both PA and ultrasound imaging. These systems provide freedom, agility, and versatility for performing imaging, but have limited field of view (FOV) and directivity that degrade the final image quality. Capacitive micromachined ultrasonic transducers (CMUTs) have a great potential to be used for PAI since they provide larger bandwidth and better cost efficiency. In this study, to improve the FOV, resolution, and contrast, we propose a multiperspective PAI (MP-PAI) approach using multiple CMUTs on a flexible array with shared channels. The designed array was used to perform MP-PAI in an in vitro experiment using a plaque mimicking phantom where the images were compounded both incoherently and coherently. The MP-PAI approach showed a significant improvement in overall image quality. Using only three CMUTs led to about 20% increase in generalized-contrast-to-noise ratio (gCNR), 2-dB improvement in peak signal-to-noise ratio (PSNR), and double the structural coverage in comparison to a single CMUT setup. In numerical studies, the MP-PAI was thoroughly evaluated for both the coherent and incoherent compounding methods. The assessments showed that the image quality further improved for increased number of transducers and angular coverage. For 15 transducers, the improvement for resolution and contrast could be up to three times the amount in a single-perspective image. Nonetheless, the most prominent improvement of MP-PAI was its ability to resolve the structural information of the phantoms.

Robust Waveform Design of Ultrasound Arrays for Medical Imaging.

Sound speed is an effective parameter in designing an optimal beamformer. In conventional ultrasound imaging systems, the beamformer is designed assuming a fixed value of speed, whereas the speed in a tissue is not known precisely and also may fluctuate by a great value. The errors in estimating sound speed may lead to a severe degradation in the reconstructed image, as mainlobe width and sidelobe level of the beampattern are sensitive to the speed variations. In this paper, we consider the design of a transmit beamformer, which is robust to the speed variations. The problem is formulated as a convex optimization problem versus the covariance matrix of the excitation waveforms to obtain a beampattern with predefined mainlobe width and a minimum sidelobe level for all possible variations of speed. Then, by eigen-analysis of the obtained covariance matrix, a set of nonidentical single-carrier short-pulses for the excitation waveforms were designed. Various simulations indicate that the proposed method can yield a robust beampattern whose mainlobe width and sidelobe level almost remain constant by 10% speed variations. In contrast, the beampatterns obtained by nonrobust methods suffer extensive changes.

A robust approach to apodization design in phased arrays for ultrasound imaging.

Apodization is a common way to control the sidelobe level of the beampattern. To design the apodization, it is usually assumed that the medium is homogenous with a fixed value of sound speed, despite the fact that the speed may vary by a great value in practical situations. On the other hand, the beamforming performance is highly affected by the speed value. In this paper, the beampattern sensitivity to the speed variations is firstly investigated through both mathematical representations and simulation results showing that the speed errors lead to a sever degradation of the beampattern in terms of both mainlobe width and sidelobe level. Then, we consider an optimization problem robust to the speed variations which minimizes the sidelobe level while maintaining a predefined mainlobe width. Furthermore, the optimization problem is reformulated as a convex problem using semidefinite relaxation (SDR) method to be solved more efficiently. The solutions are evaluated for some exemplary phased arrays at different values of focusing depths and mainlobe widths. The results show that the robust apodization is capable of maintaining the mainlobe properties for all possible values of the speed, while minimizing the sidelobe level. Moreover, the superiority of the robust method against non-robust method is highlighted at lower focusing depths and smaller mainlobe widths.