Integrating photoacoustic tomography into a multimodal automated breast ultrasound scanner

Singular value decomposition with weighting matrix applied for optical-resolution photoacoustic microscopes

The prestige target selectivity and imaging depth of optical-resolution photoacoustic microscope (OR-PAM) have gained attentions to enable advanced intra-cellular visualizations. However, the broad-band nature of photoacoustic signals is prone to noise and artifacts caused by the inefficient light-to-pressure translation, resulting in poor image quality. The present study foresees application of singular value decomposition (SVD) to effectively extract the photoacoustic signals from these noise and artifacts. Although spatiotemporal SVD succeeded in ultrasound flow signal extraction, the conventional multi frame model is not suitable for data acquired with scanning OR-PAM due to the burden of accessing multiple frames. To utilize SVD on the OR-PAM, this study began with exploring SVD applied on multiple A-lines of photoacoustic signal instead of frames. Upon explorations, an obstacle of uncertain presence of unwanted singular vectors was observed. To tackle this, a data-driven weighting matrix was designed to extract relevant singular vectors based on the analyses of temporal-spatial singular vectors. Evaluation on the extraction capability by the SVD with the weighting matrix showed a superior signal quality with efficient computation against past studies. In summary, this study contributes to the field by providing exploration of SVD applied on A-line signals as well as its practical utilization to distinguish and recover photoacoustic signals from noise and artifact components.

Clinical photoacoustic/ultrasound dual-modal imaging: Current status and future trends

Photoacoustic tomography (PAT) is an emerging biomedical imaging modality that combines optical and ultrasonic imaging, providing overlapping fields of view. This hybrid approach allows for a natural integration of PAT and ultrasound (US) imaging in a single platform. Due to the similarities in signal acquisition and processing, the combination of PAT and US imaging creates a new hybrid imaging for novel clinical applications. Over the recent years, particular attention is paid to the development of PAT/US dual-modal systems highlighting mutual benefits in clinical cases, with an aim of substantially improving the specificity and sensitivity for diagnosis of diseases. The demonstrated feasibility and accuracy in these efforts open an avenue of translating PAT/US imaging to practical clinical applications. In this review, the current PAT/US dual-modal imaging systems are discussed in detail, and their promising clinical applications are presented and compared systematically. Finally, this review describes the potential impacts of these combined systems in the coming future.

Denoising of pre-beamformed photoacoustic data using generative adversarial networks

We have trained generative adversarial networks (GANs) to mimic both the effect of temporal averaging and of singular value decomposition (SVD) denoising. This effectively removes noise and acquisition artifacts and improves signal-to-noise ratio (SNR) in both the radio-frequency (RF) data and in the corresponding photoacoustic reconstructions. The method allows a single frame acquisition instead of averaging multiple frames, reducing scan time and total laser dose significantly. We have tested this method on experimental data, and quantified the improvement over using either SVD denoising or frame averaging individually for both the RF data and the reconstructed images. We achieve a mean squared error (MSE) of 0.05%, structural similarity index measure (SSIM) of 0.78, and a feature similarity index measure (FSIM) of 0.85 compared to our ground-truth RF results. In the subsequent reconstructions using the denoised data we achieve a MSE of 0.05%, SSIM of 0.80, and a FSIM of 0.80 compared to our ground-truth reconstructions.