Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography

Optimization on artifacts in photoacoustic images based on spectrum analyses and signal extraction

Photoacoustic (PA) imaging is a promising technology for functional imaging of biological tissues, offering optical contrast and acoustic penetration depth. However, the presence of signal aliasing from multiple PA sources within the same imaging object can introduce artifacts and significantly impact the quality of the PA tomographic images. In this study, an optimized method is proposed to suppress these artifacts and enhance image quality effectively. By leveraging signal time-frequency spectrum, signals from each PA source can be extracted. Subsequently, the images are reconstructed using these extracted signals and fused together to obtain an optimized image. To verify this proposed method, PA imaging experiments were conducted on two phantoms and two in vitro samples and the distribution relative error and root mean square error of the images obtained through conventional and optimized methods were calculated. The results demonstrate that the proposed method successfully suppresses the artifacts and substantially improves the image quality.

Recent development of photoacoustic imaging in dentistry: A review on studies over the last decade

Background

This work performs a literature review of photoacoustic imaging (PAI) in dentistry and discusses the development of PAI in relation to oral health.

Methods

A search method was used to locate papers published between 2011 and 2023 in Google Scholar and PubMed databases, and 25 studies were selected. Reports on PAI in dentistry were included. Articles not written in English or whose full text could not be accessed were excluded. The remaining publications were checked and evaluated to determine whether they contain supportive materials for PAI in dentistry.

Results

The majority of articles about PAI in dentistry are associated with caries studies. Photoacoustic microscopy is the most commonly utilized PAI system. PAI studies generally focus on ex-vivo investigations using extracted human teeth. The acoustic signal obtained from carious teeth is greater than that obtained from normal teeth. In addition to imaging oral soft tissues from animal models and the periodontal pocket depth in human volunteers, PAI is applied to evaluate dental implants and oral biofilms.

Conclusion

There have been numerous investigation on PAI in dentistry, but it is not yet applicable in dental practice. In the future, PAI studies are expected to contribute to the invention of an alternative non-ionizing imaging technology that is comfortable for patients, user friendly, and capable of providing reliable information at a reasonable cost.

Plane wave ultrasound imaging using compressive sensing and minimum variance beamforming

Coherent plane-wave compounding (CPWC) is a widely used technique in medical ultrasound imaging due to its high frame rate property. It is well-known that increasing the plane waves leads to improving the image quality. However, the image quality still needs to be further improved in CPWC. In this regard, a variety of methods have been proposed. In this paper, a new compressive sensing (CS) based approach is introduced with the combination of the adaptive minimum variance (MV) algorithm to further improve the image quality in terms of resolution and contrast. In the proposed method, which is called the CS-based MV technique, the CS method is used in the receive direction to produce the beamformed data for each plane wave. Then, the MV algorithm is performed in the plane wave transmit angle direction to coherently compound the images and improve the resolution. Moreover, to deal with the high computational complexity and also, the needing for high memory space during the CS method implementation, an approximation is considered which results in considerably reduced computational burden and memory space. The results obtained from the simulated point targets show that the proposed method leads to resolution improvement for about 71%, 5.5%, and 37% respectively, compared to DAS, DAS+MV, and CS+DAS beamformers. Also, the quantitative results obtained from the experimental contrast phantom in plane wave imaging challenge in medical ultrasound (PICMUS) data show a 3.02 dB, 2.57 dB, and 2.24 dB improvement of the contrast ratio metric using the proposed method compared to DAS, DAS+MV, and double-MV methods, respectively, indicating the good performance of the proposed method in image quality improvement.

Subaperture Processing-Based Adaptive Beamforming for Photoacoustic Imaging

Delay-and-sum (DAS) beamformers, when applied to photoacoustic (PA) image reconstruction, produce strong sidelobes due to the absence of transmit focusing. Consequently, DAS PA images are often severely degraded by strong off-axis clutter. For preclinical in vivo cardiac PA imaging, the presence of these noise artifacts hampers the detectability and interpretation of PA signals from the myocardial wall, crucial for studying blood-dominated cardiac pathological information and to complement functional information derived from ultrasound imaging. In this article, we present PA subaperture processing (PSAP), an adaptive beamforming method, to mitigate these image degrading effects. In PSAP, a pair of DAS reconstructed images is formed by splitting the received channel data into two complementary nonoverlapping subapertures. Then, a weighting matrix is derived by analyzing the correlation between subaperture beamformed images and multiplied with the full-aperture DAS PA image to reduce sidelobes and incoherent clutter. We validated PSAP using numerical simulation studies using point target, diffuse inclusion and microvasculature imaging, and in vivo feasibility studies on five healthy murine models. Qualitative and quantitative analysis demonstrate improvements in PAI image quality with PSAP compared to DAS and coherence factor weighted DAS (DAS _CF ). PSAP demonstrated improved target detectability with a higher generalized contrast-to-noise (gCNR) ratio in vasculature simulations where PSAP produces 19.61% and 19.53% higher gCNRs than DAS and DAS _CF , respectively. Furthermore, PSAP provided higher image contrast quantified using contrast ratio (CR) (e.g., PSAP produces 89.26% and 11.90% higher CR than DAS and DAS _CF in vasculature simulations) and improved clutter suppression.

Beamforming for large-area scan and improved SNR in array-based photoacoustic microscopy

Beamforming enhances the performance of array-based photoacoustic microscopy (PAM) systems for large-area scan. In this study, we quantify the imaging performance of a large field-of-view optical-resolution photoacoustic-microscopy system using an phased-array detector. The system combines a low-cost pulsed-laser diode with a 128-element linear ultrasound probe. Signal-to-noise ratio (SNR) and generalized contrast-to-noise ratio (gCNR) are quantified using the phased-array detector and applying three beamforming strategies: a no-beamforming method equivalent to a single-element flat transducer, a fixed focus beamforming method that mimics a single-element focused transducer, and a dynamic focus beamforming using a delay-and-sum (DAS) algorithm. The imaging capabilities of the system are demonstrated generating high-resolution images of tissue-mimicking phantoms containing sub-millimetre ink tubes and an ex vivo rabbit's ear. The results show that dynamic focus DAS beamforming increases and homogenizes SNR along 1-cm² images, reaching values up to 15 dB compared to an unfocused detector and up to 30 dB compared to out-of-focus regions of the fixed focus configuration. Moreover, the obtained values of gCNR using the DAS beamformer indicate an excellent target visibility, both on phantoms and ex vivo. This strategy makes it possible to scan larger surfaces compared to standard configurations using single-element detectors, paving the way for advanced array-based PAM systems.

Enhanced contrast acoustic-resolution photoacoustic microscopy using double-stage delay-multiply-and-sum beamformer for vasculature imaging

In acoustic-resolution photoacoustic microscopy (AR-PAM) systems, the lateral resolution in the focal zone of the ultrasound (US) transducer is determined by the numerical aperture (NA) of the transducer. To have a high lateral resolution, a large NA is used. However, the larger the NA, the smaller the depth of focus [DOF]. As a result, the lateral resolution is deteriorated at depths out of the focal region. The synthetic aperture focusing technique (SAFT) along with a beamformer can be used to improve the resolution outside the focal region. In this work, for image formation in AR-PAM, we propose the double-stage delay-multiply-and-sum (DS_DMAS) algorithm to be combined with SAFT. The proposed method is evaluated experimentally using hair targets and in vivo vasculature imaging. It is shown that DS_DMAS provides a higher resolution and contrast compared to other methods. For the B-mode images obtained using the hair phantom, the proposed method reduces the average noise level for all the depths by about 134%, 57% and 23%, compared to the original low- resolution, SAFT+DAS and SAFT+DMAS methods, respectively. All the results indicate that the proposed method can be an appropriate algorithm for image formation in AR-PAM systems.