Synthetic-aperture based photoacoustic re-beamforming (SPARE) approach using beamformed ultrasound data

Automatic search for photoacoustic marker using automated transrectal ultrasound

Real-time transrectal ultrasound (TRUS) image guidance during robot-assisted laparoscopic radical prostatectomy has the potential to enhance surgery outcomes. Whether conventional or photoacoustic TRUS is used, the robotic system and the TRUS must be registered to each other. Accurate registration can be performed using photoacoustic (PA markers). However, this requires a manual search by an assistant [IEEE Robot. Autom. Lett8, 1287 (2023).10.1109/LRA.2022.3191788]. This paper introduces the first automatic search for PA markers using a transrectal ultrasound robot. This effectively reduces the challenges associated with the da Vinci-TRUS registration. This paper investigated the performance of three search algorithms in simulation and experiment: Weighted Average (WA), Golden Section Search (GSS), and Ternary Search (TS). For validation, a surgical prostate scenario was mimicked and various ex vivo tissues were tested. As a result, the WA algorithm can achieve 0.53°±0.30° average error after 9 data acquisitions, while the TS and GSS algorithm can achieve 0.29 ∘ ± 0.31 ∘ and 0.48°±0.32° average errors after 28 data acquisitions.

Intraoperative laparoscopic photoacoustic image guidance system in the da Vinci surgical system

This paper describes a framework allowing intraoperative photoacoustic (PA) imaging integrated into minimally invasive surgical systems. PA is an emerging imaging modality that combines the high penetration of ultrasound (US) imaging with high optical contrast. With PA imaging, a surgical robot can provide intraoperative neurovascular guidance to the operating physician, alerting them of the presence of vital substrate anatomy invisible to the naked eye, preventing complications such as hemorrhage and paralysis. Our proposed framework is designed to work with the da Vinci surgical system: real-time PA images produced by the framework are superimposed on the endoscopic video feed with an augmented reality overlay, thus enabling intuitive three-dimensional localization of critical anatomy. To evaluate the accuracy of the proposed framework, we first conducted experimental studies in a phantom with known geometry, which revealed a volumetric reconstruction error of 1.20 ± 0.71 mm. We also conducted an ex vivo study by embedding blood-filled tubes into chicken breast, demonstrating the successful real-time PA-augmented vessel visualization onto the endoscopic view. These results suggest that the proposed framework could provide anatomical and functional feedback to surgeons and it has the potential to be incorporated into robot-assisted minimally invasive surgical procedures.

Achieving depth-independent lateral resolution in AR-PAM using the synthetic-aperture focusing technique

Acoustic-resolution photoacoustic microscopy (AR-PAM) is a promising imaging modality that renders images with ultrasound resolution and extends the imaging depth beyond the optical ballistic regime. To achieve a high lateral resolution, a large numerical aperture (NA) of a focused transducer is usually applied for AR-PAM. However, AR-PAM fails to hold its performance in the out-of-focus region. The lateral resolution and signal-to-noise ratio (SNR) degrade substantially, thereby leading to a significantly deteriorated image quality outside the focal area. Based on the concept of the synthetic-aperture focusing technique (SAFT), various strategies have been developed to address this challenge. These include 1D-SAFT, 2D-SAFT, adaptive-SAFT, spatial impulse response (SIR)-based schemes, and delay-multiply-and-sum (DMAS) strategies. These techniques have shown progress in achieving depth-independent lateral resolution, while several challenges remain. This review aims to introduce these developments in SAFT-based approaches, highlight their fundamental mechanisms, underline the advantages and limitations of each approach, and discuss the outlook of the remaining challenges for future advances.

Acoustic-resolution photoacoustic microscope based on compact and low-cost delta configuration actuator

The state-of-the-art configurations for acoustic-resolution photoacoustic (PA) microscope (AR-PAM) are large in size and expensive, hindering their democratization. While previous research on AR-PAMs introduced a low-cost light source to reduce the cost, few studies have investigated the possibility of optimizing the sensor actuation, particularly for the AR-PAM. Additionally, there is an unmet need to evaluate the image quality deterioration associated with the actuation inaccuracy. A low-cost actuation device is introduced to reduce the system size and cost of the AR-PAM while maintaining the image quality by implementing the advanced beamformers. This work proposes an AR-RAM incorporating the delta configuration actuator adaptable from a low-cost off-the-shelf 3D printer as the sensor actuation device. The image degradation due to the data acquisition positioning inaccuracy is evaluated in the simulation. We further assess the mitigation of potential actuation precision uncertainty through advanced 3D synthetic aperture focusing algorithms represented by the Delay-and-Sum (DAS) with Coherence Factor (DAS+CF) and Delay-Multiply-and-Sum (DMAS) algorithms. The simulation study demonstrated the tolerance of image quality on actuation inaccuracy and the effect of compensating the actuator motion precision error through advanced reconstruction algorithms. With those algorithms, the image quality degradation was suppressed to within 25% with the presence of 0.2 mm motion inaccuracy. The experimental evaluation using phantoms and an ex-vivo sample presented the applicability of low-cost delta configuration actuators for AR-PAMs. The measured full width at half maximum of the 0.2 mm diameter pencil-lead phantom were 0.45 ± 0.06 mm, 0.31 ± 0.04 mm, and 0.35 ± 0.07 mm, by applying the DAS, DAS+CF, and DMAS algorithms, respectively. AR-PAMs with a compact and low-cost delta configuration provide high-quality PA imaging with better accessibility for biomedical applications. The research evaluated the image degradation contributed by the actuation inaccuracy and suggested that the advanced beamformers are capable of suppressing the actuation inaccuracy.

GPU implementation of photoacoustic short-lag spatial coherence imaging for improved image-guided interventions

SIGNIFICANCE

Photoacoustic-based visual servoing is a promising technique for surgical tool tip tracking and automated visualization of photoacoustic targets during interventional procedures. However, one outstanding challenge has been the reliability of obtaining segmentations using low-energy light sources that operate within existing laser safety limits.

AIM

We developed the first known graphical processing unit (GPU)-based real-time implementation of short-lag spatial coherence (SLSC) beamforming for photoacoustic imaging and applied this real-time algorithm to improve signal segmentation during photoacoustic-based visual servoing with low-energy lasers.

APPROACH

A 1-mm-core-diameter optical fiber was inserted into ex vivo bovine tissue. Photoacoustic-based visual servoing was implemented as the fiber was manually displaced by a translation stage, which provided ground truth measurements of the fiber displacement. GPU-SLSC results were compared with a central processing unit (CPU)-SLSC approach and an amplitude-based delay-and-sum (DAS) beamforming approach. Performance was additionally evaluated with in vivo cardiac data.

RESULTS

The GPU-SLSC implementation achieved frame rates up to 41.2 Hz, representing a factor of 348 speedup when compared with offline CPU-SLSC. In addition, GPU-SLSC successfully recovered low-energy signals (i.e., ≤268  μJ) with mean ± standard deviation of signal-to-noise ratios of 11.2  ±  2.4 (compared with 3.5  ±  0.8 with conventional DAS beamforming). When energies were lower than the safety limit for skin (i.e., 394.6  μJ for 900-nm wavelength laser light), the median and interquartile range (IQR) of visual servoing tracking errors obtained with GPU-SLSC were 0.64 and 0.52 mm, respectively (which were lower than the median and IQR obtained with DAS by 1.39 and 8.45 mm, respectively). GPU-SLSC additionally reduced the percentage of failed segmentations when applied to in vivo cardiac data.

CONCLUSIONS

Results are promising for the use of low-energy, miniaturized lasers to perform GPU-SLSC photoacoustic-based visual servoing in the operating room with laser pulse repetition frequencies as high as 41.2 Hz.

An economic photoacoustic imaging platform using automatic laser synchronization and inverse beamforming

We present a proof-of-concept of an automatic integration of photoacoustic (PA) imaging on clinical ultrasound (US) imaging platforms. Here we tackle two critical challenges: the laser synchronization and the inaccessibility to the beamformer core embedded in commercial US imaging platform. In particular, the line trigger frequency (LTF) estimation and the asynchronous synthetic aperture inverse beamforming (ASAIB) were developed and evaluated in both k-Wave simulation and phantom experiment. The proposed method is an economical solution to enable PA imaging on a greater number of US equipment to further thrive the PA imaging research community.

Efficient nonlinear beamformer based on P'th root of detected signals for linear-array photoacoustic tomography: application to sentinel lymph node imaging

In linear-array transducer-based photoacoustic (PA) imaging, B-scan PA images are formed using the raw channel PA signals. Delay-and-sum (DAS) is the most prevalent algorithm due to its simple implementation, but it leads to low-quality images. Delay-multiply-and-sum (DMAS) provides a higher image quality in comparison with DAS while it imposes a computational burden of O  (  M2  )  . We introduce a nonlinear (NL) beamformer for linear-array PA imaging, which uses the p'th root of the detected signals and imposes the complexity of DAS [O  (  M  )  ]. The proposed algorithm is evaluated numerically and experimentally [wire-target and in-vivo sentinel lymph node (SLN) imaging], and the effects of the parameter p are investigated. The results show that the NL algorithm, using a root of p (NL_p), leads to lower sidelobes and higher signal-to-noise ratio compared with DAS and DMAS, for (p  >  2). The sidelobes level (for the wire-target phantom), at the depth of 11.4 mm, are about -31, -52, -52, -67, -88, and -109  dB, for DAS, DMAS, NL_2, NL_3, NL_4, and NL_5, respectively, indicating the superiority of the NL_p algorithm. In addition, the best value of p for SLN imaging is reported to be 12.

Enhanced linear-array photoacoustic beamforming using modified coherence factor

Photoacoustic imaging (PAI) is a promising medical imaging modality providing the spatial resolution of ultrasound imaging and the contrast of optical imaging. For linear-array PAI, a beamformer can be used as the reconstruction algorithm. Delay-and-sum (DAS) is the most prevalent beamforming algorithm in PAI. However, using DAS beamformer leads to low-resolution images as well as high sidelobes due to nondesired contribution of off-axis signals. Coherence factor (CF) is a weighting method in which each pixel of the reconstructed image is weighted, based on the spatial spectrum of the aperture, to mainly improve the contrast. We demonstrate that the numerator of the formula of CF contains a DAS algebra and propose the use of a delay-multiply-and-sum beamformer instead of the available DAS on the numerator. The proposed weighting technique, modified CF (MCF), has been evaluated numerically and experimentally compared to CF. It was shown that MCF leads to lower sidelobes and better detectable targets. The quantitative results of the experiment (using wire targets) show that MCF leads to for about 45% and 40% improvement, in comparison with CF, in the terms of signal-to-noise ratio and full-width-half-maximum, respectively.

Deconvolution based photoacoustic reconstruction with sparsity regularization

In most photoacoustic tomography (PAT) reconstruction approaches, it is assumed that the receiving transducers have omnidirectional response and can fully surround the region of interest. These assumptions are not satisfied in practice. To deal with these limitations, we present a novel deconvolution based photoacoustic reconstruction with sparsity regularization (DPARS) technique. The DPARS algorithm is a semi-analytical reconstruction approach in which the projections of the absorber distribution derived from a deconvolution-based method are computed and used to generate a large linear system of equations. In these projections, computed over limited viewing angles, the directivity effect of the transducer is taken into account. The distribution of absorbers is computed using a sparse representation of absorber coefficients obtained from the discrete cosine transform. This sparse representation helps improve the numerical conditioning of the system of equations and reduces the computation time of the deconvolution-based approach by one order of magnitude relative to Tikhonov regularization. The algorithm has been tested in simulations, and using two-dimensional and three-dimensional experimental data obtained with a conventional ultrasound transducer. The results show that DPARS, when evaluated using contrast-to-noise ratio and root-mean-square errors, outperforms the conventional delay-and-sum (DAS) reconstruction method.