Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm

Miniaturized preamplifier integration in ultrasound transducer design for enhanced photoacoustic imaging

Photoacoustic imaging (PAI) utilizes the photoacoustic effect to record both vascular and functional characteristics of a biological tissue. Photoacoustic signals have typically low amplitude that cannot be read efficiently by data acquisition systems. This necessitates the use of one or more amplifiers. These amplifiers are somewhat bulky (e.g., the ZFL-500LN+, Mini-Circuits, USA, or 351A-3-50-NI, Analog Modules Inc., USA). Here, we describe the fabrication and development process of a transducer with a built-in low-noise preamplifier that is encased within the transducer housing. This new, to the best of our knowledge, design could be advantageous for applications where a compact transducer + preamplifier is required. We demonstrate the performance of this compact detection unit in a laser scanning photoacoustic microscopy system by imaging a rat ear ex vivo and a rat brain vasculature in vivo.

Evaluation of 10 current image reconstruction algorithms for linear array photoacoustic imaging

Various reconstruction algorithms have been implemented for linear array photoacoustic imaging systems with the goal of accurately reconstructing the strength absorbers within the tissue being imaged. Since the existing algorithms have been introduced by different research groups and the context of performance evaluation was not consistent, it is difficult to make a fair comparison between them. In this study, we systematically compared the performance of 10 published image reconstruction algorithms (DAS, UBP, pDAS, DMAS, MV, EIGMV, SLSC, GSC, TR, and FD) using in-vitro phantom data. Evaluations were conducted based on lateral resolution of the reconstructed images, computational time, target detectability, and noise sensitivity. We anticipate the outcome of this study will assist researchers in selecting appropriate algorithms for their linear array PA imaging applications.

Improvement of LED-based photoacoustic imaging using lag-coherence factor (LCF) beamforming

BACKGROUND

Owing to its portability, affordability, and energy-efficiency, LED-based photoacoustic (PA) imaging is increasingly becoming popular when compared to its laser-based alternative, mainly for superficial vascular imaging applications. However, this technique suffers from low SNR and thereby limited imaging depth. As a result, visual image quality of LED-based PA imaging is not optimal, especially in sub-surface vascular imaging applications.

PURPOSE

Combination of linear ultrasound (US) probes and LED arrays are the most common implementation in LED-based PA imaging, which is currently being explored for different clinical imaging applications. Traditional delay-and-sum (DAS) is the most common beamforming algorithm in linear array-based PA detection. Side-lobes and reconstruction-related artifacts make the DAS performance unsatisfactory and poor for a clinical-implementation. In this work, we explored a new weighting-based image processing technique for LED-based PAs to yield improved image quality when compared to the traditional methods.

METHODS

We are proposing a lag-coherence factor (LCF), which is fundamentally based on the combination of the spatial auto-correlation of the detected PA signals. In LCF, the numerator contains lag-delay-multiply-and-sum (DMAS) beamformer instead of a conventional DAS beamformer. A spatial auto-correlation operation is performed between the detected US array signals before using DMAS beamformer. We evaluated the new method on both tissue-mimicking phantom (2D) and human volunteer imaging (3D) data acquired using a commercial LED-based PA imaging system.

RESULTS

Our novel correlation-based weighting technique showed LED-based PA image quality improvement when it is combined with conventional DAS beamformer. Both phantom and human volunteer imaging results gave a direct confirmation that by introducing LCF, image quality was improved and this method could reduce side-lobes and artifacts when compared to the DAS and coherence-factor (CF) approaches. Signal-to-noise ratio, generalized contrast-to-noise ratio, contrast ratio and spatial resolution were evaluated and compared with conventional beamformers to assess the reconstruction performance in a quantitative way. Results show that our approach offered image quality enhancement with an average signal-to-noise ratio and spatial resolution improvement of around 20% and 25% respectively, when compared with conventional CF based DAS algorithm.

CONCLUSIONS

Our results demonstrate that the proposed LCF based algorithm performs better than the conventional DAS and CF algorithms by improving signal-to-noise ratio and spatial resolution. Therefore, our new weighting technique could be a promising tool to improve the performance of LED-based PA imaging and thus accelerate its clinical translation.

Spatial resolution and reconstructed size accuracy using advanced beamformers in linear array-based PAT systems

Limitations associated with linear-array probes in photoacoustic tomography are partially compensated by using advanced beamformers that exploit the temporal and spatial coherence of the recorded signals, such as Delay Multiply and Sum (DMAS), Minimum Variance (MV) or coherence factor (CF), among others. However, their associated signal processing leads to an overestimation of the spatial resolution, as well as alterations in the reconstructed object size. Numerical and experimental results reported here support this hypothesis. First, we show that the Rayleigh criterion (RC) is the most suitable choice to characterize the spatial resolution instead of the Point Spread Function (PSF) when considering advanced beamformers. Then, we observe that several advanced beamformers fail to properly reconstruct target sizes slightly above the spatial resolution, underestimating their size. This work sheds light on the suitability of this type of beamformers combined with linear probes for determining sizes and morphology in photoacoustic images.

Quantification of prefrontal cortical neuronal ensembles following conditioned fear learning in a Fos-LacZ transgenic rat with photoacoustic imaging in Vivo

Understanding the neurobiology of complex behaviors requires measurement of activity in the discrete population of active neurons, neuronal ensembles, which control the behavior. Conventional neuroimaging techniques ineffectively measure neuronal ensemble activity in the brain in vivo because they assess the average regional neuronal activity instead of the specific activity of the neuronal ensemble that mediates the behavior. Our functional molecular photoacoustic tomography (FM-PAT) system allows direct imaging of Fos-dependent neuronal ensemble activation in Fos-LacZ transgenic rats in vivo. We tested four experimental conditions and found increased FM-PAT signal in prefrontal cortical areas in rats undergoing conditioned fear or novel context exposure. A parallel immunofluorescence ex vivo study of Fos expression found similar findings. These findings demonstrate the ability of FM-PAT to measure Fos-expressing neuronal ensembles directly in vivo and support a mechanistic role for the prefrontal cortex in higher-order processing of response to specific stimuli or environmental cues.

Image Quality Improvement Techniques and Assessment Adequacy in Clinical Optoacoustic Imaging: A Systematic Review

Optoacoustic imaging relies on the detection of optically induced acoustic waves to offer new possibilities in morphological and functional imaging. As the modality matures towards clinical application, research efforts aim to address multifactorial limitations that negatively impact the resulting image quality. In an endeavor to obtain a clear view on the limitations and their effects, as well as the status of this progressive refinement process, we conduct an extensive search for optoacoustic image quality improvement approaches that have been evaluated with humans in vivo, thus focusing on clinically relevant outcomes. We query six databases (PubMed, Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and Google Scholar) for articles published from 1 January 2010 to 31 October 2021, and identify 45 relevant research works through a systematic screening process. We review the identified approaches, describing their primary objectives, targeted limitations, and key technical implementation details. Moreover, considering comprehensive and objective quality assessment as an essential prerequisite for the adoption of such approaches in clinical practice, we subject 36 of the 45 papers to a further in-depth analysis of the reported quality evaluation procedures, and elicit a set of criteria with the intent to capture key evaluation aspects. Through a comparative criteria-wise rating process, we seek research efforts that exhibit excellence in quality assessment of their proposed methods, and discuss features that distinguish them from works with similar objectives. Additionally, informed by the rating results, we highlight areas with improvement potential, and extract recommendations for designing quality assessment pipelines capable of providing rich evidence.

Higher-order correlation based real-time beamforming in photoacoustic imaging

Although a delay-and-sum (DAS) beamformer is best suited for real-time photoacoustic (PA) image formation, the reconstructed images are often afflicted by noises, sidelobes, and other intense artifacts due to inaccurate assumptions in PA signal correlation. The present work aims to develop a reconstruction method that reduces the occurrence of sidelobes and artifacts and thus improves the reconstructed image quality or imaging performance. This beamformer is fundamentally based on higher-order signal correlation wherein a higher number of delayed PA signals-compared to conventional delay-multiply-and-sum (DMAS)-are combined and summed up. The proposed technique provides significant improvements in resolution, contrast, and signal-to-noise ratio (SNR) compared to traditional beamformers. For real-time implementation, the proposed algorithms were simplified, and their computational complexities were shrunk to the order of DAS [O(N)]. A GPU based study was also performed to validate the real-time capability of the proposed beamformers. For validation studies, both numerical simulation and experiments were conducted. Quantitative evaluation studies involving SNR, contrast ratio, generalized contrast-to-noise ratio, and FWHM demonstrate that the proposed higher-order DMAS beamformer is superior in PA image reconstruction. Conclusively, the proposed beamformer uniquely facilitates real-time PA image reconstruction with an achievable frame rate close to DAS and DMAS but with better imaging performance, which holds promise for real-time PA imaging and its clinical applications.

Tutorial on Development of 3D Vasculature Digital Phantoms for Evaluation of Photoacoustic Image Reconstruction Algorithms

A synthetic phantom model is typically utilized to evaluate the initial performance of a photoacoustic image reconstruction algorithm. The characteristics of the phantom model (structural, optical, and acoustic) are required to be very similar to those of the biological tissue. Typically, generic two-dimensional shapes are used as imaging targets to calibrate reconstruction algorithms. However, these structures are not representative of complex biological tissue, and therefore the artifacts that exist in reconstructed images of biological tissue vasculature are ignored. Real data from 3D MRI/CT volumes can be extrapolated to create high-quality phantom models; however, these sometimes involve complicated pre-processing and mostly are challenging, due to the inaccessibility of these datasets or the requirement for approval to utilize the data. Therefore, it is necessary to develop a 3D tissue-mimicking phantom model consisting of different compartments with characteristics that can be easily modified. In this tutorial, we present an optimized development process of a generic 3D complex digital vasculature phantom model in Blender. The proposed workflow is such that an accurate and easily editable digital phantom can be developed. Other workflows for creating the same phantom will take much longer to set up and require more time to edit. We have made a few examples of editable 3D phantom models, which are publicly available to test and modify.

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.

Refraction-Corrected Transcranial Ultrasound Imaging Through the Human Temporal Window Using a Single Probe

Transcranial ultrasound imaging (TUI) is a diagnostic modality with numerous applications, but unfortunately, it is hindered by phase aberration caused by the skull. In this article, we propose to reconstruct a transcranial B-mode image with a refraction-corrected synthetic aperture imaging (SAI) scheme. First, the compressional sound velocity of the aberrator (i.e., the skull) is estimated using the bidirectional headwave technique. The medium is described with four layers (i.e., lens, water, skull, and water), and a fast marching method calculates the travel times between individual array elements and image pixels. Finally, a delay-and-sum algorithm is used for image reconstruction with coherent compounding. The point spread function (PSF) in a wire phantom image and reconstructed with the conventional technique (using a constant sound speed throughout the medium), and the proposed method was quantified with numerical synthetic data and experiments with a bone-mimicking plate and a human skull, compared with the PSF achieved in a ground truth image of the medium without the aberrator (i.e., the bone plate or skull). A phased-array transducer (P4-1, ATL/Philips, 2.5 MHz, 96 elements, pitch = 0.295 mm) was used for the experiments. The results with the synthetic signals, the bone-mimicking plate, and the skull indicated that the proposed method reconstructs the scatterers with an average lateral/axial localization error of 0.06/0.14 mm, 0.11/0.13 mm, and 1.0/0.32 mm, respectively. With the human skull, an average contrast ratio (CR) and full-width-half-maximum (FWHM) of 37.1 dB and 1.75 mm were obtained with the proposed approach, respectively. This corresponds to an improvement of CR and FWHM by 7.1 dB and 36% compared with the conventional method, respectively. These numbers were 12.7 dB and 41% with the bone-mimicking plate.

Transfontanelle photoacoustic imaging: ultrasound transducer selection analysis

Transfontanelle ultrasound imaging (TFUI) is the conventional approach for diagnosing brain injury in neonates. Despite being the first stage imaging modality, TFUI lacks accuracy in determining the injury at an early stage due to degraded sensitivity and specificity. Therefore, a modality like photoacoustic imaging that combines the advantages of both acoustic and optical imaging can overcome the existing TFUI limitations. Even though a variety of transducers have been used in TFUI, it is essential to identify the transducer specification that is optimal for transfontanelle imaging using the photoacoustic technique. In this study, we evaluated the performance of 6 commercially available ultrasound transducer arrays to identify the optimal characteristics for transfontanelle photoacoustic imaging. We focused on commercially available linear and phased array transducer probes with center frequencies ranging from 2.5MHz to 8.5MHz which covers the entire spectrum of the transducer arrays used for brain imaging. The probes were tested on both in vitro and ex vivo brain tissue, and their performance in terms of transducer resolution, size, penetration depth, sensitivity, signal to noise ratio, signal amplification and reconstructed image quality were evaluated. The analysis of selected transducers in these areas allowed us to determine the optimal transducer for transfontanelle imaging, based on vasculature depth and blood density in tissue using ex vivo sheep brain. The outcome of this evaluation identified the two most suitable ultrasound transducer probes for transfontanelle photoacoustic imaging.

Improving Minimum Variance Beamforming with Sub-Aperture Processing for Photoacoustic Imaging

Minimum variance (MV) beamforming improves resolution and reduces sidelobes when compared to delay-and-sum (DAS) beamforming for photoacoustic imaging (PAI). However, some level of sidelobe signal and incoherent clutter persist degrading MV PAI quality. Here, an adaptive beamforming algorithm (PSAPMV) combining MV formulation and sub-aperture processing is proposed. In PSAPMV, the received channel data are split into two complementary nonoverlapping sub-apertures and beamformed using MV. A weighting matrix based on similarity between sub-aperture beamformed images was derived and multiplied with the full aperture MV image resulting in suppression of sidelobe and incoherent clutter in the PA image. Numerical simulation experiments with point targets, diffuse inclusions and microvasculature networks are used to validate PSAPMV. Quantitative evaluation was done in terms of main-lobe-to-side-lobe ratio, full width at half maximum (FWHM), contrast ratio (CR) and generalized contrast-to-noise ratio (gCNR). PSAPMV demonstrated improved beamforming performance both qualitatively and quantitatively. PSAPMV had higher resolution (FWHM =0.19 mm) than MV (0.21 mm) and DAS (0.22mm) in point target simulations, better target detectability (gCNR =0.99) than MV (0.89) and DAS (0.84) for diffuse inclusions and improved contrast (CR in microvasculature simulation, DAS = 15.38, MV = 22.42, PSAPMV = 51.74 dB).

Delay-and-sum-to-delay-standard-deviation factor: a promising adaptive beamformer

A new adaptive weighting method [delay-and-sum-to-delay-standard-deviation factor (DASDSF)] combined with minimum variance (MV) beamforming is introduced in photoacoustic imaging (PAI). Existing MV-based beamformers improve photoacoustic image quality in terms of achieving narrow main lobes and, thus, improving spatial resolution. But, the beamformers give a strong side-lobe signal strength that greatly degrades the reconstructed image contrast. As a feedback weighting factor, DASDSF addresses the persisting side-lobe issue present in MV-beamformed images, i.e., our proposed method is robust against reduction in noises as well as side lobes, and it outperforms MV and MV combined with coherence factor beamformers. Validation studies-being carried out both in numerical simulation and experiments employing a low-cost (16 elements) linear transducer array in a home-built PAI system-demonstrate an excellent performance of the proposed weighting approach in improving SNR, while reducing main-lobe width (i.e., FWHM) and side-lobe signal strength. The study demonstrates that the proposed algorithm holds promise for development of a cost-effective PAI system using a low-cost linear transducer (∼16 elements against ∼128 generally used).

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.

Nonlinear mechanisms in photoacoustics-Powerful tools in photoacoustic imaging

Many nonlinear effects have been discovered and developed in photoacoustic imaging. These nonlinear mechanisms have been explored for different utilizations, such as enhancing imaging contrast, measuring tissue temperature, achieving super-resolution imaging, enabling functional imaging, and extracting important physical parameters. This review aims to introduce different nonlinear mechanisms in photoacoustics, underline the fundamental principles, highlight their representative applications, and outline the occurrence conditions and applicable range of each nonlinear mechanism. Furthermore, this review thoroughly discusses the nonlinearity rule concerning how the mathematical structure of the nonlinear dependence is correlated to its practical applications. This summarization is useful for identifying and guiding the potential applications of nonlinearity based on their mathematical expressions, and is helpful for new nonlinear mechanism discovery or implementation in the future, which facilitates further breakthroughs in nonlinear photoacoustics.

Computationally efficient minimum-variance baseband delay-multiply-and-sum beamforming for adjustable enhancement of ultrasound image resolution

Baseband Delay-Multiply-and-Sum (BB-DMAS) beamforming takes advantage of the baseband spatial coherence of receiving aperture to improve image resolution and contrast. Meanwhile, the side-lobe clutter and noise level can also be effectively suppressed in BB-DMAS beamforming due to their low coherence when being detected by channels in different spatial locations. BB-DMAS scales the magnitude of channel signal by p-th root and restores the output dimensionality by p-th power after channel summation. Higher p value introduces more spatial coherence into DMAS beamforming and provides higher image resolution at the cost of background speckle quality. In this study, a computationally efficient integration of BB-DMAS with minimum-variance (MV) beamforming is developed so that the image resolution can be drastically improved with low p value (e.g. p < 2) while maintaining the speckle quality. For each image pixel, the proposed MV-DMAS only requires single MV estimation to optimize the aperture apodization for DMAS beamforming. Our simulation results show that, with p = 1.5, the -6-dB lateral width of wire reflector noticeably improves from 0.22 mm to 0.13 mm by adopting MV estimation in BB-DMAS beamforming. In MV-DMAS, the suppression of uncorrelated random noises also remains effective. Experimental results not only confirm the superior resolution in MV-DMAS beamforming but also demonstrates comparable image contrast and speckle quality to BB-DMAS counterpart. In conclusion, MV-DMAS beamforming can provide improvement in image resolution while maintaining the other image quality metrics using an efficient combination of moderate spatial coherence and MV estimation of receiving aperture apodization in ultrasonic imaging.

Photoacoustic tomography reconstruction using lag-based delay multiply and sum with a coherence factor improves in vivo ovarian cancer diagnosis

Ovarian cancer is the fifth most common cause of death due to cancer, and it is the deadliest of all gynecological cancers. Diagnosing ovarian cancer via conventional photoacoustic delay-and-sum beamforming (DAS) presents several challenges, such as poor image resolution and low lesion to background tissue contrast. To address these concerns, we propose an improved beamformer named lag-based delay multiply and sum combined with coherence factor (DMAS-LAG-CF). Simulations and phantom experiments demonstrate that compared with the conventional DAS, the proposed algorithm can provide 1.39 times better resolution and 10.77 dB higher contrast. For patient data, similar performance on contrast ratios has been observed. However, since the diagnostic accuracy between cancer and benign/normal groups is a significant measure, we have extracted photoacoustic histogram features of mean, kurtosis and skewness. DMAS-LAG-CF can improve cancer diagnosis with an AUC of 0.91 for distinguishing malignant vs. benign ovarian lesions when mean and skewness are used as features.

Generalized spatial coherence reconstruction for photoacoustic computed tomography

SIGNIFICANCE

Coherence, a fundamental property of waves and fields, plays a key role in photoacoustic image reconstruction. Previously, techniques such as short-lag spatial coherence (SLSC) and filtered delay, multiply, and sum (FDMAS) have utilized spatial coherence to improve the reconstructed resolution and contrast with respect to delay-and-sum (DAS). While SLSC uses spatial coherence directly as the imaging contrast, FDMAS employs spatial coherence implicitly. Despite being more robust against noise, both techniques have their own drawbacks: SLSC does not preserve a relative signal magnitude, and FDMAS shows a reduced contrast-to-noise ratio.

AIM

To overcome these limitations, our aim is to develop a beamforming algorithm-generalized spatial coherence (GSC)-that unifies SLSC and FDMAS into a single equation and outperforms both beamformers.

APPROACH

We demonstrated the application of GSC in photoacoustic computed tomography (PACT) through simulation and experiments and compared it to previous beamformers: DAS, FDMAS, and SLSC.

RESULTS

GSC outperforms the imaging metrics of previous state-of-the-art coherence-based beamformers in both simulation and experiments.

CONCLUSIONS

GSC is an innovative reconstruction algorithm for PACT, which combines the strengths of FDMAS and SLSC expanding PACT's applications.

Photoacoustic-guided surgery from head to toe [Invited]

Photoacoustic imaging-the combination of optics and acoustics to visualize differences in optical absorption - has recently demonstrated strong viability as a promising method to provide critical guidance of multiple surgeries and procedures. Benefits include its potential to assist with tumor resection, identify hemorrhaged and ablated tissue, visualize metal implants (e.g., needle tips, tool tips, brachytherapy seeds), track catheter tips, and avoid accidental injury to critical subsurface anatomy (e.g., major vessels and nerves hidden by tissue during surgery). These benefits are significant because they reduce surgical error, associated surgery-related complications (e.g., cancer recurrence, paralysis, excessive bleeding), and accidental patient death in the operating room. This invited review covers multiple aspects of the use of photoacoustic imaging to guide both surgical and related non-surgical interventions. Applicable organ systems span structures within the head to contents of the toes, with an eye toward surgical and interventional translation for the benefit of patients and for use in operating rooms and interventional suites worldwide. We additionally include a critical discussion of complete systems and tools needed to maximize the success of surgical and interventional applications of photoacoustic-based technology, spanning light delivery, acoustic detection, and robotic methods. Multiple enabling hardware and software integration components are also discussed, concluding with a summary and future outlook based on the current state of technological developments, recent achievements, and possible new directions.

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing potential for handheld clinical periodontal imaging.

Dictionary-based protoacoustic dose map imaging for proton range verification

Proton radiotherapy has the potential to provide state-of-the-art dose conformality in the tumor area, reducing possible adverse effects on surrounding organs at risk. However, uncertainties in the exact location of the proton Bragg peak inside the patient prevent this technique from achieving full clinical potential. In this context, in vivo verification of the range of protons in patients is key to reduce uncertainty margins. Protoacoustic range verification employs acoustic pressure waves generated by protons due to the radio-induced thermoacoustic effect to reconstruct the dose deposited in a patient during proton therapy. In this paper, we propose to use the a priori knowledge of the shape of the proton dose distribution to create a dictionary with the expected ultrasonic signals at predetermined detector locations. Using this dictionary, the reconstruction of deposited dose is performed by matching pre-calculated dictionary acoustic signals with data acquired online during treatment. The dictionary method was evaluated on a single-field proton plan for a prostate cancer patient. Dose calculation was performed with the open-source treatment planning system matRad, while acoustic wave propagation was carried out with k-Wave. We studied the ability of the proposed dictionary method to detect range variations caused by anatomical changes in tissue density, and alterations of lateral and longitudinal beam position. Our results show that the dictionary-based protoacoustic method was able to identify the changes in range originated by all the alterations introduced, with an average accuracy of 1.4 mm. This procedure could be used for in vivo verification, comparing the measured signals with the precalculated dictionary.

Spatiotemporal Coherence Weighting for In Vivo Cardiac Photoacoustic Image Beamformation

Photoacoustic (PA) image reconstruction generally utilizes delay-and-sum (DAS) beamforming of received acoustic waves from tissue irradiated with optical illumination. However, nonadaptive DAS reconstructed cardiac PA images exhibit temporally varying noise which causes reduced myocardial PA signal specificity, making image interpretation difficult. Adaptive beamforming algorithms such as minimum variance (MV) with coherence factor (CF) weighting have been previously reported to improve the DAS image quality. In this article, we report on an adaptive beamforming algorithm by extending CF weighting to the temporal domain for preclinical cardiac PA imaging (PAI). The proposed spatiotemporal coherence factor (STCF) considers multiple temporally adjacent image acquisition events during beamforming and cancels out signals with low spatial coherence and temporal coherence, resulting in higher background noise cancellation while preserving the main features of interest (myocardial wall) in the resultant PA images. STCF has been validated using the numerical simulations and in vivo ECG and respiratory-signal-gated cardiac PAI in healthy murine hearts. The numerical simulation results demonstrate that STCF weighting outperforms DAS and MV beamforming with and without CF weighting under different levels of inherent contrast, acoustic attenuation, optical scattering, and signal-to-noise (SNR) of channel data. Performance improvement is attributed to higher sidelobe reduction (at least 5 dB) and SNR improvement (at least 10 dB). Improved myocardial signal specificity and higher signal rejection in the left ventricular chamber and acoustic gel region are observed with STCF in cardiac PAI.

Technical considerations in the Verasonics research ultrasound platform for developing a photoacoustic imaging system

Photoacoustic imaging (PAI) is an emerging functional and molecular imaging technology that has attracted much attention in the past decade. Recently, many researchers have used the vantage system from Verasonics for simultaneous ultrasound (US) and photoacoustic (PA) imaging. This was the motivation to write on the details of US/PA imaging system implementation and characterization using Verasonics platform. We have discussed the experimental considerations for linear array based PAI due to its popularity, simple setup, and high potential for clinical translatability. Specifically, we describe the strategies of US/PA imaging system setup, signal generation, amplification, data processing and study the system performance.

Ultra-broadband axicon transducer for optoacoustic endoscopy

Image performance in optoacoustic endoscopy depends markedly on the design of the transducer employed. Ideally, high-resolution performance is required over an expanded depth of focus. Current optoacoustic focused transducers achieve lateral resolutions in the range of tens of microns in the mesoscopic regime, but their depth of focus is limited to hundreds of microns by the nature of their spherical geometry. We designed an ultra-broadband axicon detector with a 2 mm central aperture and investigated whether the imaging characteristics exceeded those of a spherical detector of similar size. We show a previously undocumented ability to achieve a broadband elongated pencil-beam optoacoustic sensitivity with an axicon detection geometry, providing approximately 40 μm-lateral resolution maintained over a depth of focus of 950 μm—3.8 times that of the reference spherical detector. This performance could potentially lead to optoacoustic endoscopes that can visualize optical absorption deeper and with higher resolution than any other optical endoscope today.

High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.

Skull acoustic aberration correction in photoacoustic microscopy using a vector space similarity model: a proof-of-concept simulation study

Skull bone represents a highly acoustical impedance mismatch and a dispersive barrier for the propagation of acoustic waves. Skull distorts the amplitude and phase information of the received waves at different frequencies in a transcranial brain imaging. We study a novel algorithm based on vector space similarity model for the compensation of the skull-induced distortions in transcranial photoacoustic microscopy. The results of the algorithm tested on a simplified numerical skull phantom, demonstrate a fully recovered vasculature with the recovery rate of 91.9%.

Multi-Operator Minimum Variance Adaptive Beamforming Algorithms Accelerated With GPU

The goal of this work is to design high-resolution, high-contrast and robust MV adaptive beamforming algorithms, which are also implemented in real-time frame rate. Multi-operator optimization is introduced into MV adaptive beamforming in this work to propose a multi-operator MV adaptive beamforming algorithmic optimization framework. Based on the proposed algorithmic optimization framework, the algorithm optimization can be either conducted by activating a single optimization operator, or conducted by activating multiple optimization operators. The multi-operator MV (MOMV) adaptive beamforming algorithms are then derived from this framework. Moreover, in order to promote the real-time imaging capability of MOMV beamforming, a GPU-based parallel acceleration framework is proposed along with the algorithmic optimization framework by exploring the image-level coarse-grained parallelization and pixel-level fine-grained parallelization. GPU computing resource allocation strategy and memory access strategy are both explored to better design the acceleration framework. Comprehensive quantitative simulation evaluations and qualitative in vivo experiments of imaging performance are studied, and the results demonstrate that the proposed MOMV adaptive beamforming algorithms significantly improve the imaging performance as compared with other MV beamforming algorithms, which have high resolution, high contrast, good robustness, and real-time imaging capability with thousands of acceleration speedup. Furthermore, the MOMV beamforming algorithm without eigen-decomposition and projection optimization operator achieves much higher beamforming frame rate with little downgrade of image quality as compared with the MOMV beamforming algorithm with all optimization operators.

Investigation of the Effect of the Skull in Transcranial Photoacoustic Imaging: A Preliminary Ex Vivo Study

Although transcranial photoacoustic imaging (TCPAI) has been used in small animal brain imaging, in animals with thicker skull bones or in humans both light illumination and ultrasound propagation paths are affected. Hence, the PA image is largely degraded and in some cases completely distorted. This study aims to investigate and determine the maximum thickness of the skull through which photoacoustic imaging is feasible in terms of retaining the imaging target structure without incorporating any post processing. We identify the effect of the skull on both the illumination path and acoustic propagation path separately and combined. In the experimental phase, the distorting effect of ex vivo sheep skull bones with thicknesses in the range of 0.7~1.3 mm are explored. We believe that the findings in this study facilitate the clinical translation of TCPAI.

Overview of Ultrasound Detection Technologies for Photoacoustic Imaging

Ultrasound detection is one of the major components of photoacoustic imaging systems. Advancement in ultrasound transducer technology has a significant impact on the translation of photoacoustic imaging to the clinic. Here, we present an overview on various ultrasound transducer technologies including conventional piezoelectric and micromachined transducers, as well as optical ultrasound detection technology. We explain the core components of each technology, their working principle, and describe their manufacturing process. We then quantitatively compare their performance when they are used in the receive mode of a photoacoustic imaging system.

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.

Multiple Delay and Sum With Enveloping Beamforming Algorithm for Photoacoustic Imaging

Delay and Sum (DAS) is one of the most common beamforming algorithms for photoacoustic imaging (PAI) reconstruction. Based on calculating beamformed signal with simple delaying and summing, DAS can function in a quick response and is quite suitable for real-time PAI. However, high sidelobes and intense artifacts may appear when using DAS due to summing with unnecessary data. In this paper, a beamforming algorithm called Multiple Delay and Sum with Enveloping (multi-DASE) is introduced to solve this problem. Compared to DAS, the multi-DASE algorithm calculates not only the initial value of the beamformed signal but also the complete N-shaped photoacoustic signal for each pixel. Through computer simulation, a phantom experiment and experiment on human finger joint, the multi-DASE algorithm is compared with other beamforming methods in removing artifacts by evaluating the quality of the reconstructed images. Furthermore, by rearranging the calculation sequences, the multi-DASE algorithm can be computing in parallel using GPU acceleration to meet the needs of real-time clinical application.

Simultaneous Photoacoustic Imaging and Cavitation Mapping in Shockwave Lithotripsy

Kidney stone disease is a major health problem worldwide. Shockwave lithotripsy (SWL), which uses high-energy shockwave pulses to break up kidney stones, is extensively used in clinic. However, despite its noninvasiveness, SWL can produce cavitation in vivo. The rapid expansion and violent collapse of cavitation bubbles in small blood vessels may result in renal vascular injury. To better understand the mechanism of tissue injury and improve treatment safety and efficiency, it is highly desirable to concurrently detect cavitation and vascular injury during SWL. Current imaging modalities used in SWL ( e.g. , C-arm fluoroscopy and B-mode ultrasound) are not sensitive to vascular injuries. By contrast, photoacoustic imaging is a non-invasive and non-radiative imaging modality that is sensitive to blood, by using hemoglobin as the endogenous contrast. Moreover, photoacoustic imaging is also compatible with passive cavitation detection by sharing the ultrasound detection system. Here, we have integrated shockwave treatment, photoacoustic imaging, and passive cavitation detection into a single system. Our experimental results on phantoms and in vivo small animals have collectively demonstrated that the integrated system is capable of capturing shockwave-induced cavitation and the resultant vascular injury simultaneously. We expect that the integrated system, when combined with our recently developed internal-light-illumination photoacoustic imaging, will find important applications for monitoring shockwave-induced vascular injury in deep tissues during SWL.

Combining autocorrelation signals with delay multiply and sum beamforming algorithm for ultrasound imaging

Beamformer is one of the most important components in ultrasound imaging system. The delay and sum (DAS) beamforming algorithm has been widely used in recent decades due to its simplicity and robustness. However, it has poor impact on resolution and contrast. A new beamformer named filtered delay multiply and sum (F-DMAS) which was an alternative of delay multiply and sum (DMAS) was proposed to overcome these shortcomings of DAS. Although F-DMAS partially enhances the image quality, its performance still has room for improvement. Therefore, a novel beamformer named lag-based delay multiply and sum (L-DMAS) which combines autocorrelation signals with DMAS algorithm is proposed by us to improve its efficiency. Field II was employed to synthesize a point target phantom and a cyst phantom to compare the performance between DAS, F-DMAS, double stage delay multiply and sum (DS-DMAS), and L-DMAS. We also estimate the performance of four algorithms on experimental data and in vivo data. These results show that both DS-DMAS and L-DMAS are better than DAS and F-DMAS in each case. In some cases, DS-DMAS and L-DMAS have little difference in performance, but in other cases, L-DMAS outperforms DS-DMAS. Graphical Abstract.

Development of a Stationary 3D Photoacoustic Imaging System Using Sparse Single-Element Transducers: Phantom Study

Photoacoustic imaging (PAI) is an emerging label-free and non-invasive modality for imaging biological tissues. PAI has been implemented in different configurations, one of which is photoacoustic computed tomography (PACT) with a potential wide range of applications, including brain and breast imaging. Hemispherical Array PACT (HA-PACT) is a variation of PACT that has solved the limited detection-view problem. Here, we designed an HA-PACT system consisting of 50 single element transducers. For implementation, we initially performed a simulation study, with parameters close to those in practice, to determine the relationship between the number of transducers and the quality of the reconstructed image. We then used the greatest number of transducers possible on the hemisphere and imaged copper wire phantoms coated with a light absorbing material to evaluate the performance of the system. Several practical issues such as light illumination, arrangement of the transducers, and an image reconstruction algorithm have been comprehensively studied.

Review of cost reduction methods in photoacoustic computed tomography

Photoacoustic Computed Tomography (PACT) is a major configuration of photoacoustic imaging, a hybrid noninvasive modality for both functional and molecular imaging. PACT has rapidly gained importance in the field of biomedical imaging due to superior performance as compared to conventional optical imaging counterparts. However, the overall cost of developing a PACT system is one of the challenges towards clinical translation of this novel technique. The cost of a typical commercial PACT system originates from optical source, ultrasound detector, and data acquisition unit. With growing applications of photoacoustic imaging, there is a tremendous demand towards reducing its cost. In this review article, we have discussed various approaches to reduce the overall cost of a PACT system, and provided a cost estimation to build a low-cost PACT system.

GPU-accelerated Double-stage Delay-multiply-and-sum Algorithm for Fast Photoacoustic Tomography Using LED Excitation and Linear Arrays

Double-stage delay-multiply-and-sum (DS-DMAS) is an algorithm proposed for photoacoustic image reconstruction. The DS-DMAS algorithm offers a higher contrast than conventional delay-and-sum and delay-multiply and-sum but at the expense of higher computational complexity. Here, we utilized a compute unified device architecture (CUDA) graphics processing unit (GPU) parallel computation approach to address the high complexity of the DS-DMAS for photoacoustic image reconstruction generated from a commercial light-emitting diode (LED)-based photoacoustic scanner. In comparison with a single-threaded central processing unit (CPU), the GPU approach increased speeds by nearly 140-fold for 1024 × 1024 pixel image; there was no decrease in accuracy. The proposed implementation makes it possible to reconstruct photoacoustic images with frame rates of 250, 125, and 83.3 when the images are 64 × 64, 128 × 128, and 256 × 256, respectively. Thus, DS-DMAS can be efficiently used in clinical devices when coupled with CUDA GPU parallel computation.

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

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution. In this paper, it is proposed to form the photoacoustic (PA) images through a regularized inverse problem to address these limitations and improve the image quality. We define a forward/backward problem of the beamforming and solve the inverse problem using a sparse constraint added to the model which forces the sparsity of the output beamformed data. It is shown that the proposed Sparse beamforming (SB) method is robust against noise due to the sparsity nature of the problem. Numerical results show that the SB method improves the signal-to-noise ratio (SNR) for about 98.69 dB, 82.26 dB and 74.73 dB, in average, compared to DAS, delay-multiply-and-sum (DMAS) and double stage-DMAS (DS-DMAS), respectively. Also, quantitative evaluation of the experimental results shows a significant noise reduction using SB algorithm. In particular, the contrast ratio of the wire phantom at the depth of 30 mm is improved about 103.97 dB, 82.16 dB and 65.77 dB compared to DAS, DMAS and DS-DMAS algorithms, respectively, indicating a better performance of the proposed SB in terms of noise reduction.

Photoacoustic/Ultrasound/Optical Coherence Tomography Evaluation of Melanoma Lesion and Healthy Skin in a Swine Model

The marked increase in the incidence of melanoma coupled with the rapid drop in the survival rate after metastasis has promoted the investigation into improved diagnostic methods for melanoma. High-frequency ultrasound (US), optical coherence tomography (OCT), and photoacoustic imaging (PAI) are three potential modalities that can assist a dermatologist by providing extra information beyond dermoscopic features. In this study, we imaged a swine model with spontaneous melanoma using these modalities and compared the images with images of nearby healthy skin. Histology images were used for validation.

Image improvement in linear-array photoacoustic imaging using high resolution coherence factor weighting technique

Background

In Photoacoustic imaging (PAI), the most prevalent beamforming algorithm is delay-and-sum (DAS) due to its simple implementation. However, it results in a low quality image affected by the high level of sidelobes. Coherence factor (CF) can be used to address the sidelobes in the reconstructed images by DAS, but the resolution improvement is not good enough, compared to the high resolution beamformers such as minimum variance (MV). In this paper, it is proposed to use high-resolution-CF (HRCF) weighting technique in which MV is used instead of the existing DAS in the formula of the conventional CF.

Results

The higher performance of HRCF is proved numerically and experimentally. The quantitative results obtained with the simulations show that at the depth of 40 mm, in comparison with DAS+CF and MV+CF, HRCF improves the full-width-half-maximum of about 91% and 15% and the signal-to-noise ratio about 40% and 14%, respectively.

Conclusion

Proposed method provides a high resolution along with a low level of sidelobes for PAI.

A Pseudo-Dynamic Delay Calculation Using Optimal Zone Segmentation for Ultra-Compact Ultrasound Imaging Systems

The implementation of dynamic delay calculations (DDCs) is challenging for ultra-compact ultrasound imaging due to the enormous computation and power consumption requirements. Here, we present an efficient pseudo-DDC method based on optimal zone segmentation (PDC-Optimal), which significantly decreases these requirements relative to an unconstrained DDC method: reductions in flip-flops of 84.35% and in look-up tables of 94.19%, respectively. The reductions lead to an up to 94.53% lower dynamic power consumption and provide image quality comparable to the unconstrained DDC method. The proposed PDC-Optimal method also provides adaptive flexibility between beamforming accuracy and battery life using the delay error allowance, a user-definable parameter. A conventional pseudo-DDC method using uniform zone segmentation (PDC-Conv) presented substantial image degradation in the near imaging field when the same number of zone segments was used. Therefore, the PDC-Optimal method provides an efficient yet flexible DDC solution to improve the experiences for ultra-compact ultrasound imaging system users.

Skull's Photoacoustic Attenuation and Dispersion Modeling with Deterministic Ray-Tracing: Towards Real-Time Aberration Correction

Although transcranial photoacoustic imaging has been previously investigated by several groups, there are many unknowns about the distorting effects of the skull due to the impedance mismatch between the skull and underlying layers. The current computational methods based on finite-element modeling are slow, especially in the cases where fine grids are defined for a large 3-D volume. We develop a very fast modeling/simulation framework based on deterministic ray-tracing. The framework considers a multilayer model of the medium, taking into account the frequency-dependent attenuation and dispersion effects that occur in wave reflection, refraction, and mode conversion at the skull surface. The speed of the proposed framework is evaluated. We validate the accuracy of the framework using numerical phantoms and compare its results to k-Wave simulation results. Analytical validation is also performed based on the longitudinal and shear wave transmission coefficients. We then simulated, using our method, the major skull-distorting effects including amplitude attenuation, time-domain signal broadening, and time shift, and confirmed the findings by comparing them to several ex vivo experimental results. It is expected that the proposed method speeds up modeling and quantification of skull tissue and allows the development of transcranial photoacoustic brain imaging.

The double-stage delay-multiply-and-sum image reconstruction method improves imaging quality in a LED-based photoacoustic array scanner

Light-emitting diode-based photoacoustic imaging is more compact and affordable than laser-based systems, but it has low power and hence a high number of replicates. Here, we describe double-stage delay-multiply-and-sum (DS-DMAS) to improve image quality collected on a LED-based scanner. DS-DMAS was evaluated experimentally using point targets (in different laterals and depths) as well as a hair and a rabbit eye. This algorithm can compensate for the low SNR of LED-based systems and offer better lateral resolution of about 60%, 25%, higher contrast ratio of about 97%, 34%, and better full-width-half-maximum of about 60%, 25%, versus delay-and-sum) and delay-multiply-and-sum, respectively. More importantly, DS-DMAS offers this using a smaller number of frames (only 2% of all the frames). These results indicate that DS-DMAS might be a valuable tool in the translation of LED-based and other low power PAI systems.

Signed Real-Time Delay Multiply and Sum Beamforming for Multispectral Photoacoustic Imaging

Reconstruction of photoacoustic (PA) images acquired with clinical ultrasound transducers is usually performed using the Delay and Sum (DAS) beamforming algorithm. Recently, a variant of DAS, referred to as Delay Multiply and Sum (DMAS) beamforming has been shown to provide increased contrast, signal-to-noise ratio (SNR) and resolution in PA imaging. The main reasons for the use of DAS beamforming in photoacoustics are its simple implementation, real-time capability, and the linearity of the beamformed image to the PA signal. This is crucial for the identification of different chromophores in multispectral PA applications. In contrast, current DMAS implementations are not responsive to the full spectrum of sound frequencies from a photoacoustic source and have not been shown to provide a reconstruction linear to the PA signal. Furthermore, due to its increased computational complexity, DMAS has not been shown yet to work in real-time. Here, we present an open-source real-time variant of the DMAS algorithm, signed DMAS (sDMAS), that ensures linearity in the original PA signal response while providing the increased image quality of DMAS. We show the applicability of sDMAS for multispectral PA applications, in vitro and in vivo. The sDMAS and reference DAS algorithms were integrated in the open-source Medical Imaging Interaction Toolkit (MITK) and are available as real-time capable implementations.

Photoacoustic Signal Enhancement: Towards Utilization of Low Energy Laser Diodes in Real-Time Photoacoustic Imaging

In practice, photoacoustic (PA) waves generated with cost-effective and low-energy laser diodes, are weak and almost buried in noise. Reconstruction of an artifact-free PA image from noisy measurements requires an effective denoising technique. Averaging is widely used to increase the signal-to-noise ratio (SNR) of PA signals; however, it is time consuming and in the case of very low SNR signals, hundreds to thousands of data acquisition epochs are needed. In this study, we explored the feasibility of using an adaptive and time-efficient filtering method to improve the SNR of PA signals. Our results show that the proposed method increases the SNR of PA signals more efficiently and with much fewer acquisitions, compared to common averaging techniques. Consequently, PA imaging is conducted considerably faster.

A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography

One of the major concerns in photoacoustic computed tomography (PACT) is obtaining a high-quality image using the minimum number of ultrasound transducers/view angles. This issue is of importance when a cost-effective PACT system is needed. On the other hand, analytical reconstruction algorithms such as back projection (BP) and time reversal, when a limited number of view angles is used, cause artifacts in the reconstructed image. Iterative algorithms provide a higher image quality, compared to BP, due to a model used for image reconstruction. The performance of the model can be further improved using the sparsity concept. In this paper, we propose using a novel sparse dictionary to capture important features of the photoacoustic signal and eliminate the artifacts while few transducers is used. Our dictionary is an optimum combination of Wavelet Transform (WT), Discrete Cosine Transform (DCT), and Total Variation (TV). We utilize two quality assessment metrics including peak signal-to-noise ratio and edge preservation index to quantitatively evaluate the reconstructed images. The results show that the proposed method can generate high-quality images having fewer artifacts and preserved edges, when fewer view angles are used for reconstruction in PACT.

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.

Photoacoustic image formation based on sparse regularization of minimum variance beamformer

Delay-and-sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast. In this paper, a novel algorithm, named Modified-Sparse-MV (MS-MV), is proposed in which a ℓ 1-norm constraint is added to the MV minimization problem after some modifications, in order to suppress the sidelobes more efficiently, compared to MV. The added constraint can be interpreted as the sparsity of the output of the MV beamformed signals. Since the final minimization problem is convex, it can be solved efficiently using a simple iterative algorithm. The numerical results show that the proposed method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in average, compared to MV. Also, the experimental results, using a wire-target phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in comparison with the MV.

Low Temperature-Mediated Enhancement of Photoacoustic Imaging Depth

We study the temperature dependence of the underlying mechanisms related to the signal strength and imaging depth in photoacoustic imaging. The presented theoretical and experimental results indicate that imaging depth can be improved by lowering the temperature of the intermediate medium that the laser passes through to reach the imaging target. We discuss the temperature dependency of optical and acoustic properties of the intermediate medium and their changes due to cooling. We demonstrate that the SNR improvement of the photoacoustic signal is mainly due to the reduction of Grüneisen parameter of the intermediate medium which leads to a lower level of background noise. These findings may open new possibilities toward the application of biomedical laser refrigeration.