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

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.

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.

Development and characterization of transfontanelle photoacoustic imaging system for detection of intracranial hemorrhages and measurement of brain oxygenation: Ex-vivo

We have developed and optimized an imaging system to study and improve the detection of brain hemorrhage and to quantify oxygenation. Since this system is intended to be used for brain imaging in neonates through the skull opening, i.e., fontanelle, we called it, Transfontanelle Photoacoustic Imaging (TFPAI) system. The system is optimized in terms of optical and acoustic designs, thermal safety, and mechanical stability. The lower limit of quantification of TFPAI to detect the location of hemorrhage and its size is evaluated using in-vitro and ex-vivo experiments. The capability of TFPAI in measuring the tissue oxygenation and detection of vasogenic edema due to brain blood barrier disruption are demonstrated. The results obtained from our experimental evaluations strongly suggest the potential utility of TFPAI, as a portable imaging modality in the neonatal intensive care unit. Confirmation of these findings in-vivo could facilitate the translation of this promising technology to the clinic.

Pixel-based approach to delay multiply and sum beamforming in combination with Wiener filter for improving ultrasound image quality

Unified pixel-based (PB) beamforming has been implemented for ultrasound imaging, offering significant enhancements in lateral resolution compared to the conventional dynamic focusing. However, it still suffers from clutter and off-axis artifacts, limiting the contrast resolution. This paper proposes an efficient method to improve image quality by integrating filtered delay multiply and sum (F-DMAS) into the framework. This hybrid strategy incorporates the spatial coherence of the received data into the beamforming process to improve contrast resolution and clutter rejection in the generated image. We also integrate a Wiener filter to suppress the spatiotemporal spreading using signals echoed from a single scatterer at the transmit focus as a kernel for the deconvolution. The Wiener filter is applied to the received waveforms before performing the hybrid strategy. The Wiener filter is shown to reduce interference due to the interaction between the excitation pulse and the transfer functions of the transducer elements, thus benefiting the axial resolution of the generated images. We validate the proposed method and compare it with other beamforming strategies through a series of experiments, including simulation, phantom, and in vivo studies. The results show that our approach can substantially improve both spatial resolution and contrast over the unified PB algorithm, while still maintaining the good features of this beamformer. The simplicity and good performance of our method show its potential for use in clinical applications.

An optimized total focusing method based on delay-multiply-and-sum for nondestructive testing

Total focusing method (TFM) attracts much interest because of high image resolution and large inspection coverage. However, the synthetic focusing approach based on delay-and-sum beamforming employs only the defect information contained in the dataset while ignoring the spatial information of the array signals, leading to limited imaging performance mixed with artifacts and noise. In addition, the signal-to-noise ratio (SNR) suffers due to single-element emission of full matrix capture. This work combines a modified delay-multiply-and-sum (DMAS) beamforming approach with conventional synthetic focusing in the TFM algorithm, to achieve optimization of TFM imaging performance. DMAS-based TFM is able to take full advantage of the defect and spatial information in the array dataset, and to generate new frequency components for better image reconstruction. As demonstrated on a series of comparative simulation and experimental results, the imaging results of the optimized TFM provide a considerable improvement in SNR. Better lateral spatial resolution is also achieved due to the increased number of equivalent transducer elements and second harmonic component. Therefore, this work provides a quite promising alternative solution for the post-processing of ultrasonic phased array with improved imaging performance.

Novel spatio-temporal non-linear beamformers for sparse synthetic aperture ultrasound imaging

The development of two modified non-linear beamformers, Spatio-Temporal Delay Multiply and Sum (ST-DMAS) and Spatio-Temporal Delay Euclidian-Weighted Multiply and Sum (ST-DewMAS) is reported in this paper. A sparse-transmit scheme (with only 8 transmits) on Synthetic Transmit Aperture technique (sparse STA) was chosen to evaluate the beamformers ability to generate the high-resolution Ultrasound image. These methods allow for obtaining superior-quality imaging at enhanced frame rates. The different beamformers of ST-DewMAS, ST-DMAS, Filtered Delay Multiply and Sum (F-DMAS), and Delay and Sum (DAS), were compared in terms of the Axial and Lateral Resolutions, AR and LR, respectively, Contrast-to-Noise Ratio (CNR), Contrast Ratio (CR), and Generalized CNR (GCNR). Experimental results demonstrate that the developed ST-DMAS and ST-DewMAS reconstruction on sparse STA technique resulted in better quality images compared to those obtained using DAS and F-DMAS. Specifically, the metrics of AR, LR CR, CNR, and GCNR showed improvements of more than 25% (for ST-DMAS) and 40 % (for ST-DewMAS) over those from DAS and F-DMAS beamformed images, respectively. Thus, the results demonstrate that the frame rate and image quality of an US system can both be enhanced by ST-DewMAS compared to the beamformers of F-DMAS and DAS.

Photoacoustic image synthesis with generative adversarial networks

Photoacoustic tomography (PAT) has the potential to recover morphological and functional tissue properties with high spatial resolution. However, previous attempts to solve the optical inverse problem with supervised machine learning were hampered by the absence of labeled reference data. While this bottleneck has been tackled by simulating training data, the domain gap between real and simulated images remains an unsolved challenge. We propose a novel approach to PAT image synthesis that involves subdividing the challenge of generating plausible simulations into two disjoint problems: (1) Probabilistic generation of realistic tissue morphology, and (2) pixel-wise assignment of corresponding optical and acoustic properties. The former is achieved with Generative Adversarial Networks (GANs) trained on semantically annotated medical imaging data. According to a validation study on a downstream task our approach yields more realistic synthetic images than the traditional model-based approach and could therefore become a fundamental step for deep learning-based quantitative PAT (qPAT).

Deep Learning-Based Photoacoustic Imaging of Vascular Network Through Thick Porous Media

Photoacoustic imaging is a promising approach used to realize in vivo transcranial cerebral vascular imaging. However, the strong attenuation and distortion of the photoacoustic wave caused by the thick porous skull greatly affect the imaging quality. In this study, we developed a convolutional neural network based on U-Net to extract the effective photoacoustic information hidden in the speckle patterns obtained from vascular network images datasets under porous media. Our simulation and experimental results show that the proposed neural network can learn the mapping relationship between the speckle pattern and the target, and extract the photoacoustic signals of the vessels submerged in noise to reconstruct high-quality images of the vessels with a sharp outline and a clean background. Compared with the traditional photoacoustic reconstruction methods, the proposed deep learning-based reconstruction algorithm has a better performance with a lower mean absolute error, higher structural similarity, and higher peak signal-to-noise ratio of reconstructed images. In conclusion, the proposed neural network can effectively extract valid information from highly blurred speckle patterns for the rapid reconstruction of target images, which offers promising applications in transcranial photoacoustic imaging.

Semantic segmentation of multispectral photoacoustic images using deep learning

Photoacoustic (PA) imaging has the potential to revolutionize functional medical imaging in healthcare due to the valuable information on tissue physiology contained in multispectral photoacoustic measurements. Clinical translation of the technology requires conversion of the high-dimensional acquired data into clinically relevant and interpretable information. In this work, we present a deep learning-based approach to semantic segmentation of multispectral photoacoustic images to facilitate image interpretability. Manually annotated photoacoustic and ultrasound imaging data are used as reference and enable the training of a deep learning-based segmentation algorithm in a supervised manner. Based on a validation study with experimentally acquired data from 16 healthy human volunteers, we show that automatic tissue segmentation can be used to create powerful analyses and visualizations of multispectral photoacoustic images. Due to the intuitive representation of high-dimensional information, such a preprocessing algorithm could be a valuable means to facilitate the clinical translation of photoacoustic imaging.

Machine learning enabled multiple illumination quantitative optoacoustic oximetry imaging in humans

Optoacoustic (OA) imaging is a promising modality for quantifying blood oxygen saturation (sO₂) in various biomedical applications - in diagnosis, monitoring of organ function, or even tumor treatment planning. We present an accurate and practically feasible real-time capable method for quantitative imaging of sO₂ based on combining multispectral (MS) and multiple illumination (MI) OA imaging with learned spectral decoloring (LSD). For this purpose we developed a hybrid real-time MI MS OA imaging setup with ultrasound (US) imaging capability; we trained gradient boosting machines on MI spectrally colored absorbed energy spectra generated by generic Monte Carlo simulations and used the trained models to estimate sO₂ on real OA measurements. We validated MI-LSD in silico and on in vivo image sequences of radial arteries and accompanying veins of five healthy human volunteers. We compared the performance of the method to prior LSD work and conventional linear unmixing. MI-LSD provided highly accurate results in silico and consistently plausible results in vivo. This preliminary study shows a potentially high applicability of quantitative OA oximetry imaging, using our method.

SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics

SIGNIFICANCE

Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings.

AIM

To facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards.

APPROACH

SIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA's module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models.

RESULTS

To showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations.

CONCLUSIONS

SIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa, and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments.

Regional-Lag Signed Delay Multiply and Sum Beamforming in Ultrafast Ultrasound Imaging

Ultrafast ultrasound imaging provides very high frame rates but provides poor imaging quality due to unfocused beams. The delay multiply and sum (DMAS) beamformer has been used to improve ultrafast ultrasound imaging contrast but is always accompanied by oversuppression, which produces low-quality speckle images and degrades the contrast performance. A smaller maximum lag in the signed DMAS (sDMAS) contributes better speckle preservation but lower resolution for hyperechoic scatters. To overcome this tradeoff, a regional-lag signed delay multiply and sum (rsDMAS) beamformer is proposed in this article. Innovatively, a region discrimination tool realized by the generalized coherence factor (GCF) is used to limit the maximum lag for spatial coherence estimation. Subaperture coherence smoothing estimates the short-lag coherence instead of multiplication in pairs, thereby reducing calculation complexity and smoothing the speckle texture. Normalization and sign correction are also introduced to achieve better beamforming output. The simulated, phantom, and in vivo data are adopted to evaluate the effectiveness of the proposed beamformer. Numerical results show that the proposed method achieves improvements of the contrast ratio (CR) by 9%, contrast-to-noise ratio (CNR) by 41%, speckle signal-to-noise ratio (sSNR) by 41%, and generalized contrast-to-noise ratio (gCNR) by 0.0004 compared with DMAS (in simulation). Resolution experiments show that the proposed method obtains a loss of 0.07 mm in the full width at half maximum (FWHM) and the same separability of close point scatters as DMAS. These findings indicate that the proposed method achieves higher contrast performance at less obvious sacrifice of the lateral resolution than DMAS.

Optimally-weighted non-linear beamformer for conventional focused beam ultrasound imaging systems

A novel non-linear beamforming method, namely, filtered delay optimally-weighted multiply and sum (F-DowMAS) beamforming is reported for conventional focused beamforming (CFB) technique. The performance of F-DowMAS was compared against delay and sum (DAS), filtered delay multiply and sum (F-DMAS), filtered delay weight multiply and sum (F-DwMAS) and filter delay Euclidian weighted multiply and sum (F-DewMAS) methods. Notably, in the proposed method the optimal adaptive weights are computed for each imaging point to compensate for the effects due to spatial variations in beam pattern in CFB technique. F-DowMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral resolution (LR), axial resolution (AR), contrast ratio (CR) and contrast-to-noise ratio (CNR), estimated from experiments on a commercially available tissue-mimicking phantom. The results demonstrate that F-DowMAS improved the AR by 57.04% and 46.95%, LR by 58.21% and 53.40%, CR by 67.35% and 39.25%, and CNR by 44.04% and 30.57% compared to those obtained using DAS and F-DMAS, respectively. Thus, it can be concluded that the newly proposed F-DowMAS outperforms DAS and F-DMAS. As an aside, we also show that the optimal weighting strategy can be extended to benefit DAS.

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).

Euclidian-Weighted Non-linear Beamformer for Conventional Focused Beam Ultrasound Imaging Systems

In this paper, the recently developed method of, Filtered Delay Euclidian-Weighted Multiply and Sum (F-DewMAS), is newly investigated for Conventional Focused Beamforming (CFB) technique. The performance of F-DewMAS method was compared with the established Delay and Sum (DAS) method and the popular non-linear beamforming method of F-DMAS. The different methods of F-DewMAS, F-DMAS, and DAS were compared in terms of the resulting image quality metrics, Lateral Resolution (LR), Axial Resolution (AR), Contrast Ratio (CR) and contrast-to-noise ratio (CNR), in experiments on Nylon point scatterer and CIRS Triple modality Abdominal phantoms. Experimental results show that F-DewMAS resulted in improvements of AR by 35.56% and 25.33%, LR by 42.97 % and 31.05 % and CR by 119.94% and 61.46% compared to those obtained using DAS and F-DMAS, respectively. The CNR of F-DewMAS is 46.33 % more compared to F-DMAS. Hence, it can be concluded that the image quality is improved appreciably by F-DewMAS compared to DAS and F-DMAS.Clinical Relevance-The developed method can improve the resolution and contrast of the image, which results in better visualization of finer details and thus may aid in better diagnosis.

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.

Deep learning for biomedical photoacoustic imaging: A review

Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requires the solving of inverse image reconstruction problems, which have proven extremely difficult to solve. The application of deep learning methods has recently exploded in popularity, leading to impressive successes in the context of medical imaging and also finding first use in the field of PAI. Deep learning methods possess unique advantages that can facilitate the clinical translation of PAI, such as extremely fast computation times and the fact that they can be adapted to any given problem. In this review, we examine the current state of the art regarding deep learning in PAI and identify potential directions of research that will help to reach the goal of clinical applicability.

Enhancement of in vivo cardiac photoacoustic signal specificity using spatiotemporal singular value decomposition

SIGNIFICANCE

Photoacoustic imaging (PAI) can be used to infer molecular information about myocardial health non-invasively in vivo using optical excitation at ultrasonic spatial resolution. For clinical and preclinical linear array imaging systems, conventional delay-and-sum (DAS) beamforming is typically used. However, DAS cardiac PA images are prone to artifacts such as diffuse quasi-static clutter with temporally varying noise-reducing myocardial signal specificity. Typically, multiple frame averaging schemes are utilized to improve the quality of cardiac PAI, which affects the spatial and temporal resolution and reduces sensitivity to subtle PA signal variation. Furthermore, frame averaging might corrupt myocardial oxygen saturation quantification due to the presence of natural cardiac wall motion. In this paper, a spatiotemporal singular value decomposition (SVD) processing algorithm is proposed to reduce DAS PAI artifacts and subsequent enhancement of myocardial signal specificity.

AIM

Demonstrate enhancement of PA signals from myocardial tissue compared to surrounding tissues and blood inside the left-ventricular (LV) chamber using spatiotemporal SVD processing with electrocardiogram (ECG) and respiratory signal (ECG-R) gated in vivo murine cardiac PAI.

APPROACH

In vivo murine cardiac PAI was performed by collecting single wavelength (850 nm) photoacoustic channel data on eight healthy mice. A three-dimensional (3D) volume of complex PAI data over a cardiac cycle was reconstructed using a custom ECG-R gating algorithm and DAS beamforming. Spatiotemporal SVD was applied on a two-dimensional Casorati matrix generated using the 3D volume of PAI data. The singular value spectrum (SVS) was then filtered to remove contributions from diffuse quasi-static clutter and random noise. Finally, SVD processed beamformed images were derived using filtered SVS and inverse SVD computations.

RESULTS

Qualitative comparison with DAS and minimum variance (MV) beamforming shows that SVD processed images had better myocardial signal specificity, contrast, and target detectability. DAS, MV, and SVD images were quantitatively evaluated by calculating contrast ratio (CR), generalized contrast-to-noise ratio (gCNR), and signal-to-noise ratio (SNR). Quantitative evaluations were done at three cardiac time points (during systole, at end-systole (ES), and during diastole) identified from co-registered ultrasound M-Mode image. Mean CR, gCNR, and SNR values of SVD images at ES were 245, 115.15, and 258.17 times higher than DAS images with statistical significance evaluated with one-way analysis of variance.

CONCLUSIONS

Our results suggest that significantly better-quality images can be realized using spatiotemporal SVD processing for in vivo murine cardiac PAI.

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.

Weighted non-linear beamformers for low cost 2-element receive ultrasound imaging system

In this paper, the development of modified beamforming methods, Filtered Delay Weight Multiply and Sum (F-DwMAS) and Filtered Delay Euclidian-Weighted Multiply and Sum (F-DewMAS), are reported. These methods were investigated on a minimum-redundancy synthetic aperture technique, called as 2 element Receive Synthetic Aperture Focusing Technique (2R-SAFT), which uses one element on transmit and two consecutive elements on receive, for reducing hardware complexity without compromising much on the image quality. The performance of the developed F-DwMAS and F-DewMAS methods were compared with Delay and Sum (DAS) and recently introduced F-DMAS beamforming methods. Notably, in the proposed methods, an additional aperture window function is designed and incorporated to the F-DMAS method. The different methods of F-DwMAS, F-DewMAS, F-DMAS and DAS were compared in terms of the resulting image quality metrics, Lateral Resolution (LR), Axial Resolution (AR), Contrast Ratio (CR) and contrast-to-noise ratio (CNR), in simulation and experiments on tissue-mimicking phantoms. Experimental results show that (F-DwMAS) and {F-DewMAS} resulted in improvements of AR by (46.32% and 23.51%), {43.56% and 17.78%}, LR by (47.81% and 30.27%), {44.26% and 26.14%} and CR by (45.68% and 17.15%), {42.16% and 9.87%} compared to those obtained using DAS and F-DMAS, respectively. However, CNR of F-DwMAS and F-DewMAS was found to be 31.19% and 21.16% less compared to DAS, but 4.89% and 18.64% more than F-DMAS, respectively. Hence, it can be concluded that the image quality improved by both F-DwMAS and F-DewMAS compared to DAS and F-DMAS. Also, between F-DwMAS and F-DewMAS, the later has the advantage of ready applicability to different acquisition schemes and settings compared to the former also having an additional advantage of better CNR compared to both F-DMAS and F-DewMAS.

High Resolution, High Contrast Beamformer Using Minimum Variance and Plane Wave Nonlinear Compounding with Low Complexity

The plane wave compounding (PWC) is a promising modality to improve the imaging quality and maintain the high frame rate for ultrafast ultrasound imaging. In this paper, a novel beamforming method is proposed to achieve higher resolution and contrast with low complexity. A minimum variance (MV) weight calculated by the partial generalized sidelobe canceler is adopted to beamform the receiving array signals. The dimension reduction technique is introduced to project the data into lower dimensional space, which also contributes to a large subarray length. Estimation of multi-wave receiving covariance matrix is performed and then utilized to determine only one weight. Afterwards, a fast second-order reformulation of the delay multiply and sum (DMAS) is developed as nonlinear compounding to composite the beamforming output of multiple transmissions. Simulations, phantom, in vivo, and robustness experiments were carried out to evaluate the performance of the proposed method. Compared with the delay and sum (DAS) beamformer, the proposed method achieved 86.3% narrower main lobe width and 112% higher contrast ratio in simulations. The robustness to the channel noise of the proposed method is effectively enhanced at the same time. Furthermore, it maintains a linear computational complexity, which means that it has the potential to be implemented for real-time response.

Deep-Learning Image Reconstruction for Real-Time Photoacoustic System

Recent advances in photoacoustic (PA) imaging have enabled detailed images of microvascular structure and quantitative measurement of blood oxygenation or perfusion. Standard reconstruction methods for PA imaging are based on solving an inverse problem using appropriate signal and system models. For handheld scanners, however, the ill-posed conditions of limited detection view and bandwidth yield low image contrast and severe structure loss in most instances. In this paper, we propose a practical reconstruction method based on a deep convolutional neural network (CNN) to overcome those problems. It is designed for real-time clinical applications and trained by large-scale synthetic data mimicking typical microvessel networks. Experimental results using synthetic and real datasets confirm that the deep-learning approach provides superior reconstructions compared to conventional methods.

A Review on Advances in Intra-operative Imaging for Surgery and Therapy: Imagining the Operating Room of the Future

With the advent of Minimally Invasive Surgery (MIS), intra-operative imaging has become crucial for surgery and therapy guidance, allowing to partially compensate for the lack of information typical of MIS. This paper reviews the advancements in both classical (i.e. ultrasounds, X-ray, optical coherence tomography and magnetic resonance imaging) and more recent (i.e. multispectral, photoacoustic and Raman imaging) intra-operative imaging modalities. Each imaging modality was analyzed, focusing on benefits and disadvantages in terms of compatibility with the operating room, costs, acquisition time and image characteristics. Tables are included to summarize this information. New generation of hybrid surgical room and algorithms for real time/in room image processing were also investigated. Each imaging modality has its own (site- and procedure-specific) peculiarities in terms of spatial and temporal resolution, field of view and contrasted tissues. Besides the benefits that each technique offers for guidance, considerations about operators and patient risk, costs, and extra time required for surgical procedures have to be considered. The current trend is to equip surgical rooms with multimodal imaging systems, so as to integrate multiple information for real-time data extraction and computer-assisted processing. The future of surgery is to enhance surgeons eye to minimize intra- and after-surgery adverse events and provide surgeons with all possible support to objectify and optimize the care-delivery process.

Photoacoustics can image spreading depolarization deep in gyrencephalic brain

Spreading depolarization (SD) is a self-propagating wave of near-complete neuronal depolarization that is abundant in a wide range of neurological conditions, including stroke. SD was only recently documented in humans and is now considered a therapeutic target for brain injury, but the mechanisms related to SD in complex brains are not well understood. While there are numerous approaches to interventional imaging of SD on the exposed brain surface, measuring SD deep in brain is so far only possible with low spatiotemporal resolution and poor contrast. Here, we show that photoacoustic imaging enables the study of SD and its hemodynamics deep in the gyrencephalic brain with high spatiotemporal resolution. As rapid neuronal depolarization causes tissue hypoxia, we achieve this by continuously estimating blood oxygenation with an intraoperative hybrid photoacoustic and ultrasonic imaging system. Due to its high resolution, promising imaging depth and high contrast, this novel approach to SD imaging can yield new insights into SD and thereby lead to advances in stroke, and brain injury research.