Acoustic Resolution Photoacoustic Microscopy Research Articles

Enhancing vascular network visualization in 3D photoacoustic imaging: in vivo experiments with a vasculature filter

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

Acoustic resolution photoacoustic Doppler flowmetry for assessment of patient rectal cancer blood perfusion.

Photoacoustic Doppler flowmetry offers quantitative blood perfusion information in addition to photoacoustic vascular contrast for rectal cancer assessment. We aim to develop and validate a correlational Doppler flowmetry utilizing an acoustic resolution photoacoustic microscopy (AR-PAM) system for blood perfusion analysis. To extract blood perfusion information, we implemented AR-PAM Doppler flowmetry consisting of signal filtering and conditioning, A-line correlation, and angle compensation. We developed flow phantoms and contrast agent to systemically investigate the flowmetry's efficacy in a series of phantom studies. The developed correlational Doppler flowmetry was applied to images collected during in vivo AR-PAM for post-treatment rectal cancer evaluation. The linearity and accuracy of the Doppler flow measurement system were validated in phantom studies. Imaging rectal cancer patients treated with chemoradiation demonstrated the feasibility of using correlational Doppler flowmetry to assess treatment response and distinguish residual cancer from cancer-free tumor bed tissue and normal rectal tissue. A new correlational Doppler flowmetry was developed and validated through systematic phantom evaluations. The results of its application to in vivo patients suggest it could be a useful addition to photoacoustic endoscopy for post-treatment rectal cancer assessment.

Highly sensitive self-focused ultrasound transducer with a bionic back-reflector for multiscale-resolution photoacoustic microscopy.

In this study, we designed a self-focused ultrasonic transducer made of polyvinylidene fluoride (PVDF). This transducer involves a back-reflector, which is modeled after tapetum lucidum in the eyes of some nocturnal animals. The bionic structure reflects the ultrasound, which passes through the PVDF membrane, back to PVDF and provides a second chance for the PVDF to convert the ultrasound to electric signals. This design increases the amount of ultrasound absorbed by the PVDF, thereby improving the detection sensitivity. Both ultrasonic and photoacoustic (PA) experiments were conduct to characterize the performance of the transducer. The results show that the fabricated transducer has a center frequency of 13.07 MHz, and a bandwidth of 96% at -6 dB. With an acoustic numerical aperture (NA) of 0.64, the transducer provides a lateral resolution of 140µm. Importantly, the bionic design improves the detection sensitivity of the transducer about 30%. Finally, we apply the fabricated transducer to optical-resolution (OR) and acoustic-resolution photoacoustic microscopy (AR-PAM) to achieve multiscale-resolution PA imaging. Imaging of the bamboo leaf and the leaf skeleton demonstrates that the proposed transducer can provide high spatial resolution, better imaging intensity and contrast. Therefore, the proposed transducer design will be useful to enhance the performance of multiscale-resolution PAM.

Enhanced resolution and sensitivity acoustic-resolution photoacoustic microscopy with semi/unsupervised GANs

Acoustic-resolution photoacoustic microscopy (AR-PAM) enables visualization of biological tissues at depths of several millimeters with superior optical absorption contrast. However, the lateral resolution and sensitivity of AR-PAM are generally lower than those of optical-resolution PAM (OR-PAM) owing to the intrinsic physical acoustic focusing mechanism. Here, we demonstrate a computational strategy with two generative adversarial networks (GANs) to perform semi/unsupervised reconstruction with high resolution and sensitivity in AR-PAM by maintaining its imaging capability at enhanced depths. The b-scan PAM images were prepared as paired (for semi-supervised conditional GAN) and unpaired (for unsupervised CycleGAN) groups for label-free reconstructed AR-PAM b-scan image generation and training. The semi/unsupervised GANs successfully improved resolution and sensitivity in a phantom and in vivo mouse ear test with ground truth. We also confirmed that GANs could enhance resolution and sensitivity of deep tissues without the ground truth.

Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior

Acoustic resolution photoacoustic microscopy (AR-PAM) is a promising medical imaging modality that can be employed for deep bio-tissue imaging. However, its relatively low imaging resolution has greatly hindered its wide applications. Previous model-based or learning-based PAM enhancement algorithms either require design of complex handcrafted prior to achieve good performance or lack the interpretability and flexibility that can adapt to different degradation models. However, the degradation model of AR-PAM imaging is subject to both imaging depth and center frequency of ultrasound transducer, which varies in different imaging conditions and cannot be handled by a single neural network model. To address this limitation, an algorithm integrating both learning-based and model-based method is proposed here so that a single framework can deal with various distortion functions adaptively. The vasculature image statistics is implicitly learned by a deep convolutional neural network, which served as plug and play (PnP) prior. The trained network can be directly plugged into the model-based optimization framework for iterative AR-PAM image enhancement, which fitted for different degradation mechanisms. Based on physical model, the point spread function (PSF) kernels for various AR-PAM imaging situations are derived and used for the enhancement of simulation and in vivo AR-PAM images, which collectively proved the effectiveness of proposed method. Quantitatively, the PSNR and SSIM values have all achieve best performance with the proposed algorithm in all three simulation scenarios; The SNR and CNR values have also significantly raised from 6.34 and 5.79 to 35.37 and 29.66 respectively in an in vivo testing result with the proposed algorithm.

Depth-extended acoustic-resolution photoacoustic microscopy based on a two-stage deep learning network.

Acoustic resolution photoacoustic microscopy (AR-PAM) is a major modality of photoacoustic imaging. It can non-invasively provide high-resolution morphological and functional information about biological tissues. However, the image quality of AR-PAM degrades rapidly when the targets move far away from the focus. Although some works have been conducted to extend the high-resolution imaging depth of AR-PAM, most of them have a small focal point requirement, which is generally not satisfied in a regular AR-PAM system. Therefore, we propose a two-stage deep learning (DL) reconstruction strategy for AR-PAM to recover high-resolution photoacoustic images at different out-of-focus depths adaptively. The residual U-Net with attention gate was developed to implement the image reconstruction. We carried out phantom and in vivo experiments to optimize the proposed DL network and verify the performance of the proposed reconstruction method. Experimental results demonstrated that our approach extends the depth-of-focus of AR-PAM from 1mm to 3mm under the 4 mJ/cm2 light energy used in the imaging system. In addition, the imaging resolution of the region 2 mm far away from the focus can be improved, similar to the in-focus area. The proposed method effectively improves the imaging ability of AR-PAM and thus could be used in various biomedical studies needing deeper depth.

High-fidelity deconvolution for acoustic-resolution photoacoustic microscopy enabled by convolutional neural networks

Acoustic-resolution photoacoustic microscopy (AR-PAM) image resolution is determined by the point spread function (PSF) of the imaging system. Previous algorithms, including Richardson–Lucy (R–L) deconvolution and model-based (MB) deconvolution, improve spatial resolution by taking advantage of the PSF as prior knowledge. However, these methods encounter the problems of inaccurate deconvolution, meaning the deconvolved feature size and the original one are not consistent (e.g., the former can be smaller than the latter). We present a novel deep convolution neural network (CNN)-based algorithm featuring high-fidelity recovery of multiscale feature size to improve lateral resolution of AR-PAM. The CNN is trained with simulated image pairs of line patterns, which is to mimic blood vessels. To investigate the suitable CNN model structure and elaborate on the effectiveness of CNN methods compared with non-learning methods, we select five different CNN models, while R–L and directional MB methods are also applied for comparison. Besides simulated data, experimental data including tungsten wires, leaf veins, and in vivo blood vessels are also evaluated. A custom-defined metric of relative size error (RSE) is used to quantify the multiscale feature recovery ability of different methods. Compared to other methods, enhanced deep super resolution (EDSR) network and residual in residual dense block network (RRDBNet) model show better recovery in terms of RSE for tungsten wires with diameters ranging from 30 μm to 120 μm. Moreover, AR-PAM images of leaf veins are tested to demonstrate the effectiveness of the optimized CNN methods (by EDSR and RRDBNet) for complex patterns. Finally, in vivo images of mouse ear blood vessels and rat ear blood vessels are acquired and then deconvolved, and the results show that the proposed CNN method (notably RRDBNet) enables accurate deconvolution of multiscale feature size and thus good fidelity.

Accuracy of peak-power compensation in fiber-guided and free-space acoustic-resolution photoacoustic microscopy.

Acoustic resolution photoacoustic microscopy (AR-PAM) has gained much attention in the past two decades due to its high contrast, scalable resolution, and relatively higher imaging depth. Multimode optical fibers (MMF) are extensively used to transfer light to AR-PAM imaging scan-head from the laser source. Typically, peak-power-compensation (PPC) is used to reduce the effect of pulse-to-pulse peak-power variation in generated photoacoustic (PA) signals. In MMF, the output intensity profile fluctuates due to the coherent nature of light and mode exchange caused by variations in the bending of the fibers during scanning. Therefore, using a photodiode (PD) to capture a portion of the total power of pulses as a measure of illuminated light on the sample may not be appropriate for accurate PPC. In this study, we have investigated the accuracy of PPC in fiber-guided and free-space AR-PAM systems. Experiments were conducted in the transparent and highly scattering medium. Based on obtained results for the MMF-based system, to apply PPC to the generated PA signals, tightly focused light confocal with the acoustic focus in a transparent medium must be used. In the clear medium and highly focused illumination, enhancement of about 45% was obtained in the homogeneity of an optically homogeneous sample image. In addition, it is shown that, as an alternative, free-space propagation of the laser pulses results in more accurate PPC in both transparent and highly scattering mediums. In free-space light transmission, enhancement of 25-75% was obtained in the homogeneity of the optically homogeneous sample image.

Image enhancement in acoustic-resolution photoacoustic microscopy enabled by a novel directional algorithm.

By considering the line pattern of acoustic-resolution photoacoustic microscopy (AR-PAM) vessel images, we develop modified algorithms for synthetic aperture focusing technique (SAFT) and deconvolution based on a directional approach to enhance images. The modified algorithms consist of Fourier accumulation SAFT (FA-SAFT) and directional model-based (D-MB) deconvolution. To evaluate the performance of our algorithms, we conduct a series of imaging experiments and apply our algorithms, and existing SAFT and deconvolution algorithms are also applied for side-by-side comparison. By imaging tungsten wire phantom, our algorithms enable full width at half maximum of 26 - 31 µm over depth of focus of 1.8 mm and minimum resolvable distance of 46 - 49 µm, besting existing SAFT and deconvolution algorithms. Imaging of leaf skeleton phantom and in vivo imaging of mouse blood vessels also prove that our algorithm is capable of providing high-resolution, high-signal-to-noise ratio, and good-fidelity results for complex structures and for in vivo applications, especially for the images with the line pattern. The proposed directional approach can not only be used in AR-PAM but also in other imaging modalities to deal with the line pattern, such as FA-SAFT for ultrasound imaging and D-MB deconvolution for optical coherence tomography angiography.

Photoacoustic imaging of 3D-printed vascular networks

Thrombosis in the circulation system can lead to major myocardial infarction and cardiovascular deaths. Understanding thrombosis formation is necessary for developing safe and effective treatments. In this work, using digital light processing (DLP)-based 3D printing, we fabricated sophisticated in vitro models of blood vessels with internal microchannels that can be used for thrombosis studies. In this regard, photoacoustic microscopy (PAM) offers a unique advantage for label-free visualization of the 3D-printed vessel models, with large penetration depth and functional sensitivity. We compared the imaging performances of two PAM implementations: optical-resolution PAM and acoustic-resolution PAM, and investigated 3D-printed vessel structures with different patterns of microchannels. Our results show that PAM can provide clear microchannel structures at depths up to 3.6 mm. We further quantified the blood oxygenation in the 3D-printed vascular models, showing that thrombi had lower oxygenation than the normal blood. We expect that PAM can find broad applications in 3D printing and bioprinting for in vitro studies of various vascular and other diseases.

Non-invasive and low-artifact in vivo brain imaging by using a scanning acoustic-photoacoustic dual mode microscopy

Photoacoustic imaging is a potential candidate for in vivo brain imaging, whereas, its imaging performance could be degraded by inhomogeneous multi-layered media, consisted of scalp and skull. In this work, we propose a low-artifact photoacoustic microscopy (LAPAM) scheme, which combines conventional acoustic-resolution photoacoustic microscopy with scanning acoustic microscopy to suppress the reflection artifacts induced by multi-layers. Based on similar propagation characteristics of photoacoustic signals and ultrasonic echoes, the ultrasonic echoes can be employed as the filters to suppress the reflection artifacts to obtain low-artifact photoacoustic images. Phantom experiment is used to validate the effectiveness of this method. Furthermore, LAPAM is applied for in-vivo imaging mouse brain without removing the scalp and the skull. Experimental results show that the proposed method successfully achieves the low-artifact brain image, which demonstrates the practical applicability of LAPAM. This work might improve the photoacoustic imaging quality in many biomedical applications which involve tissues with complex acoustic properties, such as brain imaging through scalp and skull.

Ultra-compact micro-photoacoustic tomography for brain imaging in vivo

In vivo wide-field brain microscopy has remained a non-trivial challenge in life science research, which would provide indispensable preclinical insight into the whole-brain metabolism and hemodynamic. Here, we developed an ultra-compact micro-photoacoustic tomography (μPAT) system for fast brain imaging equipped with a 10 MHz 256-element full-ring ultrasound transducer array and 100 MS/s/ch multi-channel parallel data acquisition. The system design and calibration are presented in detail, along with the 62 μm in-plane resolution comparable to the level of acoustic-resolution photoacoustic microscopy. The μPAT system provided the ability to noninvasively use high-resolution mapping of the microvascular network of the full-view brain at different depths, cerebral ischemia, and intracerebral foreign-body. Ultra-compact μPAT holds great potential to take the technology out of the lab for preclinical imaging and clinical translation.

High-resolution photoacoustic microscopy with deep penetration through learning

Optical-resolution photoacoustic microscopy (OR-PAM) enjoys superior spatial resolution and has received intense attention in recent years. The application, however, has been limited to shallow depths because of strong scattering of light in biological tissues. In this work, we propose to achieve deep-penetrating OR-PAM performance by using deep learning enabled image transformation on blurry living mouse vascular images that were acquired with an acoustic-resolution photoacoustic microscopy (AR-PAM) setup. A generative adversarial network (GAN) was trained in this study and improved the imaging lateral resolution of AR-PAM from 54.0 µm to 5.1 µm, comparable to that of a typical OR-PAM (4.7 µm). The feasibility of the network was evaluated with living mouse ear data, producing superior microvasculature images that outperforms blind deconvolution. The generalization of the network was validated with in vivo mouse brain data. Moreover, it was shown experimentally that the deep-learning method can retain high resolution at tissue depths beyond one optical transport mean free path. Whilst it can be further improved, the proposed method provides new horizons to expand the scope of OR-PAM towards deep-tissue imaging and wide applications in biomedicine.

Acoustic-resolution photoacoustic microscopy based on an optically transparent focused transducer with a high numerical aperture: erratum.

This erratum corrects a few numbers in Table 1 of the original paper, Opt. Lett.46, 3280 (2021).OPLEDP0146-959210.1364/OL.423287.

Acoustic-resolution photoacoustic microscopy based on an optically transparent focused transducer with a high numerical aperture.

This Letter reports acoustic-resolution-photoacoustic microscopy (AR-PAM) based on a new optically transparent focused polyvinylidene fluoride (PVDF) transducer with a high acoustic numerical aperture (NA) of 0.64. Owing to the improved fabrication process, the new transducer has a much higher NA (0.64) than the previously reported low-NA transducer (NA=0.23). The acoustic center frequency and (pulse-echo) bandwidth are also increased to 36 and 44 MHz, respectively, which provides a 38 µm acoustic focal spot size and 210 µm acoustic depth of focus. For demonstration, AR-PAM was conducted on a twisted wire target in water and chicken breast tissue, and in vivo on a mouse tail. The imaging results show that high acoustic resolution and sensitivity can be achieved with a simple and compact setup to resolve the target at different depths. Such capabilities can be useful for the development of new AR-PAM systems for handheld, wearable, and even endoscopic imaging applications.

Axial accuracy and signal enhancement in acoustic-resolution photoacoustic microscopy by laser jitter effect correction and pulse energy compensation.

In recent years, photoacoustic imaging has found vast applications in biomedical imaging. Photoacoustic imaging has high optical contrast and high ultrasound resolution allowing deep tissue non-invasive imaging beyond the optical diffusion limit. Q-switched lasers are extensively used in photoacoustic imaging due to the availability of high energy and short laser pulses, which are essential for high-resolution photoacoustic imaging. In most cases, this type of light source suffers from pulse peak-power energy variations and timing jitter noise, resulting in uncertainty in the output power and arrival time of the laser pulses. These problems cause intensity degradation and temporal displacement of generated photoacoustic signals which in turn deteriorate the quality of the acquired photoacoustic images. In this study, we used a high-speed data acquisition system in combination with a fast photodetector and a software-based approach to capture laser pulses precisely in order to reduce the effect of timing jitter and normalization of the photoacoustic signals based on pulse peak-powers simultaneously. In the experiments, maximum axial accuracy enhancement of 14 µm was achieved in maximum-amplitude projected images on XZ and YZ planes with ±13.5 ns laser timing jitter. Furthermore, photoacoustic signal enhancement of 77% was obtained for 75% laser pulses peak-power stability.

Three-dimensional synthetic aperture focusing photoacoustic microscopy based on the acoustic simulation generated delay time and weighted factor.

Acoustic resolution photoacoustic microscopy (ARPAM) is a promising imaging tool in biomedical applications for its advantage of penetration over other optical imaging techniques. However, the lateral resolution of ARPAM deteriorates significantly in the out-of-focus region. The synthetic aperture focusing technique (SAFT) is required to restore this kind of focus-related imaging distortion. The conventional SAFT method is based on the virtual detector (VD) conception, in which the phase of the received photoacoustic (PA) signal is calculated by assuming the focus of the transducer as a VD. Nevertheless, the phase of the received PA signal is not only determined by the geometrical parameters of the transducer, but also by the transducer's electromechanic response and the original PA signal. Ignoring these two factors will reduce the quality of the imaging results. In this work, a new SAFT method, which is based on acoustic simulation, is proposed for ARPAM. The measured PA signal from a point target at the focus is employed to evaluate the convolution of the transducer's electromechanic response and the original PA signal. This measured signal is used as the excitation in an acoustic simulation. The simulation, which is based on the geometrical and acoustic parameters of the transducer, is employed to calculate the delay time and weighted coefficient for the SAFT calculation. The phantom experiments with point and line targets indicate that the proposed method obtains imaging results with better lateral resolution and improved signal-noise ratio compared with the widely used VD-based SAFT method.

Convolutional neural network for resolution enhancement and noise reduction in acoustic resolution photoacoustic microscopy.

In acoustic resolution photoacoustic microscopy (AR-PAM), a high numerical aperture focused ultrasound transducer (UST) is used for deep tissue high resolution photoacoustic imaging. There is a significant degradation of lateral resolution in the out-of-focus region. Improvement in out-of-focus resolution without degrading the image quality remains a challenge. In this work, we propose a deep learning-based method to improve the resolution of AR-PAM images, especially at the out of focus plane. A modified fully dense U-Net based architecture was trained on simulated AR-PAM images. Applying the trained model on experimental images showed that the variation in resolution is ∼10% across the entire imaging depth (∼4 mm) in the deep learning-based method, compared to ∼180% variation in the original PAM images. Performance of the trained network on in vivo rat vasculature imaging further validated that noise-free, high resolution images can be obtained using this method.

Study for quantitative evaluation of photoaging with photoacoustic microscopy.

Skin aging caused by ultraviolet light exposure is one of the serious problems from the viewpoint of beauty and healthcare. This is because ultraviolet light can cause age spot, wrinkles, at the worst case, skin cancer and so on. To evaluate skin aging, various modalities are being used, such as histopathological diagnosis, optical coherence tomography, ultrasound examination (B-mode imaging). However, they have disadvantages in terms of invasiveness, penetration depth and tissue specificity, respectively. To overcome these defects, photoacoustic imaging (PAI), a novel modality was used in this work. This modality can sense differences of tissue characteristics non-invasively. In this experiment, human skin tissues in various generations (i.e. various degrees of photoaging) were measured by using acoustic resolution photoacoustic microscopy (AR-PAM). To verify the feasibility of quantitative skin aging evaluation with PA technique, signals from sectioned human skin (cheek and buttock; female from 28 to 95 years old) were measured with PA microscopy. The effects of photoaging progress on the signal intensity were investigated. The results demonstrated that the PA signal from the dermis significantly increases with aging progress (p < 0.05). These analyses demonstrate the feasibility of quantitative skin aging evaluation with a PAI system.

Frequency Domain Based Virtual Detector for Heterogeneous Media in Photoacoustic Imaging

There has been a growing attention to using acoustic-resolution photoacoustic microscopy (ARPAM) for medical applications in the recent decade. To eliminate the distortion induced by acoustic diffraction in ARPAM, synthetic aperture focusing technique (SAFT) methods that incorporate the virtual detector (VD) concept are used to reconstruct the images. However, most of these algorithms assume homogeneous media and therefore perform poorly in heterogeneous media cases. On this note, we propose an ARPAM reconstruction technique that can be adapted to cases where layered heterogeneous media are present. This method rebuilds a VD-based receiving system by using virtual scan plane theory. It then extrapolates wave fields with the proposed phase-shift factors of different multi-layered media. Afterwards, it adopts a non-uniform fast Fourier transform to reconstruct images. The wave extrapolation eliminates the artifacts induced by refraction, simplifying the computation for layered media. According to the simulations, our proposed method is capable to reconstruct high-quality images for layered heterogeneous media.