Experimental validation of tangential resolution improvement in photoacoustic tomography using modified delay-and-sum reconstruction algorithm

Ex vivo human teeth imaging with various photoacoustic imaging systems

Dental caries cause pain and if not diagnosed, it may lead to the loss of teeth in extreme cases. Dental X-ray imaging is the gold standard for caries detection; however, it cannot detect hidden caries. In addition, the ionizing nature of X-ray radiation is another concern. Hence, other alternate imaging modalities like photoacoustic (PA) imaging are being explored for dental imaging. Here, we demonstrate the feasibility of acoustic resolution photoacoustic microscopy (ARPAM) to image a tooth with metal filling, circular photoacoustic computed tomography (cPACT) to acquire images of teeth with caries and pigmentation, and linear array-based photoacoustic imaging (lPACT) of teeth with caries and pigmentation. The cavity measured with lPACT imaging is compared with the X-ray computed tomography image. The metal filling and its boundaries are clearly seen in the ARPAM image. cPACT images at 1064 nm were a better representative of the tooth surface compared to the images acquired at 532 nm. It was possible to detect the cavities present in the dentine when lPACT imaging was used. The PA signal from the pigmented caries on the lateral surface (occlusion view) of the tooth was high when imaged using the lPACT system.

A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring

Quantitative and multiparametric blood analysis is of great clinical importance in cardiovascular disease diagnosis. Although there are various methods to extract blood information, they often require invasive procedures, lack continuity, involve bulky instruments, or have complicated testing procedures. Flexible sensors can realize on-skin assessment of several vital signals, but generally exhibit limited function to monitor blood characteristics. Here, we report a flexible optoacoustic blood 'stethoscope' for noninvasive, multiparametric, and continuous cardiovascular monitoring, without requiring complicated procedures. The optoacoustic blood 'stethoscope' features the light delivery elements to illuminate blood and the piezoelectric acoustic elements to capture light-induced acoustic waves. We show that the optoacoustic blood 'stethoscope' can adhere to the skin for continuous and non-invasive in-situ monitoring of multiple cardiovascular biomarkers, including hypoxia, intravascular exogenous agent concentration decay, and hemodynamics, which can be further visualized with a tailored 3D algorithm. Demonstrations on both in-vivo animal trials and human subjects highlight the optoacoustic blood 'stethoscope''s potential for cardiovascular disease diagnosis and prediction.

Multi-angle data acquisition to compensate transducer finite size in photoacoustic tomography

In photoacoustic tomography (PAT) systems, the tangential resolution decreases due to the finite size of the transducer as the off-center distance increases. To address this problem, we propose a multi-angle detection approach in which the transducer used for data acquisition rotates around its center (with specific angles) as well as around the scanning center. The angles are calculated based on the central frequency and diameter of the transducer and the radius of the region-of-interest (ROI). Simulations with point-like absorbers (for point-spread-function evaluation) and a vasculature phantom (for quality assessment), and experiments with ten 0.5 mm-diameter pencil leads and a leaf skeleton phantom are used for evaluation of the proposed approach. The results show that a location-independent tangential resolution is achieved with 150 spatial sampling and central rotations with angles of ±8°/±16°. With further developments, the proposed detection strategy can replace the conventional detection (rotating a transducer around ROI) in PAT.

Spherical-matching hyperbolic-array photoacoustic computed tomography

Linear-array photoacoustic computed tomography (LA-PACT), for its flexibility and simplicity, has great potential in providing anatomical and functional information of tissues. However, the limited coverage view impedes the LA-PACT obtaining high-quality images. In this study, a photoacoustic tomographic system with a hyperbolic-array transducer was developed for stereoscopic PA imaging of carotid artery. The hyperbolic-array PACT increases the receiving sensitivity for PA signal detection due to its transducer's geometric structure matching with the spherical wave. The control phantom experiment shows that the proposed system can expand the angular coverage of ∼1/3 more than that of the LA-PACT system, and the volumetric PA images of rat's carotid artery demonstrates the potential of the system for carotid artery imaging. Furthermore, volumetric imaging of the human forearm verifies that the system has significant capability in human imaging, which indicates that it has bright prospect for assisting diagnosis in the vascular disease.

Approximate back-projection method for improving lateral resolution in circular-scanning-based photoacoustic tomography

PURPOSE

In circular-scanning-based photoacoustic tomography (PAT), the effect of finite transducer aperture has not been effectively resolved. The goal of this paper is to propose a practical reconstruction method that accounts for the finite transducer aperture to improve the lateral resolution.

METHODS

We for the first time propose to calculate the spatial-temporal response (STR) of the employed finite-sized transducer in a forward model, and then compensate the time delay and the directional sensitivity of the transducer in the framework of the back-projection method. Both simulation and phantom experiments were carried out to evaluate the lateral resolution improvement with the proposed method. The performance of this new method for imaging complicated targets was also assessed by calculating the mean image gradient.

RESULTS

Simulation results showed that with this new method the lateral resolution for off-center targets can be as good as that for the center targets. Phantom experimental results showed that this new method can improve the lateral resolution more than two times for a point target about 5 mm far from the rotation center. Phantom experimental results also showed that many blurred fine structures of a piece of leaf veins at the off-center regions were well restored with the new method, and the mean image gradient improved about 1.3 times.

CONCLUSION

The proposed new method can effectively account for the effect of finite transducer aperture for circular-scanning-based PAT in homogenous acoustic media. This new method also features its robustness and computational efficiency, so that it is a worthy replacement to the conventional back-projection algorithm in circular-scanning-based PAT. This new method can be of great importance to the design of circular-scanning or spherical-scanning-based PAT systems.

Dimensionality reduced plug and play priors for improving photoacoustic tomographic imaging with limited noisy data

The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods.

Biomedical Photoacoustic Imaging With Unknown Spatially Distributed Ultrasound Sensor Array

OBJECTIVE

With the growth of interest in different medical study on biological function, non-invasive photoacoustic imaging of biological tissue attracts the interests for researchers. To eliminate the limited angle effect of photoacoustic imaging based on ultrasound linear array, spatially distributed ultrasound sensor array is applied. The accurate sensor array position determines the quality of the imaging results. In this study, we proposed three methods based on photoacoustic and ultrasound signals to enhance the imaging quality using a 256-element full-ring array.

METHODS

Groups of photoacoustic and ultrasound signals are used to regress the position of each element sensor.

RESULT

In phantom study and mouse brain study, photoacoustic imaging results can both yield details clearly with average error rate of less than 1% (50 [Formula: see text]).

CONCLUSION

The performance of our three methods have proved that they can be potentially applied to other ultrasound-based medical imaging studies with unknown distributed positions of sensor array to enhance the imaging quality.

SIGNIFICANCE

The proposed methods can contribute to precise biomedical imaging with unknown distributed positions of sensor array in different application scenarios.

Deep learning approach to improve tangential resolution in photoacoustic tomography

In circular scan photoacoustic tomography (PAT), the axial resolution is spatially invariant and is limited by the bandwidth of the detector. However, the tangential resolution is spatially variant and is dependent on the aperture size of the detector. In particular, the tangential resolution improves with the decreasing aperture size. However, using a detector with a smaller aperture reduces the sensitivity of the transducer. Thus, large aperture size detectors are widely preferred in circular scan PAT imaging systems. Although several techniques have been proposed to improve the tangential resolution, they have inherent limitations such as high cost and the need for customized detectors. Herein, we propose a novel deep learning architecture to counter the spatially variant tangential resolution in circular scanning PAT imaging systems. We used a fully dense U-Net based convolutional neural network architecture along with 9 residual blocks to improve the tangential resolution of the PAT images. The network was trained on the simulated datasets and its performance was verified by experimental in vivo imaging. Results show that the proposed deep learning network improves the tangential resolution by eight folds, without compromising the structural similarity and quality of image.

Deep Neural Network-Based Sinogram Super-Resolution and Bandwidth Enhancement for Limited-Data Photoacoustic Tomography

Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat.

In vivo detection of venous sinus distension due to intracranial hypotension in small animal using pulsed-laser-diode photoacoustic tomography

Intracranial hypotension (IH) is a pathophysiological condition of reduced intracranial pressure caused by low cerebrospinal fluid (CSF) volume due to dural injuries from lumbar puncture, surgery, or trauma. Understanding the prognosis of IH in small animal models is important to gain insights on the complications associated with it such as orthostatic headache, cerebral venous thrombosis, coma, and so forth. Photoacoustic tomography (PAT) offers a novel and cost-effective way to perceive and detect IH in small animal models. In this study, a pulsed laser diode (PLD)-based PAT imaging system was used to examine the changes in the venous sinuses of the rat brain due to IH, induced through CSF extraction. After the CSF extraction, an increase in the sagittal sinus area by ~30% and width by 40% ± 5% was observed. These results provide supportive evidence that the PLD-PAT can be employed for detecting changes in sagittal sinus due to IH in rat model.

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.

Photoacoustic imaging depth comparison at 532-, 800-, and 1064-nm wavelengths: Monte Carlo simulation and experimental validation

Photoacoustic imaging (PAI) provides high-resolution and high-optical-contrast imaging beyond optical diffusion limit. Further improvement in imaging depth has been achieved by using near-infrared window-I (NIR-I, 700 to 900 nm) for illumination, due to lower scattering and absorption by tissues in this wavelength range. Recently, near-infrared window-II (NIR-II, 900 to 1700 nm) has been explored for PAI. We studied the imaging depths in biological tissues for different illumination wavelengths in visible, NIR-I, and NIR-II regions using Monte Carlo (MC) simulations and validated with experimental results. MC simulations were done to compute fluence in tissue, absorbance in blood vessel, and in a spherical absorber (mimicking sentinel lymph node) embedded at different depths in breast tissue. Photoacoustic tomography and acoustic resolution photoacoustic microscopy experiments were conducted to validate the MC results. We demonstrate that maximum imaging depth is achieved by wavelengths in NIR-I window (∼800  nm) when the energy density deposited is same for all wavelengths. However, illumination using wavelengths around 1064 nm (NIR-II window) gives the maximum imaging depth when the energy density deposited is proportional to maximum permissible exposure (MPE) at corresponding wavelength. These results show that it is the higher MPE of NIR-II window that helps in increasing the PAI depth for chromophores embedded in breast tissue.

1064 nm acoustic resolution photoacoustic microscopy

Photoacoustic imaging is a noninvasive imaging technique having the advantages of high-optical contrast and good acoustic resolution at improved imaging depths. Light transport in biological tissues is mainly characterized by strong optical scattering and absorption. Photoacoustic microscopy is capable of achieving high-resolution images at greater depth compared to conventional optical microscopy methods. In this work, we have developed a high-resolution, acoustic resolution photoacoustic microscopy (AR-PAM) system in the near infra-red (NIR) window II (NIR-II, eg, 1064 nm) for deep tissue imaging. Higher imaging depth is achieved as the tissue scattering at 1064 nm is lesser compared to visible or near infrared window-I (NIR-I). Our developed system can provide a lateral resolution of 130 μm, axial resolution of 57 μm, and image up to 11 mm deep in biological tissues. This 1064-AR-PAM system was used for imaging sentinel lymph node and the lymph vessel in rat. Urinary bladder of rat filled with black ink was also imaged to validate the feasibility of the developed system to study deeply seated organs.

PA-Fuse: deep supervised approach for the fusion of photoacoustic images with distinct reconstruction characteristics

The methods available for solving the inverse problem of photoacoustic tomography promote only one feature-either being smooth or sharp-in the resultant image. The fusion of photoacoustic images reconstructed from distinct methods improves the individually reconstructed images, with the guided filter based approach being state-of-the-art, which requires that implicit regularization parameters are chosen. In this work, a deep fusion method based on convolutional neural networks has been proposed as an alternative to the guided filter based approach. It has the combined benefit of using less data for training without the need for the careful choice of any parameters and is a fully data-driven approach. The proposed deep fusion approach outperformed the contemporary fusion method, which was proved using experimental, numerical phantoms and in-vivo studies. The improvement obtained in the reconstructed images was as high as 95.49% in root mean square error and 7.77 dB in signal to noise ratio (SNR) in comparison to the guided filter approach. Also, it was demonstrated that the proposed deep fuse approach, trained on only blood vessel type images at measurement data SNR being 40 dB, was able to provide a generalization that can work across various noise levels in the measurement data, experimental set-ups as well as imaging objects.

Photoacoustic imaging in the second near-infrared window: a review

Photoacoustic (PA) imaging is an emerging medical imaging modality that combines optical excitation and ultrasound detection. Because ultrasound scatters much less than light in biological tissues, PA generates high-resolution images at centimeters depth. In recent years, wavelengths in the second near-infrared (NIR-II) window (1000 to 1700 nm) have been increasingly explored due to its potential for preclinical and clinical applications. In contrast to the conventional PA imaging in the visible (400 to 700 nm) and the first NIR-I (700 to 1000 nm) window, PA imaging in the NIR-II window offers numerous advantages, including high spatial resolution, deeper penetration depth, reduced optical absorption, and tissue scattering. Moreover, the second window allows a fivefold higher light excitation energy density compared to the visible window for enhancing the imaging depth significantly. We highlight the importance of the second window for PA imaging and discuss the various NIR-II PA imaging systems and contrast agents with strong absorption in the NIR-II spectral region. Numerous applications of NIR-II PA imaging, including whole-body animal imaging and human imaging, are also discussed.

Impact of sensor apodization on the tangential resolution in photoacoustic tomography

Photoacoustic tomographic (PAT) image reconstruction with apodized sensors is discussed. A Gaussian function was used to model axisymmetric apodization of sensors, and its full width at half-maximum (FWHM) was varied to investigate the role of apodization on the PAT image reconstruction. The well-known conventional delay-and-sum (CDAS) algorithm and recently developed modified delay-and-sum (MDAS) algorithm were implemented to generate reconstructed images. The performances of these algorithms were examined by comparing simulated images formed by these methods and that of ideal point detectors. Simulations in two dimensions were conducted using the k-Wave toolbox for three different phantoms. The results produced by the CDAS method are very close to that of ideal point detectors when the FWHM of the Gaussian function is small. The MDAS algorithm for flat sensors provides excellent results (comparable to that of point detectors) when the FWHM of the Gaussian profile is large. This study elucidates how sensor apodization affects PAT image reconstruction.

High-speed, low-cost, pulsed-laser-diode-based second-generation desktop photoacoustic tomography system

Bulky, expensive Nd:YAG lasers are used in conventional photoacoustic tomography (PAT) systems, making them difficult to translate into clinics. Moreover, real-time imaging is not feasible when a single-element ultrasound transducer is used with these low-pulse-repetition-rate lasers (10-100 Hz). Low-cost pulsed laser diodes (PLDs) can be used instead for photoacoustic imaging due to their high-pulse-repetition rates and compact size. Together with acoustic-reflector-based multiple single-element ultrasound transducers, a portable desktop PAT system was developed. This second-generation PLD-based PAT achieved 0.5 s cross-sectional imaging time with high spatial resolution of ∼165  μm and an imaging depth of 3 cm. The performance of this system was characterized using phantom and in vivo studies. Dynamic in vivo imaging was also demonstrated by monitoring the fast uptake and clearance of indocyanine green in small animal (rat) brain vasculature.

Contrast-enhanced photoacoustic imaging in the second near-infrared window using semiconducting polymer nanoparticles

Photoacoustic imaging (PAI) is a fast growing deep-tissue imaging modality. However, light scattering and absorption in biological tissues limit imaging depth. Short near-infrared wavelengths (650 to 950 nm) are widely used for PAI. Using longer near-infrared wavelengths reduces scattering. We demonstrate deep-tissue contrast-enhanced in vivo photoacoustic imaging at a wavelength of 1064 nm. An ultranarrow bandgap semiconducting polymer poly (thienoisoindigo-alt-diketopyrrolopyrrole) (denoted as PIGD) is designed and demonstrated for imaging at 1064 nm. By embedding colloidal nanoparticles (NPs) of PIGD in chicken-breast tissue, an imaging depth of ∼5  cm is achieved. Intravenous injection of PIGD NPs in living rats showed brain vascular images with ∼2 times higher contrast compared with the brain vascular images without any contrast agent. Thus, PIGD NPs as an NIR-II contrast agent opens new opportunities for both preclinical and clinical imaging of deep tissues with enhanced contrast.

Image-guided filtering for improving photoacoustic tomographic image reconstruction

Several algorithms exist to solve the photoacoustic image reconstruction problem depending on the expected reconstructed image features. These reconstruction algorithms promote typically one feature, such as being smooth or sharp, in the output image. Combining these features using a guided filtering approach was attempted in this work, which requires an input and guiding image. This approach act as a postprocessing step to improve commonly used Tikhonov or total variational regularization method. The result obtained from linear backprojection was used as a guiding image to improve these results. Using both numerical and experimental phantom cases, it was shown that the proposed guided filtering approach was able to improve (as high as 11.23 dB) the signal-to-noise ratio of the reconstructed images with the added advantage being computationally efficient. This approach was compared with state-of-the-art basis pursuit deconvolution as well as standard denoising methods and shown to outperform them.

Accelerated image reconstruction using extrapolated Tikhonov filtering for photoacoustic tomography

PURPOSE

Development of simple and computationally efficient extrapolated Tikhonov filtering reconstruction methods for photoacoustic tomography.

METHODS

The model-based reconstruction algorithms in photoacoustic tomography typically utilize Tikhonov regularization scheme for the reconstruction of initial pressure distribution from the measured boundary acoustic data. The automated choice of regularization parameter in these cases is computationally expensive. Moreover, the Tikhonov scheme promotes the smooth features in the reconstructed image due to the smooth regularizer, thus leading to loss of sharp features. The proposed extrapolation method estimates the solution at zero regularization assuming the solution being a function of regularization parameter and thus posing it as a zero value problem. Thus, the numerically computed zero regularization solution is expected to have better features compared to standard Tikhonov solution, with an added advantage of removing the necessity of automated choice of regularization. The reconstructed results using this method were shown in three variants (Lanczos, traditional, and exponential) of Tikhonov filtering and were compared with the standard error estimate technique.

RESULTS

Four numerical (including realistic breast phantom) and two experimental phantom data were utilized to show the effectiveness of the proposed method. It was shown that the proposed method performance was superior than the standard error estimate technique, being at least four times faster in terms of computation, and provides an improvement as high as 2.6 times in terms of standard figures of merit.

CONCLUSION

The developed extrapolated Tikhonov filtering methods overcome the difficulty of obtaining a suitable regularization parameter and shown to be reconstructing high-quality photoacoustic images with additional advantage of being computationally efficient, making it more appealing in real-time applications.

Fast photoacoustic imaging systems using pulsed laser diodes: a review

Photoacoustic imaging (PAI) is a newly emerging imaging modality for preclinical and clinical applications. The conventional PAI systems use Q-switched Nd:YAG/OPO (Optical Parametric Oscillator) nanosecond lasers as excitation sources. Such lasers are expensive, bulky, and imaging speed is limited because of low pulse repetition rate. In recent years, the semiconductor laser technology has advanced to generate high-repetitions rate near-infrared pulsed lasers diodes (PLDs) which are reliable, less-expensive, hand-held, and light-weight, about 200 g. In this article, we review the development and demonstration of PLD based PAI systems for preclinical and clinical applications reported in recent years.

Calibrating reconstruction radius in a multi single-element ultrasound-transducer-based photoacoustic computed tomography system

In a circular scanning photoacoustic computed tomography (PAT/PACT) system, a single-element ultrasound transducer (SUT) (rotates in full 360° around the sample) or a full-ring array transducer is used to acquire the photoacoustic (PA) data from the target object. SUT takes several minutes to acquire the PA data, whereas the full-ring array transducer takes only few seconds. Hence, for real-time imaging, full-ring circular array transducers are preferred. However, these are custom built, very expensive, and not available readily on the market, whereas SUTs are cheap and easily available. Thus, PACT systems can be made cost effective by using SUTs. To improve the data acquisition speed, multiple SUTs can be employed at the same time. This will reduce the acquisition time by N-fold if N numbers of SUTs are used, each rotating 360/N degrees. Experimentally, all SUTs cannot be placed exactly at the same distance from the scanning center. Hence, the acquired PA data from each transducer need to be reconstructed with their corresponding radii in a delay-and-sum reconstruction algorithm. This requires the exact location of each SUT from the scanning center. Here, we propose a calibration method to find out the distance from the scanning center at which each SUT acquires the PA data. Three numerical phantoms were used to show the efficacy of the proposed method, and later it was validated with experimental data (point source phantom).

Improved back-projection method for circular-scanning-based photoacoustic tomography with improved tangential resolution

While photoacoustic computed tomography (PACT) is generally built with planar transducers of finite size, most current reconstruction algorithms assume the transducer to be an ideal point, which leads to a spinning blur in the consequently obtained PACT images due to the model mismatch. In this work, we put forward an improved back-projection method that factors in the geometry of the transducers to improve the tangential resolution for the reconstruction of 2D circular-scanning-based photoacoustic tomography. Extensive simulations and experiments were carried out to study the adaptability and stability of the proposed method. Results show that this method can effectively restore the tangential distortion of the PACT image for both simulated and experimental data. Results indicated that the improvement of the tangential resolution is more obvious for transducers with larger size. We also demonstrated the application of this method to transducers other than planar, and results show that the reconstructed image quality can be significantly affected by the shape and position of the transducers used. This study may help to guide the selection of transducer and design of scanning strategy in PACT.

Deep neural network-based bandwidth enhancement of photoacoustic data

Photoacoustic (PA) signals collected at the boundary of tissue are always band-limited. A deep neural network was proposed to enhance the bandwidth (BW) of the detected PA signal, thereby improving the quantitative accuracy of the reconstructed PA images. A least square-based deconvolution method that utilizes the Tikhonov regularization framework was used for comparison with the proposed network. The proposed method was evaluated using both numerical and experimental data. The results indicate that the proposed method was capable of enhancing the BW of the detected PA signal, which inturn improves the contrast recovery and quality of reconstructed PA images without adding any significant computational burden.

Dynamic in vivo imaging of small animal brain using pulsed laser diode-based photoacoustic tomography system

We demonstrate dynamic in vivo imaging using a low-cost portable pulsed laser diode (PLD)-based photoacoustic tomography system. The system takes advantage of an 803-nm PLD having high-repetition rate ∼7000  Hz combined with a fast-scanning single-element ultrasound transducer leading to a 5 s cross-sectional imaging. Cortical vasculature is imaged in scan time of 5 s with high signal-to-noise ratio ∼48. To examine the ability for dynamic imaging, we monitored the fast uptake and clearance process of indocyanine green in the rat brain. The system will find applications to study neurofunctional activities, characterization of pharmacokinetic, and biodistribution profiles in the development process of drugs or imaging agents.

A High-performance Compact Photoacoustic Tomography System for In Vivo Small-animal Brain Imaging

In vivo small-animal imaging has an important role to play in preclinical studies. Photoacoustic tomography (PAT) is an emerging hybrid imaging modality that shows great potential for both preclinical and clinical applications. Conventional optical parametric oscillator-based PAT (OPO-PAT) systems are bulky and expensive and cannot provide high-speed imaging. Recently, pulsed-laser diodes (PLDs) have been successfully demonstrated as an alternative excitation source for PAT. Pulsed-laser diode PAT (PLD-PAT) has been successfully demonstrated for high-speed imaging on photoacoustic phantoms and biological tissues. This work provides a visualized experimental protocol for in vivo brain imaging using PLD-PAT. The protocol includes the compact PLD-PAT system configuration and its description, animal preparation for brain imaging, and a typical experimental procedure for 2D cross-sectional rat brain imaging. The PLD-PAT system is compact and cost-effective and can provide high-speed, high-quality imaging. Brain images collected in vivo at various scan speeds are presented.

Use of acoustic reflector to make a compact photoacoustic tomography system

A typical photoacoustic tomography (PAT) system uses a Q-switched Nd:YAG laser for irradiating the sample and a single-element ultrasound transducer (UST) for acquiring the photoacoustic data. Conventionally, in PAT systems, the UST is held in a horizontal position and moved in a circular motion around the sample in full 2 ? radians. Horizontal positioning of the UST requires a large water tank to house, and load on the motor is also high. To overcome this limitation, we used the UST in the vertical plane instead of the horizontal plane. The photoacoustic (PA) waves generated from the sample are directed to the detector surface using an acoustic reflector placed at 45 deg to the transducer body. Hence, we can reduce the scanning radius, which, in turn, will reduce the size of the water tank and load on the motor, and the overall conventional PAT system size can be minimized. In this work, we demonstrate that with this system configuration, we acquire nearly similar images for phantom and in vivo data as that of the conventional PAT system using both flat and focused USTs.