Deep-learning-based multi-transducer photoacoustic tomography imaging without radius calibration.

Pulsed laser diodes are used in photoacoustic tomography (PAT) as excitation sources because of their low cost, compact size, and high pulse repetition rate. In combination with multiple single-element ultrasound transducers (SUTs) the imaging speed of PAT can be improved. However, during PAT image reconstruction, the exact radius of each SUT is required for accurate reconstruction. Here we developed a novel deep learning approach to alleviate the need for radius calibration. We used a convolutional neural network (fully dense U-Net) aided with a convolutional long short-term memory block to reconstruct the PAT images. Our analysis on the test set demonstrates that the proposed network eliminates the need for radius calibration and improves the peak signal-to-noise ratio by ∼73% without compromising the image quality. In vivo imaging was used to verify the performance of the network.

Nanotechnology Facilitated Cultured Neuronal Network and Its Applications.

The development of a biomimetic neuronal network from neural cells is a big challenge for researchers. Recent advances in nanotechnology, on the other hand, have enabled unprecedented tools and techniques for guiding and directing neural stem cell proliferation and differentiation in vitro to construct an in vivo-like neuronal network. Nanotechnology allows control over neural stem cells by means of scaffolds that guide neurons to reform synaptic networks in suitable directions in 3D architecture, surface modification/nanopatterning to decide cell fate and stimulate/record signals from neurons to find out the relationships between neuronal circuit connectivity and their pathophysiological functions. Overall, nanotechnology-mediated methods facilitate precise physiochemical controls essential to develop tools appropriate for applications in neuroscience. This review emphasizes the newest applications of nanotechnology for examining central nervous system (CNS) roles and, therefore, provides an insight into how these technologies can be tested in vitro before being used in preclinical and clinical research and their potential role in regenerative medicine and tissue engineering.

Real-time monitoring of temperature using a pulsed laser-diode-based photoacoustic system.

Tissue local temperature information is necessary for guiding energy-based medical treatments. In cancer treatments such as thermal therapy, heating is applied to local tissue to kill the tumor cells. These techniques require a temperature monitoring device with high sensitivity. In this Letter, we demonstrate a pulsed-laser-diode-(PLD)-based photoacoustic temperature sensing (PATS) system for monitoring tissue temperature in real time. The system takes advantage of a high repetition rate (7000 Hz), a near-infrared wavelength (803 nm), and a relatively high energy 1.42 mJ/pulse laser. The system is capable of providing local temperature information at high temporal resolution of 1 ms and high sensitivity of 0.31°C. The temperature data measured with a PLD-PATS system are compared with the data provided by the commercial fiber Bragg grating sensor. The proposed system will find applications in radio-frequency ablation, photothermal therapy, and focused ultrasound, etc., used for cancer treatments.

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.

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.

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).

Photoacoustic imaging of lamina cribrosa microcapillaries in porcine eyes.

Due to the embedded nature of the lamina cribrosa (LC) microcapillary network, conventional imaging techniques have failed to obtain the high-resolution images needed to assess the perfusion state of the LC. In this study, both optical resolution (OR) and acoustic resolution (AR) photoacoustic microscopy (PAM) techniques were used to obtain static and dynamic information about LC perfusion in ex vivo porcine eyes. The OR-PAM system could resolve a perfused LC microcapillary network with a lateral resolution of 4.2 µm and also provided good depth information (33 µm axial resolution) to visualize through-thickness vascular variations. The AR-PAM system was capable of detecting time-dependent perfusion variations. This study represents the first step towards using an emerging imaging modality (PAM) to study the LC's perfusion, which could be a basis for further investigation of the hemodynamic aspects of glaucomatous optic neuropathy.

Broadband Absorbing Semiconducting Polymer Nanoparticles for Photoacoustic Imaging in Second Near-Infrared Window.

Photoacoustic (PA) imaging holds great promise for preclinical research and clinical practice. However, most studies rely on the laser wavelength in the first near-infrared (NIR) window (NIR-I, 650-950 nm), while few studies have been exploited in the second NIR window (NIR-II, 1000-1700 nm), mainly due to the lack of NIR-II absorbing contrast agents. We herein report the synthesis of a broadband absorbing PA contrast agent based on semiconducting polymer nanoparticles (SPN-II) and apply it for PA imaging in NIR-II window. SPN-II can absorb in both NIR-I and NIR-II regions, providing the feasibility to directly compare PA imaging at 750 nm with that at 1064 nm. Because of the weaker background PA signals from biological tissues in NIR-II window, the signal-to-noise ratio (SNR) of SPN-II resulted PA images at 1064 nm can be 1.4-times higher than that at 750 nm when comparing at the imaging depth of 3 cm. The proof-of-concept application of NIR-II PA imaging is demonstrated in in vivo imaging of brain vasculature in living rats, which showed 1.5-times higher SNR as compared with NIR-I PA imaging. Our study not only introduces the first broadband absorbing organic contrast agent that is applicable for PA imaging in both NIR-I and NIR-II windows but also reveals the advantages of NIR-II over NIR-I in PA imaging.

Application of phase shifting electronic speckle pattern interferometry in studies of photoinduced shrinkage of photopolymer layers.

Photoinduced shrinkage occurring in photopolymer layers during holographic recording was determined by phase shifting electronic speckle pattern interferometry. Phase maps were calculated from the changes in intensity at each pixel due to the phase differences introduced between object and reference beams. Shrinkage was then obtained from the changes in phase as recording proceeded. The technique allows for whole field measurement of the dimensional changes in photopolymers during holographic recording.

Carbazole-Linked Near-Infrared Aza-BODIPY Dyes as Triplet Sensitizers and Photoacoustic Contrast Agents for Deep-Tissue Imaging.

Four new N-ethylcarbazole-linked aza-boron-dipyrromethene (aza-BODIPY) dyes (8 a,b and 9 a,b) were synthesized and characterized. The presence of the N-ethylcarbazole moiety shifts their absorption and fluorescence spectra to the near-infrared region, λ≈650-730 nm, of the electromagnetic spectrum. These dyes possess strong molar absorptivity in the range of 3-4×104 m-1 cm-1 with low fluorescence quantum yields. The triplet excited state and singlet oxygen generation of these dyes were enhanced upon iodination at the core position. The core-iodinated dyes 9 a,b showed excellent triplet quantum yields of about 90 and 75 %, with singlet oxygen generation efficiency of about 70 and 60 % relative to that of the parent dyes. Derivatives 8 a,b showed dual absorption profiles, in contrast to dyes 9 a,b, which had the characteristic absorption band of aza-BODIPY dyes. DFT calculations revealed that the electron density was spread over the iodine and dipyrromethene plane of 9 a,b, whereas in 8 a,b the electron density was distributed on the carbazole group and dipyrromethene plane of aza-BODIPY. The uniqueness of these aza-BODIPY systems is that they exhibit efficient triplet-state quantum yields, high singlet oxygen generation yields, and good photostability. Furthermore, the photoacoustic (PA) characteristics of these aza-BODIPY dyes was explored, and efficient PA signals for 8 a were observed relative to blood serum with in vitro deep-tissue imaging, thereby confirming its use as a promising PA contrast agent.

Performance Characterization of a Switchable Acoustic Resolution and Optical Resolution Photoacoustic Microscopy System.

Photoacoustic microscopy (PAM) is a scalable bioimaging modality; one can choose low acoustic resolution with deep penetration depth or high optical resolution with shallow imaging depth. High spatial resolution and deep penetration depth is rather difficult to achieve using a single system. Here we report a switchable acoustic resolution and optical resolution photoacoustic microscopy (AR-OR-PAM) system in a single imaging system capable of both high resolution and low resolution on the same sample. Lateral resolution of 4.2 µm (with ~1.4 mm imaging depth) and lateral resolution of 45 µm (with ~7.6 mm imaging depth) was successfully demonstrated using a switchable system. In vivo blood vasculature imaging was also performed for its biological application.

Importance sampling-based Monte Carlo simulation of time-domain optical coherence tomography with embedded objects.

Monte Carlo simulation for light propagation in biological tissue is widely used to study light-tissue interaction. Simulation for optical coherence tomography (OCT) studies requires handling of embedded objects of various shapes. In this work, time-domain OCT simulations for multilayered tissue with embedded objects (such as sphere, cylinder, ellipsoid, and cuboid) was done. Improved importance sampling (IS) was implemented for the proposed OCT simulation for faster speed. At first, IS was validated against standard and angular biased Monte Carlo methods for OCT. Both class I and class II photons were in agreement in all the three methods. However, the IS method had more than tenfold improvement in terms of simulation time. Next, B-scan images were obtained for four types of embedded objects. All the four shapes are clearly visible from the B-scan OCT images. With the improved IS B-scan OCT images of embedded objects can be obtained with reasonable simulation time using a standard desktop computer. User-friendly, C-based, Monte Carlo simulation for tissue layers with embedded objects for OCT (MCEO-OCT) will be very useful for time-domain OCT simulations in many biological applications.

Measurement of large discontinuities using single white light interferogram.

White light interferometry is a widely used tool to extend the unambiguous measurement range of a monochromatic interferometer. In this work, we discuss Hilbert transformation analysis of a single white light interferogram acquired with a single-chip color CCD camera for step height measurement which lies beyond the unambiguous range of the monochromatic interferometry. The color interferogram is decomposed and phase maps for red, green, and blue components are calculated independently using Hilbert transformation. This procedure makes the measurement faster, simpler, and cost-effective. The usefulness of the technique is demonstrated on micro-sample.

Improving tangential resolution with a modified delay-and-sum reconstruction algorithm in photoacoustic and thermoacoustic tomography.

Spatial resolution in photoacoustic and thermoacoustic tomography is ultrasound transducer (detector) bandwidth limited. For a circular scanning geometry the axial (radial) resolution is not affected by the detector aperture, but the tangential (lateral) resolution is highly dependent on the aperture size, and it is also spatially varying (depending on the location relative to the scanning center). Several approaches have been reported to counter this problem by physically attaching a negative acoustic lens in front of the nonfocused transducer or by using virtual point detectors. Here, we have implemented a modified delay-and-sum reconstruction method, which takes into account the large aperture of the detector, leading to more than fivefold improvement in the tangential resolution in photoacoustic (and thermoacoustic) tomography. Three different types of numerical phantoms were used to validate our reconstruction method. It is also shown that we were able to preserve the shape of the reconstructed objects with the modified 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.

Deconvolution-based deblurring of reconstructed images in photoacoustic/thermoacoustic tomography.

Photoacoustic/thermoacoustic tomography is an emerging hybrid imaging modality combining optical/microwave imaging with ultrasound imaging. Here, a k-wave MATLAB toolbox was used to simulate various configurations of excitation pulse shape, width, transducer types, and target object sizes to see their effect on the photoacoustic/thermoacoustic signals. A numerical blood vessel phantom was also used to demonstrate the effect of various excitation pulse waveforms and pulse widths on the reconstructed images. Reconstructed images were blurred due to the broadening of the pressure waves by the excitation pulse width as well as by the limited transducer bandwidth. The blurring increases with increase in pulse width. A deconvolution approach is presented here with Tikhonov regularization to correct the photoacoustic/thermoacoustic signals, which resulted in improved reconstructed images by reducing the blurring effect. It is observed that the reconstructed images remain unaffected by change in pulse widths or pulse shapes, as well as by the limited bandwidth of the ultrasound detectors after the use of the deconvolution technique.

Physics-guided neural network for tissue optical properties estimation.

Finding the optical properties of tissue is essential for various biomedical diagnostic/therapeutic applications such as monitoring of blood oxygenation, tissue metabolism, skin imaging, photodynamic therapy, low-level laser therapy, and photo-thermal therapy. Hence, the research for more accurate and versatile optical properties estimation techniques has always been a primary interest of researchers, especially in the field of bioimaging and bio-optics. In the past, most of the prediction methods were based on physics-based models such as the pronounced diffusion approximation method. In more recent years, with the advancement and growing popularity of machine learning techniques, most of the prediction methods are data-driven. While both methods have been proven to be useful, each of them suffers from several shortcomings that could be complemented by their counterparts. Thus, there is a need to bring the two domains together to obtain superior prediction accuracy and generalizability. In this work, we proposed a physics-guided neural network (PGNN) for tissue optical properties regression which integrates physics prior and constraint into the artificial neural network (ANN) model. With this method, we have demonstrated superior generalizability of PGNN compared to its pure ANN counterpart. The prediction accuracy and generalizability of the network were evaluated on single-layered tissue samples simulated with Monte Carlo simulation. Two different test datasets, the in-domain test dataset and out-domain dataset were used to evaluate in-domain generalizability and out-domain generalizability, respectively. The physics-guided neural network (PGNN) showed superior generalizability for both in-domain and out-domain prediction compared to pure ANN.

Special Section Guest Editorial: Seeing Inside Tissue with Optical Molecular Probes.

The editorial introduces the Special Section on Seeing Inside Tissue with Optical Molecular Probes.

Android mobile-platform-based image reconstruction for photoacoustic tomography.

Significance: In photoacoustic tomography (PAT), numerous reconstruction algorithms have been utilized to recover initial pressure rise distribution from the acquired pressure waves. In practice, most of these reconstructions are carried out on a desktop/workstation and the mobile-based reconstructions are far-flung. In recent years, mobile phones are becoming so ubiquitous, and most of them encompass a higher computing ability. Hence, realizing PAT image reconstruction on a mobile platform is intrinsic, and it will enhance the adaptability of PAT systems with point-of-care applications. Aim: To implement PAT image reconstruction in Android-based mobile platforms. Approach: For implementing PAT image reconstruction in Android-based mobile platforms, we proposed an Android-based application using Python to perform beamforming process in Android phones. Results: The performance of the developed application was analyzed on different mobile platforms using both simulated and experimental datasets. The results demonstrate that the developed algorithm can accomplish the image reconstruction of in vivo small animal brain dataset in 2.4 s. Furthermore, the developed application reconstructs PAT images with comparable speed and no loss of image quality compared to that on a laptop. Employing a two-fold downsampling procedure could serve as a viable solution for reducing the time needed for beamforming while preserving image quality with minimal degradation. Conclusions: We proposed an Android-based application that achieves image reconstruction on cheap, small, and universally available phones instead of relatively bulky expensive desktop computers/laptops/workstations. A beamforming speed of 2.4 s is achieved without hampering the quality of the reconstructed image.

Ultrasound-guided needle tracking with deep learning: A novel approach with photoacoustic ground truth.

Accurate needle guidance is crucial for safe and effective clinical diagnosis and treatment procedures. Conventional ultrasound (US)-guided needle insertion often encounters challenges in consistency and precisely visualizing the needle, necessitating the development of reliable methods to track the needle. As a powerful tool in image processing, deep learning has shown promise for enhancing needle visibility in US images, although its dependence on manual annotation or simulated data as ground truth can lead to potential bias or difficulties in generalizing to real US images. Photoacoustic (PA) imaging has demonstrated its capability for high-contrast needle visualization. In this study, we explore the potential of PA imaging as a reliable ground truth for deep learning network training without the need for expert annotation. Our network (UIU-Net), trained on ex vivo tissue image datasets, has shown remarkable precision in localizing needles within US images. The evaluation of needle segmentation performance extends across previously unseen ex vivo data and in vivo human data (collected from an open-source data repository). Specifically, for human data, the Modified Hausdorff Distance (MHD) value stands at approximately 3.73, and the targeting error value is around 2.03, indicating the strong similarity and small needle orientation deviation between the predicted needle and actual needle location. A key advantage of our method is its applicability beyond US images captured from specific imaging systems, extending to images from other US imaging systems.