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

Surgical Hyperspectral imaging and Indocyanine green Near-infrared Examination (SHINE) for brain arteriovenous malformation resection: a case report on how to visualize perfusion

Background and importance

Arteriovenous malformations (AVMs) are complex vascular anomalies that pose significant risks, including intracranial hemorrhage and neurological deficits. Surgical resection is the preferred treatment, requiring precise intraoperative imaging to ensure complete removal while preserving critical structures. This case report presents the first combined use of hyperspectral imaging (HSI) and indocyanine green video angiography (ICG VA) to visualize perfusion during brain AVM surgery, highlighting the potential benefits of these advanced imaging techniques.

Case description

A 66-year-old male presented with chronic headaches but no neurological deficits. MRI revealed a superficial AVM in the left frontal lobe within the superior frontal sulcus, measuring approximately 2.4 cm. The AVM was fed by feeders from the pericallosal artery, callosomarginal artery, and middle cerebral artery (MCA) branches, with drainage through a dilated cortical vein into the superior sagittal sinus. Preoperative embolization of two MCA feeding branches was performed, followed by microsurgical resection with ICG VA and HSI.

Conclusions

This case report demonstrates the successful application of HSI and ICG VA in brain AVM surgery. The combined use of these technologies provided comprehensive intraoperative assessment, enhancing surgical precision and safety. The integration of HSI offers non-invasive, contrast-agent-free imaging, potentially improving outcomes by enabling detailed perfusion mapping. Future studies should explore the broader applications of these imaging modalities in neurovascular practice.

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.

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.

Full-view LED-based optoacoustic tomography

Optoacoustic tomography is commonly performed with bulky and expensive short-pulsed solid-state lasers providing high per-pulse energies in the millijoule range. Light emitting diodes (LEDs) represent a cost-effective and portable alternative for optoacoustic signal excitation that can additionally provide excellent pulse-to-pulse stability. Herein, we introduce a full-view LED-based optoacoustic tomography (FLOAT) system for deep tissue in vivo imaging. It is based on a custom-made electronic unit driving a stacked array of LEDs, which attains 100 ns pulse width and highly stable (0.62 % standard deviation) total per-pulse energy of 0.48 mJ. The illumination source is integrated into a circular array of cylindrically-focused ultrasound detection elements to result in a full-view tomographic configuration, which plays a critical role in circumventing limited-view effects, enhancing the effective field-of-view and image quality for cross-sectional (2D) imaging. We characterized the FLOAT performance in terms of pulse width, power stability, excitation light distribution, signal-to-noise and penetration depth. FLOAT of the human finger revealed a comparable imaging performance to that achieved with the standard pulsed Nd:YAG laser. It is anticipated that this compact, affordable and versatile illumination technology will facilitate optoacoustic imaging developments in resource-limited settings for biological and clinical applications.

Looking deep inside tissue with photoacoustic molecular probes: a review

Significance

Deep tissue noninvasive high-resolution imaging with light is challenging due to the high degree of light absorption and scattering in biological tissue. Photoacoustic imaging (PAI) can overcome some of the challenges of pure optical or ultrasound imaging to provide high-resolution deep tissue imaging. However, label-free PAI signals from light absorbing chromophores within the tissue are nonspecific. The use of exogeneous contrast agents (probes) not only enhances the imaging contrast (and imaging depth) but also increases the specificity of PAI by binding only to targeted molecules and often providing signals distinct from the background.

Aim

We aim to review the current development and future progression of photoacoustic molecular probes/contrast agents.

Approach

First, PAI and the need for using contrast agents are briefly introduced. Then, the recent development of contrast agents in terms of materials used to construct them is discussed. Then, various probes are discussed based on targeting mechanisms, in vivo molecular imaging applications, multimodal uses, and use in theranostic applications.

Results

Material combinations are being used to develop highly specific contrast agents. In addition to passive accumulation, probes utilizing activation mechanisms show promise for greater controllability. Several probes also enable concurrent multimodal use with fluorescence, ultrasound, Raman, magnetic resonance imaging, and computed tomography. Finally, targeted probes are also shown to aid localized and molecularly specific photo-induced therapy.

Conclusions

The development of contrast agents provides a promising prospect for increased contrast, higher imaging depth, and molecularly specific information. Of note are agents that allow for controlled activation, explore other optical windows, and enable multimodal use to overcome some of the shortcomings of label-free PAI.

High frame rate (∼3 Hz) circular photoacoustic tomography using single-element ultrasound transducer aided with deep learning

Significance

In circular scanning photoacoustic tomography (PAT), it takes several minutes to generate an image of acceptable quality, especially with a single-element ultrasound transducer (UST). The imaging speed can be enhanced by faster scanning (with high repetition rate light sources) and using multiple-USTs. However, artifacts arising from the sparse signal acquisition and low signal-to-noise ratio at higher scanning speeds limit the imaging speed. Thus, there is a need to improve the imaging speed of the PAT systems without hampering the quality of the PAT image.

Aim

To improve the frame rate (or imaging speed) of the PAT system by using deep learning (DL).

Approach

For improving the frame rate (or imaging speed) of the PAT system, we propose a novel U-Net-based DL framework to reconstruct PAT images from fast scanning data.

Results

The efficiency of the network was evaluated on both single- and multiple-UST-based PAT systems. Both phantom and in vivo imaging demonstrate that the network can improve the imaging frame rate by approximately sixfold in single-UST-based PAT systems and by approximately twofold in multi-UST-based PAT systems.

Conclusions

We proposed an innovative method to improve the frame rate (or imaging speed) by using DL and with this method, the fastest frame rate of ∼ 3    Hz imaging is achieved without hampering the quality of the reconstructed image.

Photoacoustic assessment of the fetal brain and placenta as a method of non-invasive antepartum and intrapartum monitoring

A noninvasive monitor for concurrent evaluation of placental and fetal sagittal sinus sO 2 for both antepartum surveillance at the late 2nd and 3rd trimesters and intrapartum monitoring would be a great advantage over current methods. A PA fetal brain and placental monitor has potential value to rapidly identify the fetus at risk for developing hypoxia and ischemia of a sufficient degree that brain injury or death may develop, which may be prevented by intervention with delivery and other follow-up treatments.

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.

Deep learning enabled real-time photoacoustic tomography system via single data acquisition channel

Photoacoustic computed tomography (PACT) combines the optical contrast of optical imaging and the penetrability of sonography. In this work, we develop a novel PACT system to provide real-time imaging, which is achieved by a 120-elements ultrasound array only using a single data acquisition (DAQ) channel. To reduce the channel number of DAQ, we superimpose 30 nearby channels' signals together in the analog domain, and shrinking to 4 channels of data (120/30 = 4). Furthermore, a four-to-one delay-line module is designed to combine these four channels' data into one channel before entering the single-channel DAQ, followed by decoupling the signals after data acquisition. To reconstruct the image from four superimposed 30-channels' PA signals, we train a dedicated deep learning model to reconstruct the final PA image. In this paper, we present the preliminary results of phantom and in-vivo experiments, which manifests its robust real-time imaging performance. The significance of this novel PACT system is that it dramatically reduces the cost of multi-channel DAQ module (from 120 channels to 1 channel), paving the way to a portable, low-cost and real-time PACT system.

Extending Imaging Depth in PLD-Based Photoacoustic Imaging: Moving Beyond Averaging

Pulsed laser diodes (PLDs) promise to be an attractive alternative to solid-state laser sources in photoacoustic tomography (PAT) due to their portability, high-pulse repetition frequency (PRF), and cost effectiveness. However, due to their lower energy per pulse, which, in turn, results in lower fluence required per photoacoustic signal generation, PLD-based photoacoustic systems generally have maximum imaging depth that is lower in comparison to solid-state lasers. Averaging of multiple frames is usually employed as a common practice in high PRF PLD systems to improve the signal-to-noise ratio of the PAT images. In this work, we demonstrate that by combining the recently described approach of subpitch translation on the receive-side ultrasound transducer alongside averaging of multiple frames, it is feasible to increase the depth sensitivity in a PLD-based PAT imaging system. Here, experiments on phantom containing diluted India ink targets were performed at two different laser energy level settings, that is, 21 and [Formula: see text]. Results obtained showed that the imaging depth improves by ~38.5% from 9.1 to 12.6 mm for 21- [Formula: see text] energy level setting and by ~33.3% from 10.8 to 14.4 mm for 27- [Formula: see text] energy level setting by using λ /4-pitch translation and average of 128 frames in comparison to λ -pitch data acquired with the average of 128 frames. However, the achievable frame rate is reduced by a factor of 2 and 4 for λ /2 and λ /4 subpitch translation, respectively.

Another decade of photoacoustic imaging

Photoacoustic imaging - a hybrid biomedical imaging modality finding its way to clinical practices. Although the photoacoustic phenomenon was known more than a century back, only in the last two decades it has been widely researched and used for biomedical imaging applications. In this review we focus on the development and progress of the technology in the last decade (2010-2020). From becoming more and more user friendly, cheaper in cost, portable in size, photoacoustic imaging promises a wide range of applications, if translated to clinic. The growth of photoacoustic community is steady, and with several new directions researchers are exploring, it is inevitable that photoacoustic imaging will one day establish itself as a regular imaging system in the clinical practices.

Photoacoustic imaging with fiber optic technology: A review

Photoacoustic imaging (PAI) has achieved remarkable growth in the past few decades since it takes advantage of both optical and ultrasound (US) imaging. In order to better promote the wide clinical applications of PAI, many miniaturized and portable PAI systems have recently been proposed. Most of these systems utilize fiber optic technologies. Here, we overview the fiber optic technologies used in PAI. This paper discusses three different fiber optic technologies: fiber optic light transmission, fiber optic US transmission, and fiber optic US detection. These fiber optic technologies are analyzed in different PAI modalities including photoacoustic microscopy (PAM), photoacoustic computed tomography (PACT), and minimally invasive photoacoustic imaging (MIPAI).

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.

Portable and Affordable Light Source-Based Photoacoustic Tomography

Photoacoustic imaging is a hybrid imaging modality that offers the advantages of optical (spectroscopic contrast) and ultrasound imaging (scalable spatial resolution and imaging depth). This promising modality has shown excellent potential in a wide range of preclinical and clinical imaging and sensing applications. Even though photoacoustic imaging technology has matured in research settings, its clinical translation is not happening at the expected pace. One of the main reasons for this is the requirement of bulky and expensive pulsed lasers for excitation. To accelerate the clinical translation of photoacoustic imaging and explore its potential in resource-limited settings, it is of paramount importance to develop portable and affordable light sources that can be used as the excitation light source. In this review, we focus on the following aspects: (1) the basic theory of photoacoustic imaging; (2) inexpensive light sources and different implementations; and (3) important preclinical and clinical applications, demonstrated using affordable light source-based photoacoustics. The main focus will be on laser diodes and light-emitting diodes as they have demonstrated promise in photoacoustic tomography-the key technological developments in these areas will be thoroughly reviewed. We believe that this review will be a useful opus for both the beginners and experts in the field of biomedical photoacoustic imaging.

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.

Towards Clinical Translation of LED-Based Photoacoustic Imaging: A Review

Photoacoustic imaging, with the capability to provide simultaneous structural, functional, and molecular information, is one of the fastest growing biomedical imaging modalities of recent times. As a hybrid modality, it not only provides greater penetration depth than the purely optical imaging techniques, but also provides optical contrast of molecular components in the living tissue. Conventionally, photoacoustic imaging systems utilize bulky and expensive class IV lasers, which is one of the key factors hindering the clinical translation of this promising modality. Use of LEDs which are portable and affordable offers a unique opportunity to accelerate the clinical translation of photoacoustics. In this paper, we first review the development history of LED as an illumination source in biomedical photoacoustic imaging. Key developments in this area, from point-source measurements to development of high-power LED arrays, are briefly discussed. Finally, we thoroughly review multiple phantom, ex-vivo, animal in-vivo, human in-vivo, and clinical pilot studies and demonstrate the unprecedented preclinical and clinical potential of LED-based photoacoustic imaging.

Enhancement of photoacoustic tomography of acoustically inhomogeneous tissue by utilizing a memory effect

One of the major challenges for photoacoustic tomography is the variance of the speed of sound (SOS) in realistic tissue, which could lead to defocusing in image reconstruction and degrade the reconstructed image. In this study, we propose a method to optimize the SOS used for image reconstruction based on a memory effect of photoacoustic signal. We reveal that the photoacoustic signals received by two adjacent transducers have a high degree of similarity in waveform, while a time delay exists between them. The time delay is related to the SOS. Based on this physical phenomenon, an iterative operation is implemented to estimate the SOS used for image reconstruction. Both simulations and experiments confirm that the method significantly enhances the reconstructed image in inhomogeneous tissue. This study may have potential value in improving the performance of photoacoustic tomography in biomedical applications.

High resolution, label-free photoacoustic imaging of live chicken embryo developing in bioengineered eggshell

Chicken embryos have been proven to be an attractive vertebrate model for biomedical research. They have helped in making significant contributions for advancements in various fields like developmental biology, cancer research and cardiovascular studies. However, a non-invasive, label-free method of imaging live chicken embryo at high resolution still needs to be developed and optimized. In this work, we have shown the potential of photoacoustic tomography (PAT) for imaging live chicken embryos cultured in bioengineered eggshells. Laser pulses at wavelengths of 532 and 740 nm were used for attaining cross-sectional images of chicken embryos at different developmental stages. Cross-sections along different depths were imaged to gain knowledge of the relative depth of different vessels and organs. Due to high optical absorption of vasculature and embryonic eye, images with good optical contrast could be acquired using this method. We have thus reported a label-free method of performing cross-sectional imaging of chicken embryos at high resolution demonstrating the capacity of PAT as a promising tool for avian embryo imaging.

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.

Light-Emitting-Diode-Based Multispectral Photoacoustic Computed Tomography System

Photoacoustic computed tomography (PACT) has been widely explored for non-ionizing functional and molecular imaging of humans and small animals. In order for light to penetrate deep inside tissue, a bulky and high-cost tunable laser is typically used. Light-emitting diodes (LEDs) have recently emerged as cost-effective and portable alternative illumination sources for photoacoustic imaging. In this study, we have developed a portable, low-cost, five-dimensional (x, y, z, t, λ ) PACT system using multi-wavelength LED excitation to enable similar functional and molecular imaging capabilities as standard tunable lasers. Four LED arrays and a linear ultrasound transducer detector array are housed in a hollow cylindrical geometry that rotates 360 degrees to allow multiple projections through the subject of interest placed inside the cylinder. The structural, functional, and molecular imaging capabilities of the LED-PACT system are validated using various tissue-mimicking phantom studies. The axial, lateral, and elevational resolutions of the system at 2.3 cm depth are estimated as 0.12 mm, 0.3 mm, and 2.1 mm, respectively. Spectrally unmixed photoacoustic contrasts from tubes filled with oxy- and deoxy-hemoglobin, indocyanine green, methylene blue, and melanin molecules demonstrate the multispectral molecular imaging capabilities of the system. Human-finger-mimicking phantoms made of a bone and blood tubes show structural and functional oxygen saturation imaging capabilities. Together, these results demonstrate the potential of the proposed LED-based, low-cost, portable PACT system for pre-clinical and clinical applications.

Photoacoustic imaging with low-cost sources; A review

Benefitting from advantages of optical and ultrasound imaging, photoacoustic imaging (PAI) has demonstrated potentials in a wide range of medical applications. In order to facilitate clinical applications of PAI and encourage its application in low-resource settings, research on low-cost photoacoustic imaging with inexpensive optical sources has gained attention. Here, we review the advances made in photoacoustic imaging with low-cost sources.

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.

Transducer-matched multipulse excitation for signal-to-noise ratio improvement in diode laser-based photoacoustic systems

We analyze transducer-matched multipulse excitation as a method for improving of the signal-to-noise ratio (SNR) for diode laser-based photoacoustic systems. We discuss the principle of the technique, its advantages, and potential drawbacks and perform measurements to analyze the obtainable SNR increase. We show in experiment and computationally that a lower boundary estimate of 1.2 to 1.8 fold SNR improvement can be provided using transducer-matched pulse bursts, depending on the transducer and particular arrangement. Finally, we analyze implications that the transducer resonance effects may have on the recently introduced advanced photoacoustic techniques. The findings are of immediate interest to modalities utilizing dense pulse sequences and systems possessing limited pulse energy. In particular, transducer-matched multipulse excitation may be beneficial for diode-based photoacoustic systems operated with transducers in the range of 1 to 5 MHz since the required hardware is readily available.

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.

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

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

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.