Multiview Hilbert transformation in full-ring transducer array-based photoacoustic computed tomography

On the Importance of Low-Frequency Signals in Functional and Molecular Photoacoustic Computed Tomography

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

On the importance of low-frequency signals in functional and molecular photoacoustic computed tomography

In photoacoustic computed tomography (PACT) with short-pulsed laser excitation, wideband acoustic signals are generated in biological tissues with frequencies related to the effective shapes and sizes of the optically absorbing targets. Low-frequency photoacoustic signal components correspond to slowly varying spatial features and are often omitted during imaging due to the limited detection bandwidth of the ultrasound transducer, or during image reconstruction as undesired background that degrades image contrast. Here we demonstrate that low-frequency photoacoustic signals, in fact, contain functional and molecular information, and can be used to enhance structural visibility, improve quantitative accuracy, and reduce spare-sampling artifacts. We provide an in-depth theoretical analysis of low-frequency signals in PACT, and experimentally evaluate their impact on several representative PACT applications, such as mapping temperature in photothermal treatment, measuring blood oxygenation in a hypoxia challenge, and detecting photoswitchable molecular probes in deep organs. Our results strongly suggest that low-frequency signals are important for functional and molecular PACT.

Sentinel lymph node mapping in patients with breast cancer using a photoacoustic/ultrasound dual-modality imaging system with carbon nanoparticles as the contrast agent: a pilot study

Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.

Research on the Multi-Element Synthetic Aperture Focusing Technique in Breast Ultrasound Imaging, Based on the Ring Array

As a widely clinical detection method, ultrasonography (US) has been applied to the diagnosis of breast cancer. In this paper, the multi-element synthetic aperture focusing (M-SAF) is applied to the ring array of breast ultrasonography (US) imaging, which addresses the problem of low imaging quality due to the single active element for each emission and the reception in the synthetic aperture focusing. In order to determine the optimal sub-aperture size, the formula is derived for calculating the internal sound pressure of the ring array with a 200 mm diameter, and the sound pressure distribution is analyzed. The ring array with 1024 elements (1024 ring array) is established in COMSOL Multiphysics 5.6, and the optimal sub-aperture size is 16 elements, according to the sound field beam simulation and the directivity research. Based on the existing experimental conditions, the ring array with 256 elements (256 ring array) is simulated and verified by experiments. The simulation has a spatial resolution evaluation in the k-Wave toolbox, and the experiment uses nylon rope and breast model imaging. The results show that if the sub-aperture size has four elements, the imaging quality is the highest. Specifically, the spatial resolution is the best, and the sound pressure amplitude and signal-to-noise ratio (SNR) are maintained at a high level in the reconstructed image. The optimal sub-aperture theory is verified by the two kinds of ring arrays, which also provide a theoretical basis for the application of the multi-element synthetic aperture focusing technology (M-SAF) in ring arrays.

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.

Flexible transparent CMUT arrays for photoacoustic tomography

This paper reports the fabrication and characterization of the first flexible transparent capacitive micromachined ultrasound transducer (CMUT) array for through-illumination photoacoustic tomography. Fabricated based on an adhesive wafer bonding technique and a PDMS backfill approach, the array has a maximum transparency of 67% in visible light range and can be bent to a radius of curvature of less than 5 mm without the structural layers being damaged. With a center frequency of 3.5 MHz, 80% fractional bandwidth, and noise equivalent pressure (NEP) of 62 mPa/H z, the array was successfully used in limited-view photoacoustic tomography of a 100 µm wire target, demonstrating lateral and axial resolutions of 293 µm and 382 µm, respectively, with 46 dB signal-to-noise ratio. Additionally, deep tissue photoacoustic tomography was also demonstrated on a blood tube within a chicken tissue using the fabricated CMUT arrays.

Video-Rate Dual-Modal Wide-Beam Harmonic Ultrasound and Photoacoustic Computed Tomography

Dual-modal ultrasound (US) and photoacoustic (PA) imaging has tremendous advantages in biomedical applications, such as pharmacokinetics, cancer screening, and imaging-guided therapy. Compared with ring-shaped arrays, a linear piezoelectric transducer array applies to more anatomical sites and has been widely used in US/PA imaging. However, the linear array may limit the imaging quality due to narrow bandwidth, partial detection view, or sparse spatial sampling. To meet clinic demand of high-quality US/PA imaging with the linear transducer, we develop dual-modal wide-beam harmonic ultrasound (WBHUS) and photoacoustic computed tomography at video rate. The harmonic US imaging employs pulse phase inversion to reduce clutters and improve spatial resolution. Wide-beam US transmission can shorten the scanning times by 267% and enables a 20-Hz imaging rate, which can minimize motion artifacts in in vivo imaging. The harmonic US imaging does not only provide accurate anatomical references for locating PA features but also reduces artifacts in PA images. The improved image quality allows us to acquire high-resolution anatomical structures in deep tissue without labeling. The fast-imaging speed enables visualizing interventional procedures and monitoring the pulsations of the thoracic aorta and radial artery in real-time. The video-rate dual-modal harmonic US and single-shot PA computed tomography use a clinical-grade linear-array transducer and thus can be readily implemented in clinical US imaging.

A Deep Learning-Based Model That Reduces Speed of Sound Aberrations for Improved In Vivo Photoacoustic Imaging

Photoacoustic imaging (PAI) has attracted great attention as a medical imaging method. Typically, photoacoustic (PA) images are reconstructed via beamforming, but many factors still hinder the beamforming techniques in reconstructing optimal images in terms of image resolution, imaging depth, or processing speed. Here, we demonstrate a novel deep learning PAI that uses multiple speed of sound (SoS) inputs. With this novel method, we achieved SoS aberration mitigation, streak artifact removal, and temporal resolution improvement all at once in structural and functional in vivo PA images of healthy human limbs and melanoma patients. The presented method produces high-contrast PA images in vivo with reduced distortion, even in adverse conditions where the medium is heterogeneous and/or the data sampling is sparse. Thus, we believe that this new method can achieve high image quality with fast data acquisition and can contribute to the advance of clinical PAI.

Recent Advances in Photoacoustic Tomography

Photoacoustic tomography (PAT) that integrates the molecular contrast of optical imaging with the high spatial resolution of ultrasound imaging in deep tissue has widespread applications in basic biological science, preclinical research, and clinical trials. Recently, tremendous progress has been made in PAT regarding technical innovations, preclinical applications, and clinical translations. Here, we selectively review the recent progresses and advances in PAT, including the development of advanced PAT systems for small-animal and human imaging, newly engineered optical probes for molecular imaging, broad-spectrum PAT for label-free imaging of biological tissues, high-throughput snapshot photoacoustic topography, and integration of machine learning for image reconstruction and processing. We envision that PAT will have further technical developments and more impactful applications in biomedicine.

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.

Snapshot photoacoustic topography through an ergodic relay of optical absorption in vivo

Photoacoustic tomography (PAT) has demonstrated versatile biomedical applications, ranging from tracking single cells to monitoring whole-body dynamics of small animals and diagnosing human breast cancer. Currently, PAT has two major implementations: photoacoustic computed tomography (PACT) and photoacoustic microscopy (PAM). PACT uses a multi-element ultrasonic array for parallel detection, which is relatively complex and expensive. In contrast, PAM requires point-by-point scanning with a single-element detector, which has a limited imaging throughput. The trade-off between the system cost and throughput demands a new imaging method. To this end, we have developed photoacoustic topography through an ergodic relay (PATER). PATER can capture a wide-field image with only a single-element ultrasonic detector upon a single laser shot. This protocol describes the detailed procedures for PATER system construction, including component selection, equipment setup and system alignment. A step-by-step guide for in vivo imaging of a mouse brain is provided as an example application. Data acquisition, image reconstruction and troubleshooting procedures are also elaborated. It takes ~130 min to carry out this protocol, including ~60 min for both calibration and snapshot wide-field data acquisition using a laser with a 2-kHz pulse repetition rate. PATER offers low-cost snapshot wide-field imaging of fast dynamics, such as visualizing blood pulse wave propagation and tracking melanoma tumor cell circulation in mice in vivo. We envision that PATER will have wide biomedical applications and anticipate that the compact size of the setup will allow it to be further developed as a wearable device to monitor human vital signs.

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.

Integration of Multitargeted Polymer-Based Contrast Agents with Photoacoustic Computed Tomography: An Imaging Technique to Visualize Breast Cancer Intratumor Heterogeneity

One of the primary challenges in breast cancer diagnosis and treatment is intratumor heterogeneity (ITH), i.e., the coexistence of different genetically and epigenetically distinct malignant cells within the same tumor. Thus, the identification of ITH is critical for designing better treatments and hence to increase patient survival rates. Herein, we report a noninvasive hybrid imaging technology that integrates multitargeted and multiplexed patchy polymeric photoacoustic contrast agents (MTMPPPCAs) with single-impulse panoramic photoacoustic computed tomography (SIP-PACT). The target specificity ability of MTMPPPCAs to distinguish estrogen and progesterone receptor-positive breast tumors was demonstrated through both fluorescence and photoacoustic measurements and validated by tissue pathology analysis. This work provides the proof-of-concept of the MTMPPPCAs/SIP-PACT system to identify ITH in nonmetastatic tumors, with both high molecular specificity and real-time detection capability.

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.

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.

Video-Rate Ring-Array Ultrasound and Photoacoustic Tomography

Ultrasonography and photoacoustic tomography provide complementary contrasts in preclinical studies, disease diagnoses, and imaging-guided interventional procedures. Here, we present a video-rate (20 Hz) dual-modality ultrasound and photoacoustic tomographic platform that has a high resolution, rich contrasts, deep penetration, and wide field of view. A three-quarter ring-array ultrasonic transducer is used for both ultrasound and photoacoustic imaging. Plane-wave transmission/receiving approach is used for ultrasound imaging, which improves the imaging speed by nearly two folds and reduces the RF data size compared with the sequential single-channel scanning approach. GPU-based image reconstruction is developed to advance computational speed. We demonstrate fast dual-modality imaging in phantom, mouse, and human finger joint experiments. The results show respiration motion, heart beating, and detailed features in the mouse internal organs. To our knowledge, this is the first report on fast plane-wave ultrasound imaging and single-shot photoacoustic computed tomography in a ring-array system.

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 computed tomography with lens-free focused fiber-laser ultrasound sensor

Optical detection of ultrasound is attractive to photoacoustic imaging due to its high sensitivity per unit area, broad bandwidth, and electromagnetic immunity. To enhance the sensitivity, previous optical transducers commonly necessitate bulk acoustic lenses to achieve focused ultrasound detection. Here, we proposed and demonstrated a novel lens-free focused optical ultrasound sensor by mechanically bending a flexible fiber laser. At a curvature radius of 30 mm, the curved fiber laser well conformed to the spherical wavefront of ultrasound exhibiting ~5 times higher sensitivity compared with the straight one. The focused fiber laser ultrasound sensor (FUS) presented a minimum detectable pressure of ~36 Pa with a working distance equal to its curvature radius. The sensor was applied to circular scanning photoacoustic computed tomography (PACT), which showed a ~70 μm in-plane resolution and a ~500 μm elevational resolution. In vivo imaging of a zebrafish and mouse brain shows the potential of this focused FUS for photoacoustic imaging in biological/medical studies.

Automated registration of magnetic resonance imaging and optoacoustic tomography data for experimental studies

Multimodal imaging combining optoacoustic tomography (OAT) with magnetic resonance imaging (MRI) enables spatiotemporal resolution complementarity, improves accurate quantification, and thus yields more insights into physiology and pathophysiology. However, only manual landmark based coregistration of OAT-MRI has been used so far. We developed a toolbox (RegOA), which frames an automated registration pipeline to align OAT with high-field MR images based on mutual information. We assessed the performance of the registration method using images acquired on one phantom with fiducial markers and in vivo/ex vivo data of mouse heads/brain. The accuracy and robustness of the registration are improved using a two-step registration method with preprocessing of OAT and MRI data. The major advantages of our approach are minimal user input and quantitative assessment of the registration error. The registration with MR and standard reference atlas enables regional information extraction, facilitating the accurate, objective, and rapid analysis of large groups of rodent OAT and MR images.

In vivo characterization of connective tissue remodeling using infrared photoacoustic spectra

Premature cervical remodeling is a critical precursor of spontaneous preterm birth, and the remodeling process is characterized by an increase in tissue hydration. Nevertheless, current clinical measurements of cervical remodeling are subjective and detect only late events, such as cervical effacement and dilation. Here, we present a photoacoustic endoscope that can quantify tissue hydration by measuring near-infrared cervical spectra. We quantify the water contents of tissue-mimicking hydrogel phantoms as an analog of cervical connective tissue. Applying this method to pregnant women in vivo, we observed an increase in the water content of the cervix throughout pregnancy. The application of this technique in maternal healthcare may advance our understanding of cervical remodeling and provide a sensitive method for predicting preterm birth.

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.