Multi-wavelength photoacoustic imaging (PAI) has garnered significant attention due to its excellent capabilities in molecular and functional imaging. Researchers in the PAI community are consistently in need of reliable multi-wavelength imaging platforms. For clinical applications, PAI machines that are mobile, compact, and rapidly tunable are required. We have designed a stable and cost-effective PAI platform which consists of an optical parametric oscillator (OPO), a data acquisition system (DAQ), and various imaging probes. The stability of the OPO has been enhanced through the implementation of air-floating springs and mirror frame designs, alongside open-loop wavelength control. Effective monitoring of energy using built-in energy meters has improved the accuracy of PA spectral measurements. Integration with digital boards has effectively enhanced the noise resistance of DAQ and reduced its physical size. The platform can achieve a repetition rate of 10 Hz, swift wavelength tuning within the range of 680 to 950 nm (with a resolution of 1nm), and single-pulse energy greater than 80 mJ. The spectral range covers the absorption features of important chromophores such as hemoglobin, fat, and indocyanine green. The DAQ system can record PA data with 80 MHz sampling rate, 14-bit resolution, and 128/256/512 channels. The platform is equipped with linear array probes and semi-circular array probes to meet the requirements of both animal and human imaging. The semi-circular array probe utilizes a polydimethylsiloxane (PDMS) membrane with good light transmission to form a water bag for ultrasound coupling. This membrane is flexible and can conform well to different tissue shapes. Using this platform, we have conducted experiments including blood oxygen measurement, imaging of arm muscles and fat. In these experiments, we demonstrated accurate blood oxygen analyses and high-contrast muscle and fat imaging.
SignificanceTo ensure precise tumor localization and subsequent pathological examination, a metal marker clip (MC) is placed within the tumor or lymph node prior to neoadjuvant chemotherapy for breast cancer. However, as tumors decrease in size following treatment, detecting the MC using ultrasound imaging becomes challenging in some patients. Consequently, a mammogram is often required to pinpoint the MC, resulting in additional radiation exposure, time expenditure, and increased costs. Dual-modality imaging, combining photoacoustic (PA) and ultrasound (US), offers a promising solution to this issue.AimOur objective is to localize the MC without radiation exposure using PA/US dual-modality imaging.ApproachA PA/US dual-modality imaging system was developed. Utilizing this system, both phantom and clinical experiments were conducted to demonstrate that PA/US dual-modality imaging can effectively localize the MC.ResultsThe PA/US dual-modality imaging can identify and localize the MC. In clinical trials encompassing four patients and five MCs, the recognition rate was ∼80%. Three experiments to verify the accuracy of marker position recognition were successful.ConclusionsWe effectively localized the MC in real time using PA/US dual-modality imaging. Unlike other techniques, the new method enables surgeons to pinpoint nodules both preoperatively and intraoperatively. In addition, it boasts non-radioactivity and is comparatively cost-effective.
Photoacoustic computed tomography (PACT), also known as optoacoustic or thermoacoustic tomography, is a rapidly emerging hybrid imaging modality that combines optical image contrast with ultrasound detection. Most currently available PACT image reconstruction algorithms are based on idealized imaging models that assume a lossless and acoustically homogeneous medium. However, in many applications of PACT, if the non-uniform acoustic properties of objects are not considered in the reconstruction algorithm, the reconstructed images may contain significant distortions and artifacts. This paper proposes a fully automatic double-SoS (Speed-of-sound) reconstruction algorithm which runs at 10Hzfortwo-dimensional images of (512)(512) pixels. The algorithm uses a deep learning model to segment the animal’s outer profile from the water background. We adaptively calculate the most appropriate sound speed and assign different sound speeds to the two regions for PA image reconstruction. The reconstructed images were compared to those reconstructed using a uniform SoS quantitatively.
Photoacoustic imaging (PAI, also called optoacoustic imaging) combines light excitation and ultrasound detection for deep-tissue imaging with light absorption contrast. PAI can map the distribution of endogenous chromophores such as oxy-hemoglobin, deoxy-hemoglobin, lipids, water, and melanin. PAI performs especially well for structural and functional imaging of blood vessels. Its centimeter-deep imaging depth and ultrasound-defined resolution make it well suited for clinical application, where systems employing a linear array ultrasound probe are most commonly used due to simplicity, flexibility, and easy integration with standard ultrasonic imaging. The existing linear array based imaging systems typically employ optical fiber bundles for light delivery, such a scheme enjoys mechanical flexibility, optical stability, and simple light coupling. However, major drawbacks associated with fiber illumination include suboptimal transmission efficiency and a lack of control of the illumination pattern. Articulated arms provide an alternative light delivery option which potentially offer high transmission efficiency, stable and flexible operation, and low cost. Despite its wide applications in cosmetology, articulated arms for light delivery were understudied in the PAI community. In this paper, we reported the fabrication and experimental evaluation of an articulated arm specifically designed for linear-array-based PAI. Without losing the flexibility provided by the linear probe. Moreover, the articulated arm can be equipped with spatial positioning devices to perform three-dimensional reconstructions.
Photoacoustic imaging (PAI) is a biomedical imaging modality that can provide structural, functional, and molecular information. In PAI, laser pulses illuminate the tissue, and transient light absorption leads to instant thermal expansion and succeeding ultrasound emission. Since oxy- and deoxyhemoglobin are the major light-absorbing chromophores in biological tissue, PAI has very high contrast and is intrinsically suitable for the imaging of blood vessels. Meanwhile, superb microvascular imaging (SMI) is an emerging ultrasound imaging technique for angiography. In comparison to traditional color Doppler and power Doppler techniques which rely on the suppression of low-velocity components, SMI works by an intelligent algorithm that renders small vessels with low flow velocity visible. To date, there is no work to compare PAI and SMI in terms of vascular imaging capabilities. In this paper, we provide our recent evaluation results in imaging depths, speeds, sensitivities, and resolutions of these two modalities through phantom experiments and in-vivo studies. We used PAI and SMI to image the human forearm, and our preliminary data show that PAI is superior in imaging speeds, and sensitivities for superficial blood vessels. We acknowledge that more work needs to be done to compare the two techniques in diverse clinical applications more quantitatively, and we hope our work can pave the way for such systematic studies..
KEYWORDS: Data modeling, Image quality, Image segmentation, Image processing, Signal detection, Photoacoustic imaging, Image restoration, Model-based design, In vivo imaging, 3D modeling
Significance: Photoacoustic (PA) imaging can provide structural, functional, and molecular information for preclinical and clinical studies. For PA imaging (PAI), non-ideal signal detection deteriorates image quality, and quantitative PAI (QPAI) remains challenging due to the unknown light fluence spectra in deep tissue. In recent years, deep learning (DL) has shown outstanding performance when implemented in PAI, with applications in image reconstruction, quantification, and understanding.
Aim: We provide (i) a comprehensive overview of the DL techniques that have been applied in PAI, (ii) references for designing DL models for various PAI tasks, and (iii) a summary of the future challenges and opportunities.
Approach: Papers published before November 2020 in the area of applying DL in PAI were reviewed. We categorized them into three types: image understanding, reconstruction of the initial pressure distribution, and QPAI.
Results: When applied in PAI, DL can effectively process images, improve reconstruction quality, fuse information, and assist quantitative analysis.
Conclusion: DL has become a powerful tool in PAI. With the development of DL theory and technology, it will continue to boost the performance and facilitate the clinical translation of PAI.
Photoacoustic imaging is an emerging optical imaging modality which provides optical absorption contrasts and high resolution in the optical diffusive regime. In photoacoustic computed tomography (PACT), often times the detection of the photoacoustic signal only covers a partial solid angle less than 4π, due to experimental or economic constraints. Incomplete spatial coverage always jeopardizes image quality and resolution, and results in significant artifacts and missing of image features. This problem is referred to as “limited view” and has remained unsolved for decades. In this work, we present a new machine-learning-based method that is specifically designed to compensate for the missing information due to limited view. The robustness and effectiveness of our method were demonstrated using numerical, phantom, and in vivo experiments.
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