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1. Flexible array transducer for photoacoustic-guided interventions: phantom and ex vivo demonstrations

   https://doi.org/10.1364/BOE.491406  

   Zhang, Jiaxin; Wiacek, Alycen; Feng, Ziwei; Ding, Kai; Lediju_Bell, Muyinatu_A (July 2023, Biomedical Optics Express)

   Photoacoustic imaging has demonstrated recent promise for surgical guidance, enabling visualization of tool tips during surgical and non-surgical interventions. To receive photoacoustic signals, most conventional transducers are rigid, while a flexible array is able to deform and provide complete contact on surfaces with different geometries. In this work, we present photoacoustic images acquired with a flexible array transducer in multiple concave shapes in phantom andex vivobovine liver experiments targeted toward interventional photoacoustic applications. We validate our image reconstruction equations for known sensor geometries with simulated data, and we provide empirical elevation field-of-view, target position, and image quality measurements. The elevation field-of-view was 6.08 mm at a depth of 4 cm and greater than 13 mm at a depth of 5 cm. The target depth agreement with ground truth ranged 98.35-99.69%. The mean lateral and axial target sizes when imaging 600μm-core-diameter optical fibers inserted within the phantoms ranged 0.98-2.14 mm and 1.61-2.24 mm, respectively. The mean ± one standard deviation of lateral and axial target sizes when surrounded by liver tissue were 1.80±0.48 mm and 2.17±0.24 mm, respectively. Contrast, signal-to-noise, and generalized contrast-to-noise ratios ranged 6.92–24.42 dB, 46.50–67.51 dB, and 0.76–1, respectively, within the elevational field-of-view. Results establish the feasibility of implementing photoacoustic-guided surgery with a flexible array transducer. 

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2. Using Monte Carlo simulated PPGs signals to train a deep learning model to predict hemoglobin levels

   https://doi.org/10.1117/12.2677499  

   Alvarez, Michael; Arzuaga, Emmanuel; Sierra, Heidy (September 2023, SPIE NANOSCIENCE + ENGINEERING | 20-25 AUGUST 2023 Emerging Topics in Artificial Intelligence (ETAI) 2023)

   Volpe, Giovanni; Pereira, Joana B; Brunner, Daniel; Ozcan, Aydogan (Ed.)

   Measuring Hemoglobin (Hb) levels is required for the assessment of different health conditions, such as anemia, a condition where there are insufficient healthy red blood cells to carry enough oxygen to the body's tissues. Measuring Hb levels requires the extraction of a blood sample, which is then sent to a laboratory for analysis. This is an invasive procedure that may add challenges to the continuous monitoring of Hb levels. Noninvasive techniques, including imaging and photoplethysmography (PPG) signals combined with machine learning techniques, are being investigated for continuous measurements of Hb. However, the availability of real data to train the algorithms is limited to establishing a generalization and implementation of such techniques in healthcare settings. In this work, we present a computational model based on Monte Carlo simulations that can generate multispectral PPG signals that cover a broad range of Hb levels. These signals are then used to train a Deep Learning (DL) model to estimate hemoglobin levels. Through this approach, valuable insights about the relationships between PPG signals, oxygen saturation, and Hb levels are learned by the DL model. The signals were generated by propagating a source in a volume that contains the skin tissue properties and the target physiological parameters. The source consisted of plane waves using the 660 nm and 890 nm wavelengths. A range of 6 g/dL to 18 dL Hb values was used to generate 468 PPGs to train a Convolutional Neural Network (CNN). The initial results show high accuracy in detecting low levels of Hb. To the best of our knowledge, the complexity of biological interactions involved in measuring hemoglobin levels has yet to be fully modeled. The presented model offers an alternative approach to studying the effects of changes in Hb levels on the PPGs signal morphology and its interaction with other physiological parameters that are present in the optical path of the measured signals. 

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3. Temporal and spectral unmixing of photoacoustic signals by deep learning

   https://doi.org/10.1364/OL.426678  

   Zhou, Yifeng; Zhong, Fenghe; Hu, Song (May 2021, Optics Letters)

   Improving the imaging speed of multi-parametric photoacoustic microscopy (PAM) is essential to leveraging its impact in biomedicine. However, to avoid temporal overlap, the A-line rate is limited by the acoustic speed in biological tissues to a few megahertz. Moreover, to achieve high-speed PAM of the oxygen saturation of hemoglobin, the stimulated Raman scattering effect in optical fibers has been widely used to generate 558 nm from a commercial 532 nm laser for dual-wavelength excitation. However, the fiber length for effective wavelength conversion is typically short, corresponding to a small time delay that leads to a significant overlap of the A-lines acquired at the two wavelengths. Increasing the fiber length extends the time interval but limits the pulse energy at 558 nm. In this Letter, we report a conditional generative adversarial network-based approach that enables temporal unmixing of photoacoustic A-line signals with an interval as short as $\sim <\#comment/>38\mathrm{n}\mathrm{s}$, breaking the physical limit on the A-line rate. Moreover, this deep learning approach allows the use of multi-spectral laser pulses for PAM excitation, addressing the insufficient energy of monochromatic laser pulses. This technique lays the foundation for ultrahigh-speed multi-parametric PAM. 

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4. Photoacoustic imaging for surgical guidance: Principles, applications, and outlook

   https://doi.org/10.1063/5.0018190  

   Lediju_Bell, Muyinatu_A (August 2020, Journal of Applied Physics)

   Minimally invasive surgeries often require complicated maneuvers and delicate hand–eye coordination and ideally would incorporate “x-ray vision” to see beyond tool tips and underneath tissues prior to making incisions. Photoacoustic imaging has the potential to offer this feature but not with ionizing x-rays. Instead, optical fibers and acoustic receivers enable photoacoustic sensing of major structures—such as blood vessels and nerves—that are otherwise hidden from view. This imaging process is initiated by transmitting laser pulses that illuminate regions of interest, causing thermal expansion and the generation of sound waves that are detectable with conventional ultrasound transducers. The recorded signals are then converted to images through the beamforming process. Photoacoustic imaging may be implemented to both target and avoid blood-rich surgical contents (and in some cases simultaneously or independently visualize optical fiber tips or metallic surgical tool tips) in order to prevent accidental injury and assist device operators during minimally invasive surgeries and interventional procedures. Novel light delivery systems, counterintuitive findings, and robotic integration methods introduced by the Photoacoustic & Ultrasonic Systems Engineering Lab are summarized in this invited Perspective, setting the foundation and rationale for the subsequent discussion of the author’s views on possible future directions for this exciting frontier known as photoacoustic-guided surgery. 

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5. Fine-Tuning of Optical Resonance Wavelength of Surface-Micromachined Optical Ultrasound Transducer Arrays for Single-Wavelength Light Source Readout

   https://doi.org/10.3390/mi15091111  

   Yan, Zhiyu; Fang, Cheng; Zou, Jun (September 2024, Micromachines)

   This article reports the fine-tuning of the optical resonance wavelength (ORW) of surface-micromachined optical ultrasound transducer (SMOUT) arrays to enable ultrasound data readout with non-tunable interrogation light sources for photoacoustic computed tomography (PACT). Permanent ORW tuning is achieved by material deposition onto or subtraction from the top diaphragm of each element with sub-nanometer resolution. For demonstration, a SMOUT array is first fabricated, and its ORW is tuned for readout with an 808 nm laser diode (LD). Experiments are conducted to characterize the optical and acoustic performances of the elements within the center region of the SMOUT array. Two-dimensional and three-dimensional PACT (photoacoustic computed tomography) is also performed to evaluate the imaging performance of the ORW-tuned SMOUT array. The results show that the ORW tuning does not degrade the optical, acoustic, and overall imaging performances of the SMOUT elements. As a result, the fine-tuning method enables new SMOUT-based PACT systems that are low cost, compact, powerful, and even higher speed, with parallel readout capability. 

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