Review of deep learning for photoacoustic imaging

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.

https://www.spiedigitallibrary.org/journals/journal-of-biomedical-optics/volume-26/issue-4/040901/Deep-learning-in-photoacoustic-imaging-a-review/10.1117/1.JBO.26.4.040901.pdf

Deep learning in photoacoustic imaging: a review

Thin-film PMUTs: a review of over 40 years of research

/pdf/piezoelectric-micromachined-ultrasound-transducer-technology-1iramlmb.pdf

Piezoelectric Micromachined Ultrasound Transducer Technology: Recent Advances and Applications

This paper reports on an aluminum nitride (AlN) piezoelectric micromachined ultrasound transducer (PMUT) array for photoacoustic (PA) imaging, where the high-order resonance modes of the PMUT are utilized to improve imaging resolution. A flexural vibration mode (FVM) PMUT is fabricated and applied in a photoacoustic imaging (PAI) system. Specifically, the microelectromechanical system (MEMS)-based PMUT is suitable for PA endoscopic imaging of blood vessels and bronchi due to its miniature size and high sensitivity. More importantly, AlN is a nontoxic material, which makes it harmless for biomedical applications. In the PAI system, the AlN PMUT array is used to detect PA signals, and the acousto-mechanical response is designed and optimized at the PMUT's fundamental resonance. In this work, we focus on the high-order resonance performance of the PMUT PAI beyond the fundamental resonance. The acoustic and electrical responses of the PMUT's high-order resonance modes are characterized and analyzed. The fundamental and three high-order resonance bandwidths are 2.2, 8.8, 18.5, and 48.2 kHz. Compared with the resolution at the fundamental resonance mode, the resolutions at third- and fourth-order resonance modes increase by 38.7% and 76.9% in a phantom experiment. The high-order resonance modes of the AlN PMUT sensor array provide higher central frequency and wider bandwidth for PA signal detection, which increase the resolution of PAI compared to the PMUT working at the fundamental resonance mode. 

/pdf/beyond-fundamental-resonance-mode-high-order-multi-band-aln-2gkvsog9.pdf

Beyond fundamental resonance mode: high-order multi-band ALN PMUT for in vivo photoacoustic imaging

Photoacoustic imaging which combines high contrast of optical imaging and high resolution of ultrasound imaging, can provide functional information, potentially playing a crucial role in the study of breast cancer diagnostics. However, open source dataset for PA imaging research is insufficient on account of lacking clinical data. To tackle this problem, we propose a method to automatically generate breast numerical model for photoacoustic imaging. The different type of tissues is automatically extracted first by employing deep learning and other methods from mammography. And then the tissues are combined by mathematical set operation to generate a new breast image after being assigned optical and acoustic parameters. Finally, breast numerical model with proper optical and acoustic properties are generated, which are specifically suitable for PA imaging studies, and the experiment results indicate that our method is feasible with high efficiency.

Human Breast Numerical Model Generation Based on Deep Learning for Photoacoustic Imaging

Photoacoustic imaging (PAI) combines the deep penetration of ultrasound imaging and the high absorption contrast of optical imaging, which has attracted increasing research interest in recent years. The PAI based on multi-wavelength laser source is conducive to detecting different components in the blood such as oxygen saturation. The generation of PA signals could rely on the relative low-cost pulsed laser diode. However, the multi-wavelength PAI system requires multiple pulsed laser diodes, which will multiply the cost of the imaging system. On the other hand, the amplitude of the PA signals will change when inducing the Gruneisen relaxation effect due to the heat accumulation. In this paper, we proposed a continuous multi-wavelength photoacoustic imaging (CMPAI) system based on the combination of a single-wavelength pulsed laser diode and a multi-wavelength continuous laser diode module. The continuous wave (CW) laser is adapted for heating the sample. The PA signals are induced by a single-wavelength pulsed laser. By controlling the laser irradiation sequence (pulse-CW-pulse), the images before and after heating will be attained. By proper differentiating operations, the images with different-wavelengths’ light absorption could be revealed. Furthermore, using this proposed system to detect the concentration of ink that is mixed by different portion of green and red ink would be demonstrated in the following.

Multi-wavelengths Nonlinear Photoacoustic Imaging Based on Compact Laser Diode System

In lumbar puncture surgeries, force and position information throughout the insertion procedure is vital for needle tip localization, because it reflects different tissue properties. Especially in pediatric cases, the changes are always insignificant for surgeons to sense the crucial feeling of loss of resistance. In this study, a robot system is developed to tackle the major clinical difficulties. Four different control algorithms with intention recognition ability are applied on a novel lumbar puncture robot system for better human–robot cooperation. Specific penetration detection based on force and position derivatives captures the feeling of loss of resistance, which is deemed crucial for needle tip location. Kinematic and actuation modeling provides a clear description of the hardware setup. The control algorithm experiment compares the human–robot cooperation performance of proposed algorithms. The experiment also dictates the clear role of designed penetration detection criteria in capturing the penetration, improving the success rate, and ensuring operational safety.

Penetration detection with intention recognition for cooperatively controlled robotic needle insertion

Photoacoustic tomography (PAT) is an emerging technology for biomedical imaging that combines the superiorities of high optical contrast and acoustic penetration. In the PAT system, more PA signals are preferred to be detected from full field of view to reconstruct the PA images with higher fidelity. However, the requirement for more PA signals’ detection leads to more time consumption for single-channel scanning-based PAT system or higher cost of data acquisition (DAQ) module for an array-based PAT system. To address this issue, we proposed a programmable acoustic delay-line (PADL) module to reduce DAQ cost and accelerate imaging speed for PAT system. The module is based on bidirectional conversion between acoustic signals and electrical signals, including ultrasound transmission in between to provide sufficient time delay. The acoustic delay-line module achieves tens or hundreds of microseconds’ delay for each channel and is controlled by a programmable control unit. In this work, it achieves to merge four inputs of PA signals into one output signal, which can be recovered into original four PA signals in the digital domain after DAQ. The imaging experiments of pencil leads embedded in agar phantom are conducted by the PAT system equipped with the proposed PADL module, which demonstrated its feasibility to reduce the cost of the PAT system. An in vivo study of human finger PAT imaging with delay-line module verified its feasibility for biomedical imaging applications.

Programmable Acoustic Delay-Line Enabled Low-Cost Photoacoustic Tomography System

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