Photoacoustic (PA) imaging, also called optoacoustic imaging, is a new biomedical imaging modality based on the use of laser-generated ultrasound that has emerged over the last decade. It is a hybrid modality, combining the high-contrast and spectroscopic-based specificity of optical imaging with the high spatial resolution of ultrasound imaging. In essence, a PA image can be regarded as an ultrasound image in which the contrast depends not on the mechanical and elastic properties of the tissue, but its optical properties, specifically optical absorption. As a consequence, it offers greater specificity than conventional ultrasound imaging with the ability to detect haemoglobin, lipids, water and other light-absorbing chomophores, but with greater penetration depth than purely optical imaging modalities that rely on ballistic photons. As well as visualizing anatomical structures such as the microvasculature, it can also provide functional information in the form of blood oxygenation, blood flow and temperature. All of this can be achieved over a wide range of length scales from micrometres to centimetres with scalable spatial resolution. These attributes lend PA imaging to a wide variety of applications in clinical medicine, preclinical research and basic biology for studying cancer, cardiovascular disease, abnormalities of the microcirculation and other conditions. With the emergence of a variety of truly compelling in vivo images obtained by a number of groups around the world in the last 2–3 years, the technique has come of age and the promise of PA imaging is now beginning to be realized. Recent highlights include the demonstration of whole-body small-animal imaging, the first demonstrations of molecular imaging, the introduction of new microscopy modes and the first steps towards clinical breast imaging being taken as well as a myriad of in vivo preclinical imaging studies. In this article, the underlying physical principles of the technique, its practical implementation, and a range of clinical and preclinical applications are reviewed.

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Biomedical photoacoustic imaging

The life sciences can benefit greatly from imaging technologies that connect microscopic discoveries with macroscopic observations. One technology uniquely positioned to provide such benefits is photoacoustic tomography (PAT), a sensitive modality for imaging optical absorption contrast over a range of spatial scales at high speed. In PAT, endogenous contrast reveals a tissue's anatomical, functional, metabolic, and histologic properties, and exogenous contrast provides molecular and cellular specificity. The spatial scale of PAT covers organelles, cells, tissues, organs, and small animals. Consequently, PAT is complementary to other imaging modalities in contrast mechanism, penetration, spatial resolution, and temporal resolution. We review the fundamentals of PAT and provide practical guidelines for matching PAT systems with research needs. We also summarize the most promising biomedical applications of PAT, discuss related challenges, and envision PAT's potential to lead to further breakthroughs.

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A practical guide to photoacoustic tomography in the life sciences

Computer-aided detection/diagnosis (CAD) systems can enhance the diagnostic capabilities of physicians and reduce the time required for accurate diagnosis. The objective of this paper is to review the recent published segmentation and classification techniques and their state-of-the-art for the human brain magnetic resonance images (MRI). The review reveals the CAD systems of human brain MRI images are still an open problem. In the light of this review we proposed a hybrid intelligent machine learning technique for computer-aided detection system for automatic detection of brain tumor through magnetic resonance images. The proposed technique is based on the following computational methods; the feedback pulse-coupled neural network for image segmentation, the discrete wavelet transform for features extraction, the principal component analysis for reducing the dimensionality of the wavelet coefficients, and the feed forward back-propagation neural network to classify inputs into normal or abnormal. The experiments were carried out on 101 images consisting of 14 normal and 87 abnormal (malignant and benign tumors) from a real human brain MRI dataset. The classification accuracy on both training and test images is 99% which was significantly good. Moreover, the proposed technique demonstrates its effectiveness compared with the other machine learning recently published techniques. The results revealed that the proposed hybrid approach is accurate and fast and robust. Finally, possible future directions are suggested.

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

Neural implants are technical systems that restore sensory or motor functions after injury and modulate neural behavior in neuronal diseases. Neural interfaces or prostheses have lead to new therapeutic options and rehabilitation approaches in the last 40 years. The interface between the nervous tissue and the technical material is the place that determines success or failure of the neural implant. Recording of nerve signals and stimulation of nerve cells take place at this neuro-technical interface. Polymers are the most common material class for substrate and insulation materials in combination with metals for interconnection wires and electrode sites. This work focuses on the neuro-technical interface and summarizes its fundamental specifications first. The most common polymer materials are presented and described in detail. We conclude with an overview of the different applications and their specific designs with the accompanying manufacturing processes from precision mechanics, laser structuring and micromachining that are introduced in either the peripheral or central nervous system. © 2010 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys, 2010

https://onlinelibrary.wiley.com/doi/pdf/10.1002/polb.22169

Polymers for neural implants

Photoacoustic microscopy (PAM) is a hybrid in vivo imaging technique that acoustically detects optical contrast via the photoacoustic effect. Unlike pure optical microscopic techniques, PAM takes advantage of the weak acoustic scattering in tissue and thus breaks through the optical diffusion limit (~1 mm in soft tissue). With its excellent scalability, PAM can provide high-resolution images at desired maximum imaging depths up to a few millimeters. Compared with backscattering-based confocal microscopy and optical coherence tomography, PAM provides absorption contrast instead of scattering contrast. Furthermore, PAM can image more molecules, endogenous or exogenous, at their absorbing wavelengths than fluorescence-based methods, such as wide-field, confocal, and multi-photon microscopy. Most importantly, PAM can simultaneously image anatomical, functional, molecular, flow dynamic and metabolic contrasts in vivo. Focusing on state-of-the-art developments in PAM, this Review discusses the key features of PAM implementations and their applications in biomedical studies.

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Photoacoustic microscopy: Photoacoustic microscopy

https://ir.nctu.edu.tw:443/bitstream/11536/32293/1/000279160200018.pdf

Imaging brain hemodynamic changes during rat forepaw electrical stimulation using functional photoacoustic microscopy

https://ir.nctu.edu.tw:443/bitstream/11536/6785/1/000268904800002.pdf

Design and fabrication of a polyimide-based microelectrode array: application in neural recording and repeatable electrolytic lesion in rat brain.

https://ir.nctu.edu.tw:443/bitstream/11536/7549/1/000264038700004.pdf

Automatic segmentation of magnetic resonance images using a decision tree with spatial information

https://ir.nctu.edu.tw:443/bitstream/11536/8142/1/000261076700004.pdf

Siny Tsang

Papers

Imaging brain hemodynamic changes during rat forepaw electrical stimulation using functional photoacoustic microscopy

Design and fabrication of a polyimide-based microelectrode array: application in neural recording and repeatable electrolytic lesion in rat brain.

Automatic segmentation of magnetic resonance images using a decision tree with spatial information

Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree