Polydimethylsiloxane (PDMS) is widely used in biomedical science and can form composites that have broad applicability. One promising application where PDMS composites offer several advantages is optical ultrasound generation via the photoacoustic effect. Here, methods to create these PDMS composites are reviewed and classified. It is highlighted how the composites can be applied to a range of substrates, from micrometer‐scale, temperature‐sensitive optical fibers to centimeter‐scale curved and planar surfaces. The resulting composites have enabled all‐optical ultrasound imaging of biological tissues both ex vivo and in vivo, with high spatial resolution and with clinically relevant contrast. In addition, the first 3D all‐optical pulse‐echo ultrasound imaging of ex vivo human tissue, using a PDMS‐multiwalled carbon nanotube composite and a fiber‐optic ultrasound receiver, is presented. Gold nanoparticle‐PDMS and crystal violet‐PDMS composites with prominent absorption at one wavelength range for pulse‐echo ultrasound imaging and transmission at a second wavelength range for photoacoustic imaging are also presented. Using these devices, images of diseased human vascular tissue with both structural and molecular contrast are obtained. With a broader perspective, literature on recent advances in PDMS microfabrication from different fields is highlighted, and methods for incorporating them into new generations of optical ultrasound generators are suggested.
Photoacoustic imaging (PAI) is an emerging biomedical imaging modality that is based on optical absorption contrast, capable of revealing distinct spectroscopic signatures of tissue at high spatial resolution and large imaging depths. However, clinical applications of conventional non-invasive PAI systems have been restricted to examinations of tissues at depths less than a few cm due to strong light attenuation. Minimally invasive photoacoustic imaging (miPAI) has greatly extended the landscape of PAI by delivering excitation light within tissue through miniature fibre-optic probes. In the past decade, various miPAI systems have been developed with demonstrated applicability in several clinical fields. In this article, we present an overview of the current status of miPAI and our thoughts on future perspectives.
Multimode fibres (MMF) are remarkable high-capacity information channels owing to the large number of transmitting fibre modes 1-3 , and have recently attracted significant renewed interest in applications such as optical communication, imaging, and optical trapping [4][5][6][7][8][9][10][11][12][13][14][15] . At the same time, the optical transmitting modes inside MMFs are highly sensitive to external perturbations and environmental changes, resulting in MMF transmission channels being highly variable and random [16][17][18] . This largely limits the practical application of MMFs and hinders the full exploitation of their information capacity. Despite great research efforts made to overcome the high variability and randomness inside MMFs, any geometric change to the MMF leads to completely different transmission matrices, which unavoidably fails at the information recovery. Here, we show the successful binary image transmission using deep learning through a single MMF, which is stationary or subject to dynamic shape variations. We found that a single convolutional neural network has excellent generalisation capability with various MMF transmission states. This deep neural network can be trained by multiple MMF transmission states to accurately predict unknown information at the other end of the MMF at any of these states, without knowing which state is present. Our results demonstrate that deep learning is a promising solution to address the variability and randomness challenge of MMF based information channels. This deep-learning approach is the starting point of developing future high-capacity MMF optical systems and devices, and is applicable to optical systems concerning other diffusing media.We use a simple digital micromirror device (DMD) based single MMF transmission system 19 to demonstrate the idea, as shown in Fig. 1(a). The DMD can be controlled to display any binary pattern by turning 'ON' and 'OFF' individual micrormirrors, simulating a 2D object imaging from the DMD through the MMF to the camera. The experimental setup details are given in Methods. As the first step to verify our idea, we use transmission matrix (TM) calculated output speckle images for deep neural network training and prediction 20 . Firstly we calculate the MMF TM using 8000 input-output pairs collected from the experimental system. The measured TM is verified experimentally to have an average accuracy of 98% for output speckle pattern calculation (details in Methods). We then use the processed binary images of handwritten digits downloaded from the MNIST handwritten digits database 21,22 as the DMD input pattern. The binary processing is to convert the handwritten digit into 36×36 pixel image format that directly matches the fibre mode count and eigenchannels for optimal delivery 19 (details in Methods). As shown in Fig. 1(b), the MMF output speckle images are calculated using TM and the binary handwritten digits. We calculate 7,040 speckle images corresponding to different input binary images for the digits '0-9' (704 speckle i...
Image transmission through multimode optical fibers has been an area of immense interests driven by the demand for miniature endoscopes in biomedicine and higher speed and capacity in telecommunications. Conventionally, a complex-valued transmission matrix is obtained experimentally to link the input and output light fields of a multimode fiber for image retrieval, which complicates the experimental setup and increases the computational complexity. Here, we report a simple and high-speed method for image retrieval based on our demonstration of a pseudo-linearity between the input and output light intensity distributions of multimode fibers. We studied the impact of several key parameters to image retrieval, including image pixel count, fiber core diameter and numerical aperture. We further demonstrated with experiments and numerical simulations that a wide variety of input binary and gray scale images could be faithfully retrieved from the corresponding output speckle patterns. Thus, it promises to be useful for highly miniaturized endoscopy in biomedicine and spatial-mode-division multiplexing in telecommunications.
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