Members of IPASC have created an open-source library for image reconstruction algorithms that are compatible with the IPASC data format. Within this project, we create a testing framework for the evaluation of image reconstruction algorithms to identify their context-dependent strengths and weaknesses. We develop an open-access dataset comprising both simulated and experimental data to facilitate collaboration among all stakeholders associated with photoacoustic imaging and lower the barrier of entry for new researchers in the field by making the project deliverables available open-source.
Photoacoustic (PA) imaging combines optical spectroscopic contrast with deep tissue penetration, offering valuable functional, molecular, and structural information about tissue. However, a long-standing challenge with PA imaging has been that the quantification accuracy of tissue chromophores concentrations remains limited due to the spectral colouring effect. Monte Carlo (MC) simulation is regarded as the gold standard to model light transportation in tissue but can be computationally demanding, thus not suitable for real-time applications. We propose a time-efficiency solution using conditional generative adversarial networks (cGANs) to generate light fluence distributions within tissue towards real-time spectral decolouring in PA imaging. The networks were trained to predict light fluence distribution from realistic tissue anatomy and optical properties using MC simulation as ground truth. We achieved high-quality light fluence synthesis, with a peak signal-to-noise ratio of 31.9 dB using in vivo segmentation. We also demonstrated the validity of spectral decolouring for PA quantification, with an error of absorption efficient estimation around 0.05 using numerical phantoms. Thus, this approach holds promise for enhancing the quantification performance of PA imaging in real-time.
Photoacoustic (PA) imaging has demonstrated tremendous potential for various clinical and pre-clinical applications in the past two decades. Laser diodes and light-emitting diodes can be used as substitutes for solid-state lasers with the benefits of low cost and compact size. However, PA signals generated by such light sources have relatively low signal-to-noise ratios due to the low output energy. Here, we proposed a spatiotemporal singular value decomposition denoising method for PA radiofrequency data acquired at high frame rates. Within silico and in vivo experiments, it outperformed frame averaging and could be used for real-time in vivo applications.
Ultrasound (US) imaging is commonly used to guide minimally invasive surgeries but has poor contrast of the invasive devices such as clinical needles. Photoacoustic (PA) imaging promises to be efficient for visualising needles. Elastomeric coatings can also be applied on the needle surface to improve its visibility, however, strong signals generated from the highly absorbing coatings sometimes introduce image artefacts which affect needle identification. In this work, we developed a deep learning-based method to enhance the needle visualisation by removing the artefacts. We anticipated that the proposed methods could be useful for guiding percutaneous needle insertions.
High-speed photoacoustic (PA) endomicroscopy imaging is desired for real-time guidance of minimally invasive surgery. However, the imaging speed of wavefront shaping-based endomicroscopy has been limited by the speed of spatial light modulators. In this work, a deep convolutional neural network was used to improve the imaging speed of a newly developed PA endomicroscopy system by enhancing sparsely sampled PA images. With a carbon fibre phantom, this method increased the imaging speed by 16 times without significantly affecting the image quality. With further validation on more complex datasets, this approach is promising to achieve real-time PA endomicroscopy imaging via wavefront shaping.
Accurate identification of the interventional medical device during ultrasound-guided minimally-invasive procedures is of critical importance. A real-time 3D needle tracking system has been developed that utilises a fiber-optic, photoacoustic US transmitter integrated into the needle tip and a custom 2D, 4x4 receiver array attached to a clinical US imaging probe. Ultrasound signals received by the array are used to determine the location of source, which is then registered to the imaging probe and visualised. During initial laboratory measurements of tracking accuracy, the mean displacement between tracked and true distances from the array face was 0.8 ± 0.8 mm.
Photoacoustic (PA) endoscopy promises to be useful in a variety of clinical contexts including intravascular imaging, gastrointestinal tracts imaging and surgical guidance. Recent advancements of optical wavefront shaping allow the development of ultrathin endoscopy probes based on multimode optical fibres, which can provide higher spatial resolution than previously reported fibre bundle-based endoscopes. In this work, we developed a forward-viewing PA endomicroscopy imaging system and further improved its performance with a deep image prior (DIP) neural network. Laser was focused and scanned through a multimode fibre via wavefront shaping, in which a real-valued intensity transmission matrix approach was used for fibre characterisation, and a digital micromirror device (DMD) was used for light modulation. The excited ultrasound waves at the distal fibre tip were detected by an ultrasound transducer. High fidelity images of ex vivo mouse red blood cells were acquired. A DIP neural network was then used to improve the spatial resolution with unsupervised learning. Convolutional filters were used to learn features of low-level images as priors and reconstruct high-resolution images accordingly. The performance of the DIP approach was evaluated using a structural similarity index measure (SSIM) at a level of 0.85 with 25% effective pixels, which outperformed the bicubic method. The use of DIP allows reducing scanning positions by several times, and thus improves the speed of pixel-wise PA microscopy imaging. With further miniaturisation of the ultrasound detector, we anticipate that this system could be used for real-time guidance of minimally invasive surgeries by providing micro-structural, molecular, and functional information of tissue.
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