In photoacoustic imaging, accurate spectral unmixing is required for revealing functional and molecular information of the tissue using multispectral photoacoustic imaging data. A significant challenge in deep-tissue photoacoustic imaging is the nonlinear dependence of the received photoacoustic signals on the local optical fluence and molecular distribution. To overcome this, we have deployed an end-to-end unsupervised neural network based on autoencoders. The proposed method employs the physical properties as the constraints to the neural network which effectively performs the unmixing and outputs the individual molecular concentration maps without a-priori knowledge of their absorption spectra. The algorithm is tested on a set of simulated multispectral photoacoustic images comprising of oxyhemoglobin, deoxy-hemoglobin and indocyanine green targets embedded inside a tissue mimicking medium. These in silico experiments demonstrated promising photoacoustic spectral unmixing results using a completely unsupervised deep learning approach.
Photoacoustic imaging shows great promise for clinical environments where real-time position feedback is critical, including the guiding of minimally invasive surgery, drug delivery, stem cell transplantation, and the placement of metal implants such as stents, needles, staples, and brachytherapy seeds. Photoacoustic imaging techniques generate high contrast, label-free images of human vasculature, leveraging the high optical absorption characteristics of hemoglobin to generate measurable longitudinal pressure waves. However, the depth-dependent decrease in optical fluence and lateral resolution affects the visibility of deeper vessels or other absorbing targets. This poses a problem when the precise locations of vessels are critical for the application at hand, such as navigational tasks during minimally invasive surgery. To address this issue, a novel deep neural network was designed, developed, and trained to predict the location of circular chromophore targets in tissue mimicking a strong scattering background, given measurements of photoacoustic signals from a linear array of ultrasound elements. The network was trained on 16,240 samples of simulated sensor data and tested on a separate set of 4,060 samples. Both our training and test sets consisted of optical fluence-dependent photoacoustic signal measurements from point sources at varying locations. Our network was able to predict the location of point sources with a mean axial error of 4.3 μm and a mean lateral error of 5.8 μm.
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