Imaging peripheral nerve morphology, function, and vascular supply is important in clinical medicine and research. In this work, we evaluate the imaging capabilities of multispectral optoacoustic tomography (MSOT) for peripheral nerves. We demonstrate how recent advances in MSOT data processing combined with data-driven unmixing overcome adverse effects of measurement noise and light fluence attenuation and provide detailed insights into the vasa nervorum and the internal structure of peripheral nerves.
KEYWORDS: Model based design, Optoacoustics, Education and training, Data modeling, Medical image reconstruction, In vivo imaging, Image quality, Tomography, Scanners, Ultrasound tomography, Deep learning, Photoacoustic imaging, Inverse problems
Multispectral optoacoustic tomography requires real-time image feedback during clinical use. Herein, we present DeepMB, a deep learning framework to express the model-based reconstruction operator with a deep neural network and reconstruct high-quality optoacoustic images from arbitrary experimental input data at speeds that enable live imaging (31ms per image).
The total impulse response of a clinical optoacoustic system is characterized by combining experimentally acquired signals with a numerical model of the spatial impulse response, resulting in high-resolution images in clinical applications.
KEYWORDS: Optoacoustics, Tomography, Tissues, Interference (communication), Medical imaging, Image enhancement, Denoising, Surgery, Signal processing, Real time imaging
Image contrast in multispectral optoacoustic tomography can be reduced by electrical noise. We present a deep learning method to remove electrical noise from optoacoustic signals and thereby significantly enhance morphological and spectral contrast.
Even though the speed of sound (SoS) is non-homogeneous in biological tissue, most reconstruction algorithms for optoacoustic imaging neglect its variation. In addition, when heavy water is used as coupling medium to enable imaging of certain biological chromophores such as lipids and proteins, the SoS also differs significantly between couplant and tissue. While the assumption of uniform SoS is known to introduce visible deformations of features in single-wavelength optoacoustic images, the spectral error introduced by the assumption of uniform SoS is not fully understood. In this work, we provide an in-depth spectral analysis of multi-spectral optoacoustic imaging artifacts that result from the assumption of uniform SoS in situations where SoS changes substantially. We propose a dual-SoS model to incorporate the SoS variation between the couplant and the sample. Tissue-mimicking phantom experiments and in vivo measurements show that uniform SoS reconstruction causes spectral smearing, which dual-SoS modeling can largely eliminate. Due to this increased spectral accuracy, the method has the potential to improve clinical studies that rely on quantitative optoacoustic imaging of biomolecules like hemoglobin or lipids.
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