Multispectral interlaced sparse sampling photoacoustic tomography based on directional total variation

Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography

“…In order to further test the effectiveness and precision of five different LF correction methods, we used the linear spectral un-mixing method [47] to calculate the distribution of oxyhemoglobin (HbO 2 ), de-oxyhemoglobin (Hb) and oxygen saturation (sO 2 ) from the obtained experimental results. Fig.…”

Advanced Image Post-Processing Methods for Photoacoustic Tomography: A Review

“…This fact creates new technical challenges for PAT image post-processing. For example, advanced imaging strategies were previously proposed for ring-shaped PAT, such as the interlaced sparse sampling PAT [131,132], which employs a rotational scanning scheme. Also, there are an increasing number of studies conducted based on 3D PAT systems, such as spherical array PAT [133][134][135][136][137][138].…”

Pixel-wise reconstruction of tissue absorption coefficients in photoacoustic tomography using a non-segmentation iterative method

“…The PAT images are formed by reconstructing the original point source of the ultrasonic waves generated by absorbing the laser pulse, and the pixel value in the image can be expressed as: where Γ denotes the thermo-elastic Grüneisen parameter, and denote the absorption and reduced scattering coefficients, Φ denotes the LF within a voxel at the position . In the beginning, we make an assumption that has been reconstructed from the acoustic measurements accurately and with negligible structural distortion [35] , [36] , [37] , [38] , [39] . In biological soft tissue, the thermo-elastic Grüneisen parameter has a small variation so that it is assumed to be constant [32] .…”