Triple-path feature transform network for ring-array photoacoustic tomography image reconstruction

Photoacoustic imaging(PAI)is a noninvasive emerging imaging method based on the photoacoustic effect,which provides necessary assistance for medical diagnosis.It has the characteristics of large imaging depth and high contrast.However,limited by the equipment cost and reconstruction time requirements,the existing PAI systems distributed with annular array transducers are difficult to take into account both the image quality and the imaging speed.In this paper,a triple-path feature transform network(TFT-Net)for ring-array photoacoustic tomography is proposed to enhance the imaging quality from limited-view and sparse measurement data.Specifically,the network combines the raw photoacoustic pressure signals and conventional linear reconstruction images as input data,and takes the photoacoustic physical model as a prior information to guide the reconstruction process.In addition,to enhance the ability of extracting signal features,the residual block and squeeze and excitation block are introduced into the TFT-Net.For further efficient reconstruction,the final output of photoacoustic signals uses‘filter-then-upsample’operation with a pixel-shuffle multiplexer and a max out module.Experiment results on simulated and in-vivo data demonstrate that the constructed TFT-Net can restore the target boundary clearly,reduce background noise,and realize fast and high-quality photoacoustic image reconstruction of limited view with sparse sampling.

The sparse array elements selection in sparse imaging of circular-array photoacoustic tomography

Photoacoustic imaging(PAI)has been developed,and photoacoustic computed tomography(PACT)is widely used for in vivo tissue and mouse imaging.Simulated annealing(SA)algorithm solves optimization problems,and compressed sensing(CS)recovers sparse signals from undersampled measurements.We aim to develop an advanced sparse imaging framework for PACT,which invloves the use of SA to¯nd an optimal sparse array element distribution and CS to perform sparse imaging.PACT reconstructions were performed using a dummy and porcine liver phantoms.Compared to traditional sparse reconstruction algorithms,the proposed method recovers signals using few ultrasonic transducer elements,enabling high-speed,low-cost PACT for practical application.