Method for rebuilding photoacoustic imaging image based on sparse coefficient p norm and total vibration parameter of image

Abstract

The invention belongs to the technical field of photoacoustic imaging, and particularly relates to a method for rebuilding a photoacoustic imaging image based on a sparse coefficient p norm and a total vibration parameter of the image. According to the method, the sparse representation coefficient p (0<p<1) norm and the total vibration parameter of the image are calculated to conduct iterative correction on the image, the sparse representation coefficient p (0<p<1) norm and the total vibration parameter are recalculated according to the iterated image, iteration is conducted repeatedly until an end condition is reached, and finally a rebuilt photoacoustic image is obtained. The method can improve the quality of the rebuilt photoacoustic image, is high in convergence speed, and has practical significance on the rebuilding of the photoacoustic imaging image.

Description

technical field [0001] The invention belongs to the technical field of photoacoustic imaging, in particular to a method based on image sparse coefficient p Image reconstruction methods for photoacoustic imaging with norm and total variation parameters. technical background [0002] Photoacoustic imaging is a new medical imaging method based on the photoacoustic effect, which combines the high contrast characteristics of optical imaging and the high imaging depth of ultrasonic imaging, and because of its non-ionizing wave characteristics, it will not be in the detection process harm to human body [1,2] . Now the main application areas are breast tumor detection [3] , Angiography [4] and brain injury detection [5] Wait. At the same time, because the formed image is the light absorption characteristics of human tissue, it reflects the pathological characteristics of the imaging tissue related to the light absorption characteristics to a certain extent. [2] . Taking adva...

AI Technical Summary

Problems solved by technology

The above method can directly obtain the result without iteration, but the accuracy of reconstructing the image is not high when the sampling points are sparse, and is limited by a specific scanning method

Aiming at this phenomenon, various iterative reconstruction methods have been proposed [12-14] , the advantage of these methods is that the imaging accuracy is high and not affected by the scanning method, but the disadvantage is that the speed is slow

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