Research On Phase Correction And Adaptive Algorithm Based On Divergent Wave Transcranial Imaging

Transcranial ultrasound is currently mainly used in both treatment and imaging.In the imaging field,it is mainly in the focus line scan mode.Due to the low imaging frame rate of the focused line scan,it cannot meet some clinical application scenarios that require a higher frame rate,such as elastography,high-speed blood flow imaging,and so on.Therefore,a high frame rate imaging method such as plane wave(PW)and diverging wave(DW)is applied to ultrasound imaging research.However,the width of the imaging field of view of a plane wave is limited by the actual line array probe width.Diverging wave ultrasound imaging is a high-frame-rate,wide-field imaging mode that is generally used for deeper tissues imaging.When diverging waves are used for transcranial ultrasound imaging,we can perform intracranial elastography imaging,blood flow imaging,and detect intracranial tumors or foreign invaders,such as bullet fragments.Among them,intracranial vascular diseases have a high incidence,while intracranial tumors and foreign invaders have a high mortality rate.Therefore,improving the quality of intracranial imaging has great clinical significance.However,due to the large differences in the sound velocity of the skull and soft tissue and the mismatch of the acoustic impedance of the skull and soft tissue,it will lead to delay error with the calculation method of traditional and refraction effect at the interface.Both of these results can cause distorted image position and reduced image quality.In order to correct the image position and improve the transcranial imaging quality of divergent waves,we designed and completed the following work.(1)We designed ultrasound transcranial experiments and simulation experiments to study the effect of skull on imaging results.(2)Our method based on deep learning detects the position and contour of the skull directly from the original ultrasound signal matrix collected.(3)we use the Multi-Template Fast Matching(MFMM)and Wavefront extrapolation(EW)algorithms to compensate the phase and correct the scattering point position.First,we designed ultrasound transcranial experiments and simulation experiments to study the effect of skull on imaging results.Thenwe detect the position and contour of the skull directly from the collected original signal matrix based on deep learning.Next,in the K-Wave tumor simulation experiment,the MFMM and EW algorithms increased the contrast from 23.9d B to 25.83 d B and 26.15 d B(improved by 1.93 d B and 2.25 d B),respectively.In verasonics transcranial experiments,the MFMM and EW algorithms reduced the full width at half maximum from 2.29 mm to 2.15 mm and 2.19mm(compressed by 0.14 mm and 0.1mm),respectively.(4)the adaptive beamforming algorithm is used to further improve the image resolution and contrast.The mainstream adaptive beamforming algorithms are mainly divided into two categories:coherence factor(CF)and minimum variance(MV).Among them,the coherent factor algorithm can significantly compress the sidelobe to improve the contrast(CR),and the minimum variance algorithm can compress the main lobe width to improve the resolution.Then,for these two types of algorithms,the angle coherence factor and the minimum variance algorithm based on the signal-to-noise ratio are developed and applied to plane wave imaging.Then the above-mentioned classic adaptive beam combining algorithm is applied to divergent wave imaging.In the end,the minimum variance combined spatial smoothing coherence factor(MV-STSCF)in the k-wave simulation experiment improved the contrast by about 3d B and compressed the full width at half maximum by about 1mm.The verasonics transcranial experiments compressed the full width at half maximum by about 1.2mm.In summary,this study cited MFMM and EW to correct the phase distortion of diverging wave transcranial imaging and improve the image quality.Use deep learning to directly and instantly detect skull information from the ultrasound signal matrix,without the need to use CT or MRI and other equipment to obtain skull information before importing the ultrasound imaging system.The real-time problem of transcranial ultrasound imaging is theoretically solved.Finally,the traditional adaptive beamforming algorithm is applied to diverging wave transcranial imaging,which further improves the image quality and enables us to diagnose intracranial diseases more accurately.In addition,based on the traditional adaptive beamforming algorithm,we innovatively propose a coherence factor in the angular domain(ACF)and a minimum variance beamforming algorithm based on the signal-to-noise ratio.The validity of the new algorithm is verified in plane wave ultrasound data.