A novel transcranial ultrasound imaging method with diverging wave transmission and deep learning approach. (April 2020)

• Record Type: Journal Article
• Authors: Du, Bin; Wang, Jinyan; Zheng, Haoteng; Xiao, Chenhui; Fang, Siyuan; Lu, Minhua; Mao, Rui
• Is Part Of: Computer methods and programs in biomedicine. Volume 186(2020)
• Publication Date: 2020-04
• Subjects: Transcranial ultrasound imaging -- Deep learning -- Coherence diverging wave compounding -- Adaptive beamforming; CT computed tomography -- DW diverging wave -- CF coherence factor-based -- STSCF spatiotemporally smoothed coherence factor -- MV minimum variance -- FWHM full width at half maximum -- PSF point spread function -- CR contrast rate -- CNN convolutional neural networks; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.105308
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An ultrafast transcranial ultrasound imaging method with diverging wave transmission was proposed for real time brain imaging. Adaptive beamforming and coherent spatial compounding techniques ensured improvement of ultrasound imaging quality, especially image contrast and lateral resolution. Deep learning approach with U-net neural network was employed to detect the contour and position of the skull directly from the acquired RF signals. Abstract: Real time brain transcranial ultrasound imaging is extremely intriguing because of its numerous applications. However, the skull causes phase distortion and amplitude attenuation of ultrasound signals due to its density: the speed of sound is significantly different in bone tissue than in soft tissue. In this study, we propose an ultrafast transcranial ultrasound imaging technique with diverging wave (DW) transmission and a deep learning approach to achieve large field-of-view with high resolution and real time brain ultrasound imaging. DW transmission provides a frame rate of several kiloHz and a large field of view that is suitable for human brain imaging via a small acoustic window. However, it suffers from poor image quality because the diverging waves are all unfocused. Here, we adopted adaptive beamforming algorithms to improve both the image contrast and the lateral resolution. Both simulated and in situ experiments with a human skull resulted in significant image improvements. However, the skull still introduces a wavefront offset and distortion, which degrades the image quality even when adaptive beamforming methods are used. Thus, we also employed a U-Net neural network to detect the contour and position of the skull directly from the acquired RF signal matrix. This approach avoids the need for beamforming, image reconstruction, and image segmentation, making it more suitable for clinical use.