Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518055, China.
Ultrasonics. 2024 Sep;143:107405. doi: 10.1016/j.ultras.2024.107405. Epub 2024 Jul 20.
Transcranial ultrasound imaging presents a significant challenge due to the intricate interplay between ultrasound waves and the heterogeneous human skull. The skull's presence induces distortion, refraction, multiple scattering, and reflection of ultrasound signals, thereby complicating the acquisition of high-quality images. Extracting reflections from the entire waveform is crucial yet exceedingly challenging, as intracranial reflections are often obscured by strong amplitude direct waves and multiple scattering. In this paper, a multiple wave suppression method for ultrasound plane wave imaging is proposed to mitigate the impact of skull interference. Drawing upon prior research, we developed an enhanced high-resolution linear Radon transform using the maximum entropy principle and Bayesian method, facilitating wavefield separation. We detailed the process of wave field separation in the Radon domain through simulation of a model with a high velocity layer. When plane waves emitted at any steering angles, both multiple waves and first arrival waves manifested as distinct energy points. In the brain simulation, we contrasted the characteristic differences between skull reflection and brain-internal signal in Radon domain, and demonstrated that multiples suppression method reduces side and grating lobe levels by approximately 30 dB. Finally, we executed in vitro experiments using a monkey skull to separate weak intracranial reflection signals from strong skull reflections, enhancing the contrast-to-noise ratio by 85 % compared to conventional method using full waveform. This study deeply explores the effect of multiples on effective signal separation, addresses the complexity of wavefield separation, and verifies its efficacy through imaging, thereby significantly advancing ultrasound transcranial imaging techniques.
经颅超声成像是一项具有挑战性的任务，因为超声波与不均匀的人类颅骨之间存在复杂的相互作用。颅骨的存在会导致超声波信号的失真、折射、多次散射和反射，从而使高质量图像的获取变得复杂。从整个波形中提取反射波是至关重要的，但也极具挑战性，因为颅内反射波常常被强振幅直达波和多次散射所掩盖。本文提出了一种用于超声平面波成像的多波抑制方法，以减轻颅骨干扰的影响。基于先前的研究，我们使用最大熵原理和贝叶斯方法开发了一种增强的高分辨率线性 Radon 变换，以实现波场分离。我们通过模拟具有高速层的模型，详细描述了 Radon 域中的波场分离过程。当平面波以任意转向角发射时，多波和首波都表现为明显的能量点。在大脑模拟中，我们比较了颅骨反射和大脑内部信号在 Radon 域中的特征差异，表明多波抑制方法将旁瓣和栅瓣电平降低了约 30dB。最后，我们使用猴颅骨进行了体外实验，成功地从强颅骨反射中分离出了较弱的颅内反射信号，与传统的全波方法相比，对比度噪声比提高了 85%。本研究深入探讨了多次反射对有效信号分离的影响，解决了波场分离的复杂性问题，并通过成像验证了其有效性，从而极大地推动了超声经颅成像技术的发展。
