Department of Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai 200438, China.
Ultrasound BU, Wuhan United Imaging Healthcare Co., Ltd., Wuhan 430206, China.
Ultrasonics. 2024 Dec;144:107430. doi: 10.1016/j.ultras.2024.107430. Epub 2024 Aug 10.
Ultrafast ultrasound Doppler imaging facilitates the assessment of cerebral hemodynamics with high spatio-temporal resolution. However, the significant acoustic impedance mismatch between the skull and soft tissue results in phase aberrations, which can compromise the quality of transcranial imaging and introduce biases in velocity and direction quantification of blood flow. This paper proposed an aberration correction method that combines deep learning-based skull sound speed modelling with ray theory to realize transcranial plane-wave imaging and ultrafast Doppler imaging. The method was validated through phantom experiments using a linear array with a center frequency of 6.25 MHz, 128 elements, and a pitch of 0.3 mm. The results demonstrated an improvement in the imaging quality of intracranial targets when using the proposed method. After aberration correction, the average locating deviation decreased from 1.40 mm to 0.27 mm in the axial direction, from 0.50 mm to 0.20 mm in the lateral direction, and the average full-width-at-half-maximum (FWHM) decreased from 1.37 mm to 0.97 mm for point scatterers. For circular inclusions, the average contrast-to-noise ratio (CNR) improved from 8.1 dB to 11.0 dB, and the average eccentricity decreased from 0.36 to 0.26. Furthermore, the proposed method was applied to transcranial ultrafast Doppler flow imaging. The results showed a significant improvement in accuracy and quality for blood Doppler flow imaging. The results in the absence of the skull were considered as the reference, and the average normalized root-mean-square errors of the axial velocity component on the five selected axial profiles were reduced from 17.67% to 8.02% after the correction.
超快速超声多普勒成像是一种高时空分辨率的脑血流动力学评估方法。然而，颅骨和软组织之间存在显著的声阻抗失配，导致相位像差，这会影响颅穿透成像的质量，并对血流速度和方向的定量分析产生偏差。本文提出了一种结合基于深度学习的颅骨声速建模和射线理论的像差校正方法，实现了颅穿透平面波成像和超快速多普勒成像。该方法通过使用中心频率为 6.25 MHz、128 个阵元和 0.3mm 间距的线性阵列进行的体模实验进行了验证。结果表明，该方法可改善颅内目标的成像质量。校正像差后，轴向定位偏差的平均值从 1.40mm 降低至 0.27mm，侧向定位偏差的平均值从 0.50mm 降低至 0.20mm，点散射体的半最大值全宽（FWHM）的平均值从 1.37mm 降低至 0.97mm。对于圆形内含物，平均对比噪声比（CNR）从 8.1dB 提高到 11.0dB，平均偏心度从 0.36 降低至 0.26。此外，该方法还应用于颅穿透超快速多普勒血流成像。结果表明，该方法显著提高了血流多普勒成像的准确性和质量。在不存在颅骨的情况下，将结果作为参考，在五个选定的轴向剖面上，轴向速度分量的平均归一化均方根误差从校正前的 17.67%降低至 8.02%。
