3-D Super-Resolution Ultrasound (SR-US) Imaging with a 2-D Sparse Array
S. Harput, K. Christensen-Jeffries, A. Ramalli, J. Brown, J. Zhu, G. Zhang, C. H. Leow, M. Toulemonde, E. Boni, P. Tortoli, R. J. Eckersley, C. Dunsby, M-X. Tang
High frame rate 3-D ultrasound imaging technology combined with super-resolution processing method can visualize 3-D microvascular structures by overcoming the diffraction limited resolution in every spatial direction. However, 3-D super-resolution ultrasound imaging using a full 2-D array requires a system with large number of independent channels, the design of which might be impractical due to the high cost, complexity, and volume of data produced.
In this study, a 2-D sparse array was designed and fabricated with 512 elements chosen from a density-tapered 2-D spiral layout. High frame rate volumetric imaging was performed using two synchronized ULA-OP 256 research scanners. Volumetric images were constructed by coherently compounding 9-angle plane waves acquired in 3 milliseconds at a pulse repetition frequency of 3000 Hz. To allow microbubbles sufficient time to move between consequent compounded volumetric frames, a 7-millisecond delay was introduced after each volume acquisition. This reduced the effective volume acquisition speed to 100 Hz and the total acquired data size by 3.3-fold. Localization-based 3-D super-resolution images of two touching sub-wavelength tubes were generated from 6000 volumes acquired in 60 seconds. In conclusion, this work demonstrates the feasibility of 3D super-resolution imaging and super-resolved velocity mapping using a customized 2D sparse array transducer.
Inverse Problem Approach to Aberration Correction for in vivo Transcranial Imaging Based on a Sparse Representation of Contrast-enhanced Ultrasound Data
Polytechnique Montréal（蒙特利尔高等理工学院）；Université de Montréal（蒙特利尔大学）；Montreal Heart Institute（蒙特利尔心脏研究所）
Full-wave nonlinear ultrasound simulation on distributed clusters with applications in high-intensity focused ultrasound
高强度聚焦超声（HIFU）的基于模型的治疗计划和曝光量测量需要数值模拟非线性超声通过异质和吸收介质的传播。由于超声波相对于最高频率谐波的波长行进的距离很大，这是一个需要计算的问题。在这里，k-空间伪谱方法是用来解决一组耦合偏微分方程相当于一个广义Westerpyruvate方程。该模型在C++中实现，并使用消息传递接口（MPI）并行化，用于解决分布式集群上的大规模问题。使用1D平板分解来划分域，并且使用稀疏通信模式来执行全局通信。在转置空间中执行空间频率域中的操作以减少由3D快速傅立叶变换施加的通信负担。该模型的性能评估使用网格大小高达4096 x 2048 x 2048网格点分布在一个集群使用多达1024个计算核心。考虑到梯度计算的全局性质，该模型显示出良好的强缩放行为，每当内核数量增加一倍时，速度将提高1.7倍。这意味着大规模模拟可以分布在集群上的大量核心上，以相对较小的计算开销最大限度地减少执行时间。该模型的有效性证明了通过模拟超声束图案的HIFU超声处理的肾脏。
Deep-Learning Based Super-Resolution Functional Ultrasound Imaging of Transient Brain-Wide Neurovascular Activity on a Microscopic Scale
College of Biomedical Engineering, Fudan University（复旦大学 biomedical 工程学院）；Department of Neurosurgery, Huashan Hospital, Institutes for Translational Brain Research, Fudan University（复旦大学华山医院神经外科、转化脑研究院）；School of Psychology, Institute of Chinese Cultural Psychology, Shanghai Jiao Tong University（上海交通大学心理学学院、中国文化心理研究所）
Automatic Aberration Correction for Transcranial Functional and Super-Resolution Ultrasound Imaging in Rodents and Nonhuman Primates
Polytechnique Montréal（蒙特利尔高等工程学院）；Université de Montréal（蒙特利尔大学）；Centre interdisciplinaire de recherche sur le cerveau et l’apprentissage (CIRCA)（大脑与学习跨学科研究中心）；Montreal Heart Institute（蒙特利尔心脏研究所）
