H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4 DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 µm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R² > 0.85, p < 0.03). The result of scatterer size estimation reveals that a CNN can achieve an average mapping accuracy of 93.3%. Overall, our preliminary in vitro findings reveal that 3-D H-scan US imaging allows the visualization of tissue scatterer patterns and incorporation of a CNN can be used to help estimate size of the acoustic scattering objects.

H-Scan 超声 (US) 是一种新的成像技术，可估计声散射物体和结构的相对大小。本研究的目的是介绍一种三维 (3-D) H 扫描 US 成像方法，用于估计体积空间中的散射体大小。使用配备定制体积成像换能器（4 DL7，Vermon，Tours，France）的可编程研究扫描仪（Vantage 256，Verasonics Inc，Kirkland，法国），收集原始射频（RF）数据用于离线处理以生成 H - 扫描美国卷。一个深度卷积神经网络 (CNN) 被修改并用于实现从输入 H 扫描 US 图像到底层散射体大小的体素映射。使用嵌入不同大小（15 至 250 μm）和浓度（0.1 至 1%）的声散射体的均质明胶基组织模拟体模材料进行了初步研究。两个额外的体模嵌入了 63 或 125 µm 大小的微球，用于测试 CNN 估计精度。体外结果表明，3-D H-scan US 成像可以可视化不同浓度下不同大小的声散射体的空间分布 ( R ² > 0.85, p < 0.03)。散射体大小估计的结果表明，CNN 可以达到 93.3% 的平均映射精度。总体而言，我们的初步体外研究结果表明，3-D H 扫描 US 成像允许组织散射模式的可视化，并且可以使用 CNN 来帮助估计声学散射对象的大小。