In the medical field, 3D ultrasound reconstruction can visualize the internal structure of patients, which is very important for doctors to carry out correct analyses and diagnoses. Furthermore, medical 3D ultrasound images have been widely used in clinical disease diagnosis because they can more intuitively display the characteristics and spatial location information of the target. The traditional way to obtain 3D ultrasonic images is to use a 3D ultrasonic probe directly. Although freehand 3D ultrasound reconstruction is still in the research stage, a lot of research has recently been conducted on the freehand ultrasound reconstruction method based on wireless ultrasonic probe. In this paper, a wireless linear array probe is used to build a freehand acousto-optic positioning 3D ultrasonic imaging system. B-scan is considered the brightness scan. It is used for producing a 2D cross-section of the eye and its orbit. This system is used to collect and construct multiple 2D B-scans datasets for experiments. According to the experimental results, a freehand 3D ultrasonic reconstruction method based on depth learning is proposed, which is called sequence prediction reconstruction based on acoustic optical localization (SPRAO). SPRAO is an ultrasound reconstruction system which cannot be put into medical clinical use now. Compared with 3D reconstruction using a 3D ultrasound probe, SPRAO not only has a controllable scanning area, but also has a low cost. SPRAO solves some of the problems in the existing algorithms. Firstly, a 60 frames per second (FPS) B-scan sequence can be synthesized using a 12 FPS wireless ultrasonic probe through 2–3 acquisitions. It not only effectively reduces the requirement for the output frame rate of the ultrasonic probe, but also increases the moving speed of the wireless probe. Secondly, SPRAO analyzes the B-scans through speckle decorrelation to calibrate the acousto-optic auxiliary positioning information, while other algorithms have no solution to the cumulative error of the external auxiliary positioning device. Finally, long short-term memory (LSTM) is used to predict the spatial position and attitude of B-scans, and the calculation of pose deviation and speckle decorrelation is integrated into a 3D convolutional neural network (3DCNN). Prepare for real-time 3D reconstruction under the premise of accurate spatial pose of B-scans. At the end of this paper, SPRAO is compared with linear motion, IMU, speckle decorrelation, CNN and other methods. From the experimental results, it can be observed that the spatial pose deviation of B-scans output using SPRAO is the best of these methods.

中文翻译：

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一种基于深度学习的徒手三维超声重建方法

在医疗领域，3D超声重建可以将患者的内部结构可视化，这对于医生进行正确的分析和诊断非常重要。此外，医学三维超声图像因其能够更直观地显示目标的特征和空间位置信息而被广泛应用于临床疾病诊断。获取3D超声图像的传统方式是直接使用3D超声探头。虽然徒手3D超声重建还处于研究阶段，但最近对基于无线超声探头的徒手超声重建方法进行了大量研究。本文采用无线线阵探头搭建徒手声光定位三维超声成像系统。B扫描被认为是亮度扫描。它用于生成眼睛及其眼眶的二维横截面。该系统用于收集和构建用于实验的多个 2D B 扫描数据集。根据实验结果，提出了一种基于深度学习的徒手三维超声重建方法，称为基于声光定位的序列预测重建（SPRAO）。SPRAO是一种超声重建系统，目前还不能投入医学临床使用。与使用3D超声探头进行3D重建相比，SPRAO不仅扫描面积可控，而且成本低。SPRAO 解决了现有算法中的一些问题。首先，可以使用 12 FPS 无线超声波探头通过 2-3 次采集合成每秒 60 帧 (FPS) B 扫描序列。不仅有效降低了对超声探头输出帧率的要求，而且提高了无线探头的移动速度。其次，SPRAO通过散斑去相关分析B-scan来标定声光辅助定位信息，而其他算法对外部辅助定位设备的累积误差无解。最后，使用长短期记忆（LSTM）来预测B扫描的空间位置和姿态，并将位姿偏差和散斑去相关的计算集成到3D卷积神经网络（3DCNN）中。在 B 扫描空间姿态准确的前提下，为实时 3D 重建做准备。文末将SPRAO与线性运动、IMU、散斑解相关、CNN等方法进行了对比。