A Freehand 3D Ultrasound Reconstruction Method Based on Deep Learning

Electronics(2023)

引用 2|浏览15
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摘要
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.
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关键词
freehand ultrasound scanning,position tracking technology,ultrasound,3D reconstruction,deep learning
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