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APPLICATION OF MACHINE LEARNING TO ITERATIVE AND MULTIMODALITY IMAGE RECONSTRUCTION

机译:机器学习在迭代和多模图像重建中的应用

摘要

A method for machine learning based ultrasound image reconstruction can include receiving, at a reconstruction engine, imaging data; generating an initial estimate for a transmission image via a neural network trained (machine or self-learning) on paired transmission ultrasound and reflection ultrasound data; and performing image reconstruction using the initial estimate to generate transmission ultrasound images. The image reconstruction can generate higher quality transmission ultrasound when carried out by using the initial estimate as the starting point for iterative image reconstruction and using transmission data obtained via conventional transmission ultrasound frequencies (e.g. from 0.8 MHz to 1.5 MHz).
机译:一种基于机器学习的超声图像重建方法可以包括在重建引擎上接收成像数据; 通过成对传输超声和反射超声数据的神经网络训练(机器或自学习)生成透射图像的初始估计; 使用初始估计执行以生成传输超声图像的图像重建。 当通过使用初始估计作为迭代图像重建的起始点并且使用通过传统传输超声频率(例如0.8MHz至1.5MHz)而执行的初始估计,图像重建可以产生更高质量的传输超声。

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