论文标题
深层生成模型实时模拟2D患者特定的超声图像
Deep Generative Models to Simulate 2D Patient-Specific Ultrasound Images in Real Time
论文作者
论文摘要
我们提出了一种计算方法，用于对2D超声（US）图像的实时，特定于患者的模拟。该方法使用大量跟踪的超声图像来学习一个函数，该功能将传感器的位置和方向映射到超声图像。这是迈向现实的患者模拟的第一步，可以改善对复杂病例的培训和回顾性检查。我们的模型可以在4ms以下（在实时约束中）中模拟2D图像，并产生模拟图像，以保留真实超声图像的内容（解剖结构和人工制品）。
We present a computational method for real-time, patient-specific simulation of 2D ultrasound (US) images. The method uses a large number of tracked ultrasound images to learn a function that maps position and orientation of the transducer to ultrasound images. This is a first step towards realistic patient-specific simulations that will enable improved training and retrospective examination of complex cases. Our models can simulate a 2D image in under 4ms (well within real-time constraints), and produce simulated images that preserve the content (anatomical structures and artefacts) of real ultrasound images.
