A method of ultrasound simulation from patient-specific CT image data: A preliminary simulation study
04 Apr 2018-pp 1483-1486
TL;DR: This paper proposes combining the traditional convolution model for scatter map simulation with ray tracing approaches to simulate an ultrasound image and suggests that using Convolution model instead of Field II reduces significantly the computational time without affecting the image quality.
Abstract: Ultrasound imaging is one of the most preferred modalities for image-guided procedures due to its low-cost, non-ionizing nature, and real-time capability. However, if patient-specific ultrasound images can be simulated before the actual procedure, it may aid in better implementation of the procedure planned using pre-planning image data. For this reason, ultrasound simulations from CT data have gained much interest over the past few years. Recent approaches combine ultrasound echo reflection image, intensity transmission map, and scatter image of the region of interest to form the final ultrasound image. However, the scatter image is simulated from Field II, which is computationally very intensive. In this paper, we propose combining the traditional convolution model for scatter map simulation with ray tracing approaches to simulate an ultrasound image. The obtained results suggest that using convolution model instead of Field II reduces significantly the computational time without affecting the image quality.
26 Aug 2019
TL;DR: An artificial intelligence-based ultrasound simulator suitable for medical simulation and clinical training is presented and it is found that the GAN-based simulator can generate B-mode images following Rayleigh scattering.
Abstract: This paper presents an artificial intelligence-based ultrasound simulator suitable for medical simulation and clinical training. Particularly, we propose a machine learning approach to realistically simulate ultrasound images based on generative adversarial networks (GANs). Using B-mode ultrasound images simulated by a known ultrasound simulator, Field II, an "image-to-image" ultrasound simulator was trained. Then, through evaluations, we found that the GAN-based simulator can generate B-mode images following Rayleigh scattering. Our preliminary study demonstrated that ultrasound B-mode images from anatomies inferred from magnetic resonance imaging (MRI) data were feasible. While some image blurring was observed, ultrasound B- mode images obtained were both visually and quantitatively comparable to those obtained using the Field II simulator. It is also important to note that the GAN-based ultrasound simulator was computationally efficient and could achieve a frame rate of 15 frames/second using a regular laptop computer. In the future, the proposed GAN-based simulator will be used to synthesize more realistic looking ultrasound images with artifacts such as shadowing.
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