Exploring the Potential of Generative Adversarial Networks for Synthesizing Radiological Images of the Spine to be Used in In Silico Trials

Frontiers in Bioengineering and Biotechnology
Fabio GalbuseraHans-Joachim Wilke

Abstract

In silico trials recently emerged as a disruptive technology, which may reduce the costs related to the development and marketing approval of novel medical technologies, as well as shortening their time-to-market. In these trials, virtual patients are recruited from a large database and their response to the therapy, such as the implantation of a medical device, is simulated by means of numerical models. In this work, we propose the use of generative adversarial networks to produce synthetic radiological images to be used in in silico trials. The generative models produced credible synthetic sagittal X-rays of the lumbar spine based on a simple sketch, and were able to generate sagittal radiological images of the trunk using coronal projections as inputs, and vice versa. Although numerous inaccuracies in the anatomical details may still allow distinguishing synthetic and real images in the majority of cases, the present work showed that generative models are a feasible solution for creating synthetic imaging data to be used in in silico trials of novel medical devices.

References

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Citations

Jul 10, 2019·International Journal of Computer Assisted Radiology and Surgery·Oleksandra TmenovaLuc Duong
Nov 1, 2018·European Radiology Experimental·Fabio GalbuseraLuca Maria Sconfienza
Sep 16, 2019·Medical Image Analysis·Xin YiPaul Babyn
Mar 12, 2021·Frontiers in Big Data·Luise Gootjes-DreesbachHolger Fröhlich
Jun 18, 2021·Radiology. Artificial Intelligence·YiRang ShinYoung Han Lee

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Methods Mentioned

BETA
X-ray

Software Mentioned

Tensorflow

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