Abstract
A Magnetic Resonance Imaging (MRI) exam typically consists of several sequences that yield different image contrasts. Each sequence is parameterized through a multitude of acquisition parameters that influence image contrast, signal-to-noise ratio, scan time and/or resolution. Depending on the clinical indication, different contrasts are required by the radiologist to make a diagnosis. As the acquisition of MR sequences is time consuming, and acquired images may be corrupted due to motion, a method to synthesize MR images with fine-tuned contrast settings is required. We therefore trained an image-to-image generative adversarial network conditioned on the MR acquisition parameters repetition and echo time. Our approach is able to synthesize missing MR images with adjustable MR image contrast and yields a mean absolute error of 0.05, a peak signal-to-noise ratio of 23.23 dB and structural similarity of 0.78.
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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Denck, J., Guehring, J., Maier, A., Rothgang, E. (2021). Acquisition Parameter-conditioned Magnetic Resonance Image-to-image Translation. In: Palm, C., Deserno, T.M., Handels, H., Maier, A., Maier-Hein, K., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2021. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-33198-6_49
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DOI: https://doi.org/10.1007/978-3-658-33198-6_49
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