Current approaches for the synthesis of MR images are only trained on MR images with a specific set of acquisition parameter values, limiting the clinical value of these methods. We therefore trained a generative adversarial network (GAN) to generate synthetic MR knee images conditioned on various acquisition parameters (TR, TE, imaging orientation). This enables us to synthesize MR images with adjustable image contrast. This work can support radiologists and technologists during the parameterization of MR sequences, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.
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