  A Magnetic Resonance Imaging (MRI) exam typically consists of the acquisition
of multiple MR pulse sequences, which are required for a reliable diagnosis.
Each sequence can be parameterized through multiple acquisition parameters
affecting MR image contrast, signal-to-noise ratio, resolution, or scan time.
With the rise of generative deep learning models, approaches for the synthesis
of MR images are developed to either synthesize additional MR contrasts,
generate synthetic data, or augment existing data for AI training. However,
current generative approaches for the synthesis of MR images are only trained
on images with a specific set of acquisition parameter values, limiting the
clinical value of these methods as various sets of acquisition parameter
settings are used in clinical practice. Therefore, we trained a generative
adversarial network (GAN) to generate synthetic MR knee images conditioned on
various acquisition parameters (repetition time, echo time, image orientation).
This approach enables us to synthesize MR images with adjustable image
contrast. In a visual Turing test, two experts mislabeled 40.5% of real and
synthetic MR images, demonstrating that the image quality of the generated
synthetic and real MR images is comparable. This work can support radiologists
and technologists during the parameterization of MR sequences by previewing the
yielded MR contrast, can serve as a valuable tool for radiology training, and
can be used for customized data generation to support AI training.
