  Purpose
  A Magnetic Resonance Imaging (MRI) exam typically consists of several
sequences that yield different image contrasts. Each sequence is parameterized
through multiple acquisition parameters that influence image contrast,
signal-to-noise ratio, acquisition time, and/or resolution. Depending on the
clinical indication, different contrasts are required by the radiologist to
make a diagnosis. As MR sequence acquisition is time consuming and acquired
images may be corrupted due to motion, a method to synthesize MR images with
adjustable contrast properties is required.
  Methods
  Therefore, we trained an image-to-image generative adversarial network
conditioned on the MR acquisition parameters repetition time and echo time. Our
approach is motivated by style transfer networks, whereas the "style" for an
image is explicitly given in our case, as it is determined by the MR
acquisition parameters our network is conditioned on.
  Results
  This enables us to synthesize MR images with adjustable image contrast. We
evaluated our approach on the fastMRI dataset, a large set of publicly
available MR knee images, and show that our method outperforms a benchmark
pix2pix approach in the translation of non-fat-saturated MR images to
fat-saturated images. Our approach yields a peak signal-to-noise ratio and
structural similarity of 24.48 and 0.66, surpassing the pix2pix benchmark model
significantly.
  Conclusion
  Our model is the first that enables fine-tuned contrast synthesis, which can
be used to synthesize missing MR contrasts or as a data augmentation technique
for AI training in MRI.
