  Dynamic Contrast Enhanced Magnetic Resonance Imaging aids in the detection
and assessment of tumor aggressiveness by using a Gadolinium-based contrast
agent (GBCA). However, GBCA is known to have potential toxic effects. This risk
can be avoided if we obtain DCE-MRI images without using GBCA. We propose,
DCE-former, a transformer-based neural network to generate early and late
response prostate DCE-MRI images from non-contrast multimodal inputs (T2
weighted, Apparent Diffusion Coefficient, and T1 pre-contrast MRI).
Additionally, we introduce (i) a mutual information loss function to capture
the complementary information about contrast uptake, and (ii) a frequency-based
loss function in the pixel and Fourier space to learn local and global
hyper-intensity patterns in DCE-MRI. Extensive experiments show that DCE-former
outperforms other methods with improvement margins of +1.39 dB and +1.19 db in
PSNR, +0.068 and +0.055 in SSIM, and -0.012 and -0.013 in Mean Absolute Error
for early and late response DCE-MRI, respectively.
