  We present a novel preprocessing and prediction pipeline for the
classification of magnetic resonance imaging (MRI) that takes advantage of the
information rich complex valued k-Space. Using a publicly available MRI raw
dataset with 312 subject and a total of 9508 slices, we show the advantage of
utilizing the k-Space for better prostate cancer likelihood estimation in
comparison to just using the magnitudinal information in the image domain, with
an AUROC of $86.1\%\pm1.8\%$. Additionally, by using high undersampling rates
and a simple principal component analysis (PCA) for coil compression, we reduce
the time needed for reconstruction by avoiding the time intensive GRAPPA
reconstruction algorithm. By using digital undersampling for our experiments,
we show that scanning and reconstruction time could be reduced. Even with an
undersampling factor of 16, our approach achieves meaningful results, with an
AUROC of $71.4\%\pm2.9\%$, using the PCA coil combination and taking into
account the k-Space information. With this study, we were able to show the
feasibility of preserving phase and k-Space information, with consistent
results. Besides preserving valuable information for further diagnostics, this
approach can work without the time intensive ADC and reconstruction
calculations, greatly reducing the post processing, as well as potential
scanning time, increasing patient comfort and allowing a close to real-time
prediction.
