  In this paper, we address the problem of synthesizing multi-parameter
magnetic resonance imaging (mp-MRI) data, i.e. Apparent Diffusion Coefficients
(ADC) and T2-weighted (T2w), containing clinically significant (CS) prostate
cancer (PCa) via semi-supervised adversarial learning. Specifically, our
synthesizer generates mp-MRI data in a sequential manner: first generating ADC
maps from 128-d latent vectors, followed by translating them to the T2w images.
The synthesizer is trained in a semisupervised manner. In the supervised
training process, a limited amount of paired ADC-T2w images and the
corresponding ADC encodings are provided and the synthesizer learns the paired
relationship by explicitly minimizing the reconstruction losses between
synthetic and real images. To avoid overfitting limited ADC encodings, an
unlimited amount of random latent vectors and unpaired ADC-T2w Images are
utilized in the unsupervised training process for learning the marginal image
distributions of real images. To improve the robustness of synthesizing, we
decompose the difficult task of generating full-size images into several
simpler tasks which generate sub-images only. A StitchLayer is then employed to
fuse sub-images together in an interlaced manner into a full-size image. To
enforce the synthetic images to indeed contain distinguishable CS PCa lesions,
we propose to also maximize an auxiliary distance of Jensen-Shannon divergence
(JSD) between CS and nonCS images. Experimental results show that our method
can effectively synthesize a large variety of mpMRI images which contain
meaningful CS PCa lesions, display a good visual quality and have the correct
paired relationship. Compared to the state-of-the-art synthesis methods, our
method achieves a significant improvement in terms of both visual and
quantitative evaluation metrics.
