  Data-driven automatic approaches have demonstrated their great potential in
resolving various clinical diagnostic dilemmas for patients with malignant
gliomas in neuro-oncology with the help of conventional and advanced molecular
MR images. However, the lack of sufficient annotated MRI data has vastly
impeded the development of such automatic methods. Conventional data
augmentation approaches, including flipping, scaling, rotation, and distortion
are not capable of generating data with diverse image content. In this paper,
we propose a method, called synthesis of anatomic and molecular MR images
network (SAMR), which can simultaneously synthesize data from arbitrary
manipulated lesion information on multiple anatomic and molecular MRI
sequences, including T1-weighted (T1w), gadolinium enhanced T1w (Gd-T1w),
T2-weighted (T2w), fluid-attenuated inversion recovery (FLAIR), and amide
proton transfer-weighted (APTw). The proposed framework consists of a
stretch-out up-sampling module, a brain atlas encoder, a segmentation
consistency module, and multi-scale label-wise discriminators. Extensive
experiments on real clinical data demonstrate that the proposed model can
perform significantly better than the state-of-the-art synthesis methods.
