  Data-driven automatic approaches have demonstrated their great potential in
resolving various clinical diagnostic dilemmas in neuro-oncology, especially
with the help of standard anatomic and advanced molecular MR images. However,
data quantity and quality remain a key determinant of, and a significant limit
on, the potential of such applications. In our previous work, we explored
synthesis of anatomic and molecular MR image network (SAMR) in patients with
post-treatment malignant glioms. Now, we extend it and propose Confidence
Guided SAMR (CG-SAMR) that synthesizes data from lesion information to
multi-modal anatomic sequences, including T1-weighted (T1w), gadolinium
enhanced T1w (Gd-T1w), T2-weighted (T2w), and fluid-attenuated inversion
recovery (FLAIR), and the molecular amide proton transfer-weighted (APTw)
sequence. We introduce a module which guides the synthesis based on confidence
measure about the intermediate results. Furthermore, we extend the proposed
architecture for unsupervised synthesis so that unpaired data can be used for
training the network. Extensive experiments on real clinical data demonstrate
that the proposed model can perform better than the state-of-theart synthesis
methods.
