HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity
  Synthesis of MR Images with Structure Preservation
Ziqi Yu Botao Zhao Shengjie Zhang Xiang Chen Jianfeng Feng Tingying Peng Xiao-Yong Zhang
Synthesizing medical images while preserving their structural information is
crucial in medical research. In such scenarios, the preservation of anatomical
content becomes especially important. Although recent advances have been made
by incorporating instance-level information to guide translation, these methods
overlook the spatial coherence of structural-level representation and the
anatomical invariance of content during translation. To address these issues,
we introduce hierarchical granularity discrimination, which exploits various
levels of semantic information present in medical images. Our strategy utilizes
three levels of discrimination granularity: pixel-level discrimination using a
Brain Memory Bank, structure-level discrimination on each brain structure with
a re-weighting strategy to focus on hard samples, and global-level
discrimination to ensure anatomical consistency during translation. The image
translation performance of our strategy has been evaluated on three independent
datasets (UK Biobank, IXI, and BraTS 2018), and it has outperformed
state-of-the-art algorithms. Particularly, our model excels not only in
synthesizing normal structures but also in handling abnormal (pathological)
structures, such as brain tumors, despite the variations in contrast observed
across different imaging modalities due to their pathological characteristics.
The diagnostic value of synthesized MR images containing brain tumors has been
evaluated by radiologists. This indicates that our model may offer an
alternative solution in scenarios where specific MR modalities of patients are
unavailable. Extensive experiments further demonstrate the versatility of our
method, providing unique insights into medical image translation.