  In recent years, deep learning models comprising transformer components have
pushed the performance envelope in medical image synthesis tasks. Contrary to
convolutional neural networks (CNNs) that use static, local filters,
transformers use self-attention mechanisms to permit adaptive, non-local
filtering to sensitively capture long-range context. However, this sensitivity
comes at the expense of substantial model complexity, which can compromise
learning efficacy particularly on relatively modest-sized imaging datasets.
Here, we propose a novel adversarial model for multi-modal medical image
synthesis, I2I-Mamba, that leverages selective state space modeling (SSM) to
efficiently capture long-range context while maintaining local precision. To do
this, I2I-Mamba injects channel-mixed Mamba (cmMamba) blocks in the bottleneck
of a convolutional backbone. In cmMamba blocks, SSM layers are used to learn
context across the spatial dimension and channel-mixing layers are used to
learn context across the channel dimension of feature maps. Comprehensive
demonstrations are reported for imputing missing images in multi-contrast MRI
and MRI-CT protocols. Our results indicate that I2I-Mamba offers superior
performance against state-of-the-art CNN- and transformer-based methods in
synthesizing target-modality images.
