LiteMamba-Synth: lightweight state space models for efficient 3T-to-7T MRI translation

The superior clinical utility of 7T magnetic resonance imaging (MRI) is constrained by high acquisition costs and limited scanner availability. While deep learning-based 3T-to-7T synthesis offers a potential solution, prevailing models typically rely on heavy parameterization, which increases computational redundancy and risk of overfitting on restricted medical datasets. In this paper, we focus on model efficiency and propose LiteMamba-Synth, an architecturally streamlined state space framework designed for high-fidelity MRI translation with minimal resource requirements.

Our core contribution is the integration of the ConvMamba block, which utilizes the linear-time complexity of State Space Models (SSMs) to capture expansive spatial dependencies without the prohibitive computational overhead of traditional attention mechanisms. To preserve essential anatomical details during the compression of the feature space, we introduce the Wavelet-Enhanced Skip connection (WES), a module that facilitates multi-scale frequency-domain feature fusion to safeguard high-frequency textures and edge information. Additionally, a lightweight Convolutional Block Attention Module (CBAM) is incorporated to adaptively recalibrate feature responses toward salient neuroanatomical regions.

Experimental results on the UNC T1w dataset demonstrate that LiteMamba-Synth achieves a competitive PSNR of 20.82 dB and an SSIM of 0.711. Crucially, our model maintains a compact footprint of merely 2.15 million parameters, representing a substantial reduction in complexity compared to contemporary deep learning baselines.

By delivering high-quality synthesis results with minimal parameter overhead, LiteMamba-Synth provides a practical and scalable solution for deploying advanced MRI synthesis in resource-constrained clinical environments.