Fully Guided Neural Schrödinger bridge for Brain MR image synthesis

Multi-modal brain MRI provides essential complementary information for clinical diagnosis. However, acquiring all modalities is often challenging due to time and cost constraints. To address this, various methods have been proposed to generate missing modalities from available ones. Traditional approaches can be broadly categorized into two main types: paired and unpaired methods. While paired methods offer superior performance, obtaining large-scale paired datasets is challenging in real-world scenarios. Conversely, unpaired methods facilitate large-scale data collection but struggle to preserve critical image features, such as tumors. In this paper, we propose Fully Guided Schr\"odinger Bridges (FGSB), a novel framework based on Neural Schr\"odinger Bridges, to overcome these limitations. FGSB achieves stable, high-quality generation of missing modalities using minimal paired data. Furthermore, when provided with ground truth or a segmentation network for specific regions, FGSB can generate missing modalities while preserving these critical areas with reduced data requirements. Our proposed model consists of two consecutive phases. 1) Generation Phase: Fuses a generated image, a paired reference image, and Gaussian noise, employing iterative refinement to mitigate issues such as mode collapse and improve generation quality 2) Training Phase: Learns the mapping from the generated image to the target modality. Experiments demonstrate that FGSB achieves comparable generation performance to methods trained on large datasets, while using data from only two subjects. Moreover, the utilization of lesion information with FGSB significantly enhances its ability to preserve crucial lesion features.