CodeBrain: Imputing Any Brain MRI via Modality- and Instance-Specific Codes
This addresses the need for adaptable brain MRI imputation in medical imaging, though it appears incremental as it builds on existing unified methods with a novel pipeline.
The paper tackles the problem of unified MRI imputation to reduce scanning costs and improve clinical diagnosis by proposing CodeBrain, a two-stage framework that reframes inter-modality transformations as a full-modality code prediction task. It outperforms state-of-the-art methods on two public brain MRI datasets (IXI and BraTS 2023), setting a new benchmark.
Unified MRI imputation, which can adapt to diverse imputation scenarios, is highly desirable as it reduces scanning costs and provides comprehensive MRI information for improved clinical diagnosis. Existing unified MRI imputation methods either rely on specific prompts to guide their transformation network or require multiple modality-specific modules. However, these approaches struggle to capture large modality and instance variations or become too complex to generalize effectively. To address these limitations, we propose CodeBrain, a fundamentally different pipeline for unified brain MRI imputation. Our key idea is to reframe various inter-modality transformations as a full-modality code prediction task via a two-stage framework. In the first stage, CodeBrain reconstructs a target modality from any other modalities by learning a compact scalar-quantized code for each instance and modality. Any target modality can then be reconstructed with high fidelity by combining the corresponding code with shared features extracted from any available modality. In the second stage, a projection encoder is trained to predict full-modality compact codes from any incomplete MRI samples, effectively simulating various imputation scenarios. We evaluate our CodeBrain on two public brain MRI datasets (i.e., IXI and BraTS 2023). Extensive experiments demonstrate that CodeBrain outperforms state-of-the-art methods, setting a new benchmark for unified brain MRI imputation. Our code will be released.