CLEAR: Complex Learned Explicit Analytical Regularization for Ultra-Accelerated 4D Flow CMR Reconstruction

2026-09-19

AI Summary

为解决4D Flow CMR重建在高加速下的限制，提出CLEAR方法结合压缩感知的可解释性和学习模型的灵活性，优于现有技术并保持低参数量。

Abstract

While compressed-sensing regularizers enable interpretable reconstruction of 4D Flow CMR through transparent variational objectives, their hand-crafted nature is too restrictive under high acceleration. State-of-the-art learning-based approaches mitigate this, but typically encode regularization implicitly through unrolled network modules, which limits their interpretability. To address this limitation, we propose CLEAR, designed to combine the interpretability of compressed sensing with the flexibility of learned models. To the best of our knowledge, it is the first learned regularizer for a 4D reconstruction task. In the ultra-accelerated \(10\times\)--\(50\times\) regime of the CMRx4DFlow2026 challenge, CLEAR outperforms compressed sensing locally low-rank (LLR) and the popular variational network FlowVN, while using less than 10k parameters and preserving an interpretable regularization structure.

Problem

• compressed-sensing
• regularization
• interpretability
• learning-based

Innovation

• Learned Regularizer
• Interpretable Reconstruction
• Ultra-Accelerated 4D Flow CMR
• CLEAR
• Few Parameters