统一提示模型提升心脏MRI随机采样重建质量
UPCMR: A Universal Prompt-guided Model for Random Sampling Cardiac MRI Reconstruction
- 用可学习提示融合特定采样与空间信息，嵌入UNet结构
- 在CMRxRecon2024数据集上全场景优于传统方法
- 适合需要适配多种采样模式的临床MRI加速重建
心脏磁共振成像（CMR）对心脏病诊断至关重要，但扫描时间长仍是主要瓶颈。通过欠采样k空间可加速成像，但会降低图像质量。近年来深度学习方法虽提升了速度与质量，但在不同采样模式和欠采样因子下的适应性仍受限。为此，本文提出UPCMR——一种通用的非迭代重建模型。该模型在每个UNet模块中引入两类可学习提示：针对采样方式的提示和针对空间位置的提示，并进行有效融合。基于CMRxRecon2024挑战赛数据集训练与验证，UPCMR在所有随机采样场景下均显著提升重建图像质量，展现出强适应性潜力。
原文摘要 · Abstract (English)
Cardiac magnetic resonance imaging (CMR) is vital for diagnosing heart diseases, but long scan time remains a major drawback. To address this, accelerated imaging techniques have been introduced by undersampling k-space, which reduces the quality of the resulting images. Recent deep learning advancements aim to speed up scanning while preserving quality, but adapting to various sampling modes and undersampling factors remains challenging. Therefore, building a universal model is a promising direction. In this work, we introduce UPCMR, a universal unrolled model designed for CMR reconstruction. This model incorporates two kinds of learnable prompts, undersampling-specific prompt and spatial-specific prompt, and integrates them with a UNet structure in each block. Overall, by using the CMRxRecon2024 challenge dataset for training and validation, the UPCMR model highly enhances reconstructed image quality across all random sampling scenarios through an effective training strategy compared to some traditional methods, demonstrating strong adaptability potential for this task.
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