Accelerating dynamic MRI is essential for enhancing clinical applications, such as adaptive radiotherapy, and improving patient comfort. Traditional deep learning (DL) approaches for accelerated dynamic MRI reconstruction typically rely on predefined or random subsampling patterns, applied uniformly across all temporal phases. This standard practice overlooks the potential benefits of leveraging temporal correlations and lacks the adaptability required for case-specific subsampling optimization, which holds the potential for maximizing reconstruction quality. Addressing this gap, we present a novel end-to-end framework for adaptive dynamic MRI subsampling and reconstruction. Our pipeline integrates a DL-based adaptive sampler, generating case-specific dynamic subsampling patterns, trained end-to-end with a state-of-the-art 2D dynamic reconstruction network, namely vSHARP, which effectively reconstructs the adaptive dynamic subsampled data into a moving image. Our method is assessed using dynamic cine cardiac MRI data, comparing its performance against vSHARP models that employ common subsampling trajectories, and pipelines trained to optimize dataset-specific sampling schemes alongside vSHARP reconstruction. Our results indicate superior reconstruction quality, particularly at high accelerations.

翻译：加速动态磁共振成像对于增强临床应用（如自适应放射治疗）和改善患者舒适度至关重要。传统的加速动态MRI深度学习重建方法通常依赖预定义或随机子采样模式，并统一应用于所有时间相位。这种标准做法忽视了利用时间相关性的潜在优势，且缺乏针对特定病例优化子采样所需的自适应能力，而这正是最大化重建质量的潜力所在。针对这一不足，我们提出了一种用于自适应动态MRI子采样与重建的新型端到端框架。我们的流程整合了一个基于深度学习的自适应采样器，用于生成病例特定的动态子采样模式，并与先进2D动态重建网络vSHARP进行端到端联合训练，从而将自适应动态子采样数据有效重建为运动图像。我们利用动态电影心脏MRI数据评估了该方法，将其性能与采用常见子采样轨迹的vSHARP模型以及针对数据集特定采样方案优化并联合vSHARP重建的流程进行比较。结果表明，我们的方法在重建质量上表现更优，尤其是在高加速倍数下。