OpenMASC：加速MRI中跨轨迹金属感知采样与校正的开源流水线
OpenMASC: An Open-Source Pipeline for Cross-Trajectory Metal-Aware Sampling and Correction in Accelerated MRI
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中文总结 AI 辅助
提出OpenMASC开源流水线，结合物理数据生成、MA-VarNet重建网络和强化学习采样，实现跨轨迹的金属伪影感知加速MRI，在4倍和8倍加速下优于基线。
中文摘要 AI 辅助
金属植入物在整个k空间中破坏MRI测量，然而现有的加速MRI方法假设数据是干净的，且大多数金属伪影去除方法假设全采样采集。目前没有公开数据集提供同一解剖结构下有无金属的配对k空间和图像，也没有框架能跨采样轨迹联合处理伪影感知的采集与重建。我们提出OpenMASC，一个覆盖从数据生成到部署全流程的开源流水线。基于物理的数据生成模块将公开CT体积转换为成对的干净和金属污染MRI数据，包括笛卡尔和径向两种格式。MA-VarNet是一种展开重建网络，配备每级DC整流器，可纠正数据一致性步骤从污染测量中重新引入的伪影。一个强化学习代理主动选择k空间读出，并通过解耦的三阶段过程与重建网络协同训练。该框架除数据一致性算子外与轨迹无关，支持笛卡尔和径向采集而无需架构更改。在两个数据集上，4倍和8倍加速实验表明，在两种轨迹上均比传统和学习的基线有一致的改进。
英文摘要
Metal implants corrupt MRI measurements throughout $k$-space, yet existing accelerated MRI methods assume clean data and most metal artifact reduction approaches assume fully sampled acquisitions. No public dataset provides paired $k$-space and images with and without metal for the same anatomy, and no framework jointly addresses artifact-aware acquisition and reconstruction across sampling trajectories. We present OpenMASC, an open-source pipeline covering the full workflow from data generation to deployment. A physics-based data generation module converts public CT volumes into paired clean and metal-corrupted MRI data in both Cartesian and radial formats. MA-VarNet, an unrolled reconstruction network with a per-cascade DC Rectifier, corrects artifacts that data-consistency steps reintroduce from corrupted measurements. A reinforcement learning agent actively selects $k$-space readouts and co-trains with the reconstruction network through a decoupled three-stage procedure. The framework is trajectory-agnostic except for the data-consistency operator, supporting both Cartesian and radial acquisition without architectural changes. Experiments on two datasets at $4\times$ and $8\times$ acceleration demonstrate consistent improvements over conventional and learned baselines on both trajectories.
