Magnetic particle imaging (MPI) offers exceptional contrast for magnetic nanoparticles (MNP) at high spatio-temporal resolution. A common procedure in MPI starts with a calibration scan to measure the system matrix (SM), which is then used to set up an inverse problem to reconstruct images of the MNP distribution during subsequent scans. This calibration enables the reconstruction to sensitively account for various system imperfections. Yet time-consuming SM measurements have to be repeated under notable changes in system properties. Here, we introduce a novel deep learning approach for accelerated MPI calibration based on Transformers for SM super-resolution (TranSMS). Low-resolution SM measurements are performed using large MNP samples for improved signal-to-noise ratio efficiency, and the high-resolution SM is super-resolved via model-based deep learning. TranSMS leverages a vision transformer module to capture contextual relationships in low-resolution input images, a dense convolutional module for localizing high-resolution image features, and a data-consistency module to ensure measurement fidelity. Demonstrations on simulated and experimental data indicate that TranSMS significantly improves SM recovery and MPI reconstruction for up to 64-fold acceleration in two-dimensional imaging.

中文翻译：

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TranSMS：用于磁粒子成像超分辨率校准的变压器


磁性粒子成像 (MPI) 为磁性纳米粒子 (MNP) 在高时空分辨率下提供卓越的对比度。 MPI 中的常见过程从校准扫描开始，以测量系统矩阵 (SM)，然后使用该校准扫描来设置反问题，以在后续扫描期间重建 MNP 分布的图像。这种校准使得重建能够敏感地考虑各种系统缺陷。然而，在系统属性发生显着变化的情况下，必须重复耗时的 SM 测量。在这里，我们介绍了一种基于 SM 超分辨率 Transformers (TranSMS) 的加速 MPI 校准的新型深度学习方法。使用大型 MNP 样本执行低分辨率 SM 测量，以提高信噪比效率，而高分辨率 SM 通过基于模型的深度学习实现超分辨率。 TranSMS 利用视觉转换器模块来捕获低分辨率输入图像中的上下文关系，利用密集卷积模块来定位高分辨率图像特征，并利用数据一致性模块来确保测量保真度。模拟和实验数据的演示表明，TranSMS 显着改善了 SM 恢复和 MPI 重建，在二维成像中加速高达 64 倍。