Magnetic particle imaging (MPI) is a tracer-based imaging modality that detects superparamagnetic iron oxide nanoparticles in vivo, with applications in cancer cell tracking, lymph node mapping, and cell therapy monitoring. We introduce a new 3D image reconstruction framework for MPI data acquired using multi-angle field-free line (FFL) scans, demonstrating improvements in spatial resolution, quantitative accuracy, and high dynamic range performance over conventional sequential reconstruction pipelines. The framework is built by combining a physics-based FFL signal model with tomographic projection operators to form an efficient 3D forward operator, enabling the full dataset to be reconstructed jointly rather than as a series of independent 2D projections. A harmonic-domain compression step is incorporated naturally within this operator formulation, reducing memory overhead by over two orders of magnitude while preserving the structure and fidelity of the model, enabling volumetric reconstructions on standard desktop GPU hardware in only minutes. Phantom and in vivo results demonstrate substantially reduced background haze and improved visualization of low-intensity regions adjacent to bright structures, with an estimated $\sim$11$\times$ improvement in iron detection sensitivity relative to the conventional X-space CT approach. These advances enhance MPI image quality and quantitative reliability, supporting broader use of MPI in preclinical and future clinical imaging.

翻译：磁粒子成像（MPI）是一种基于示踪剂的成像技术，可检测体内的超顺磁性氧化铁纳米颗粒，应用于癌细胞追踪、淋巴结定位和细胞治疗监测。我们提出了一种新的三维图像重建框架，用于处理通过多角度无场线（FFL）扫描获取的MPI数据，该框架在空间分辨率、定量精度和高动态范围性能方面优于传统的顺序重建流程。该框架通过将基于物理的FFL信号模型与层析投影算子相结合，构建了一个高效的三维前向算子，使得整个数据集能够联合重建，而非作为一系列独立的二维投影进行处理。谐波域压缩步骤自然地融入该算子表达式中，在保持模型结构和保真度的同时，将内存开销降低两个数量级以上，从而可在标准桌面GPU硬件上仅用数分钟完成体积重建。仿体和体内实验结果表明，背景雾度显著降低，邻近明亮结构的低强度区域可视化效果改善，相对于传统的X空间CT方法，铁检测灵敏度估计提高约11倍。这些进展提升了MPI图像质量和定量可靠性，有助于MPI在临床前及未来临床成像中的更广泛应用。