用隐式神经表示解决单方向磁共振成像的重建难题，提升脑组织微结构成像精度。
QSMnet-INR: Single-Orientation Quantitative Susceptibility Mapping via Implicit Neural Representation in k-Space
- 在k空间引入隐式神经表示，连续建模多方向偶极响应并补全锥形零区
- 在2016年挑战赛和临床数据上优于传统及深度学习方法，显著减少伪影与结构丢失
- 无需多方向扫描即可实现高精度重建，适合临床快速成像场景
定量磁化率成像（QSM）从磁共振相位数据中量化组织磁化率，在脑微结构成像、铁沉积评估和神经疾病研究中具有关键作用。然而，单方向QSM反演因偶极核在傅里叶域存在锥形零区，导致病态性严重，引发条纹伪影和结构损失。为此，我们提出QSMnet-INR，一种融合隐式神经表示（INR）的物理引导深度框架，将INR模块嵌入k空间域。该模块连续建模多方向偶极响应并显式填补锥形零区，频率域残差加权偶极损失确保物理一致性。整体网络通过3D U-Net骨干与INR模块交替优化，实现端到端联合训练。在2016年QSM重建挑战赛、多方向GRE数据集以及自建和公开的单方向临床数据上的实验表明，QSMnet-INR在多个定量指标上持续优于传统及近期深度学习方法。该框架在锥形零区内的结构恢复和伪影抑制方面表现突出。消融实验证实INR模块与偶极损失的互补贡献，分别促进细节保留与物理稳定性。总体而言，QSMnet-INR有效缓解了单方向QSM的病态性，无需多方向采集即可实现高精度、强鲁棒性与跨场景泛化能力，具备临床转化潜力。
原文摘要 · Abstract (English)
Quantitative Susceptibility Mapping (QSM) quantifies tissue magnetic susceptibility from magnetic-resonance phase data and plays a crucial role in brain microstructure imaging, iron-deposition assessment, and neurological-disease research. However, single-orientation QSM inversion remains highly ill-posed because the dipole kernel exhibits a cone-null region in the Fourier domain, leading to streaking artifacts and structural loss. To overcome this limitation, we propose QSMnet-INR, a deep, physics-informed framework that integrates an Implicit Neural Representation (INR) into the k-space domain. The INR module continuously models multi-directional dipole responses and explicitly completes the cone-null region, while a frequency-domain residual-weighted Dipole Loss enforces physical consistency. The overall network combines a 3D U-Net-based QSMnet backbone with the INR module through alternating optimization for end-to-end joint training. Experiments on the 2016 QSM Reconstruction Challenge, a multi-orientation GRE dataset, and both in-house and public single-orientation clinical data demonstrate that QSMnet-INR consistently outperforms conventional and recent deep-learning approaches across multiple quantitative metrics. The proposed framework shows notable advantages in structural recovery within cone-null regions and in artifact suppression. Ablation studies further confirm the complementary contributions of the INR module and Dipole Loss to detail preservation and physical stability. Overall, QSMnet-INR effectively alleviates the ill-posedness of single-orientation QSM without requiring multi-orientation acquisition, achieving high accuracy, robustness, and strong cross-scenario generalization-highlighting its potential for clinical translation.
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