Implicit Neural Representation for Spherical Harmonics Reconstruction of Motion-Corrupted Fetal Diffusion MRI

Diffusion MRI of the fetal brain has potential to provide important insights into early development of white matter fibers. However, in vivo fetal acquisitions are severely corrupted by unpredictable motion, making the data unusable for downstream analysis. To address this challenge, we propose an implicit neural representation framework for fetal diffusion MRI reconstruction. The proposed method models the diffusion signal as a spatially and angularly continuous field by predicting direction-independent spherical harmonics (SH) coefficients from continuous spatial coordinates. Based on this representation, we formulate a physics-based forward slice acquisition model that links the underlying clean diffusion volume with the observed motion-corrupted slices, enabling joint optimization of the diffusion signal, fetal motion and acquisition artefacts. Furthermore, the single slice orientation acquisition typically used for each diffusion gradient introduces an ill-posed reconstruction problem along the slice-select direction. To mitigate this issue, we introduce a novel Through-Plane Continuity regularization loss that enforces smoothness in through-plane direction to suppress stripe artifacts, while preserving genuine anatomical details. Experiments on one in vivo fetal dataset and two simulated neonatal datasets demonstrate that the proposed framework achieves improved reconstruction fidelity, sharper anatomical features, and stronger robustness to acquisition artifacts. The code is available at: https://github.com/baby-MedIA/INR-dMRI-Recon