CryoSD: Score Distillation for Heterogeneous ab initio Reconstruction in Cryo-EM

Heterogeneous *ab initio* reconstruction in single-particle cryo-electron microscopy (cryo-EM) aims to recover an ensemble of 3D molecular structures from extremely noisy 2D projection images. The task is further complicated by unknown particle orientations and contrast transfer function (CTF) distortions. Existing approaches typically combine variational autoencoders with explicit pose estimation, which becomes unreliable in the presence of severe noise, pronounced conformational variability, and structural symmetries. We introduce cryoSD, a framework for heterogeneous cryo-EM reconstruction that combines ideas from score-based diffusion models, Noisier2Noise and score distillation sampling (SDS). CryoSD learns a score-based diffusion model over particle images and leverages the learned image distribution for unsupervised denoising, CTF correction and SDS-based 2D-to-3D reconstruction, eliminating the need for explicit pose estimation. We evaluate cryoSD on four challenging datasets exhibiting continuous and compositional heterogeneity, including IgG-RL and IgG-1D from CryoBench and data from EMPIAR-10295. We compare cryoSD with the state of the art in heterogeneous *ab initio* reconstruction. Our results demonstrate the feasibility of SDS-based heterogeneous cryo-EM reconstruction and suggest cryoSD as a powerful alternative to existing approaches in heterogeneous cryo-EM *ab initio* reconstruction. In particular, cryoSD shows superior performance on sparse heterogeneous data.