Quinoa: Efficient and Robust CTF Estimation for CryoET Tilt Series

Abstract

Accurate estimation of the contrast transfer function (CTF) of tilt images is a critical first step in cryo electron tomography (cryoET), enabling reliable recovery of high-resolution structural information from thick, heterogeneous specimens. This challenge is especially acute in in situ cryoET, where macromolecules are imaged in their native cellular environment, often at high tilt and through substantial specimen thickness, with correspondingly low signal-to-noise ratios. Although CTF parameters can be later refined using reference-based approaches, accurate initial estimates are critical for downstream processing and the interpretability of tomographic reconstructions, yet they remain difficult to automate. Here, we present Quinoa, a software package designed to address these challenges. Quinoa first validates the tilt geometry and assesses data quality to generate robust initial estimates of defocus and phase shift. These estimates are then refined through optimization of a single global model, enabling precise fitting of the per-image defoci, tilt-dependent astigmatisms, time-dependent phase shifts, the specimen orientation (rotation, tilt and pitch) and the specimen thickness. Notably, and as a key distinguishing feature of this approach is that Quinoa fits equiphase-binned polar power spectra. This substantially reduces the computational cost of optimization without sacrificing accuracy, enabling more progressive and exhaustive refinement passes that further improve robustness. We validated Quinoa using both simulated and experimental data and benchmarked its performance against Warp, Ctfplotter, CTFMeasure, and AreTomo. Our results show that Quinoa is the most robust approach across all simulated cases, maintaining high accuracy even in the simultaneous presence of severe astigmatism, high specimen inclination and variable phase shift. Integrated recovery mechanisms further allow Quinoa to adapt automatically to a wide range of pixel sizes, defoci, astigmatisms and specimen thicknesses. Despite fitting a more complex and dynamic model, Quinoa remains extremely efficient due to extensive GPU acceleration, making it well suited for real-time monitoring during data collection as well as high-throughput offline batch processing. By improving automated CTF estimation in challenging tomographic data, Quinoa supports more accurate structural analysis of cells and tissues in situ.