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arXiv · 2508.10821

SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions

Jacob Egebjerg·Daniel Wüstner
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Abstract

Soft X-ray tomography provides detailed structural insight into whole cells but is hindered by experimental artifacts such as the missing wedge and by limited availability of annotated datasets. We present SimAQ, a simulation pipeline that generates realistic yeast phantoms and applies synthetic imaging artifacts to produce paired noisy volumes, sinograms, and reconstructions. We validate our approach by training a neural network primarily on synthetic data and demonstrate effective few-shot and zero-shot transfer learning on real X-ray tomograms. Our model delivers accurate segmentations, enabling quantitative analysis of noisy tomograms without relying on large labeled datasets.

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Jacob Egebjerg, Daniel Wüstner. 2025-08-14. SimAQ: Mitigating Experimental Artifacts in Soft X-Ray Tomography using Simulated Acquisitions. https://arxiv.org/abs/2508.10821

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