SONAR: A Physics-Constrained Neural Representation for X-Ray Dark-Field CT

MCML Authors

Julian McGinnis

→ Group Benedikt Wiestler
 AI for Image-Guided Diagnosis and Therapy

Daniel Rückert

Prof. Dr.

Director

Artificial Intelligence in Healthcare and Medicine

Abstract

Dark-field computed tomography (DFCT) enables functional lung imaging with small-angle X-ray scattering, but reconstructions are often degraded by streak artifacts. We propose SONAR (Shot-Optimized Neural Adaptive Representation), a projection-based implicit neural representation (INR) that jointly models transmission, phase shift, and dark-field signals across neighboring shots using a physics-based Talbot–Lau interferometer forward model. By leveraging adaptive per-projection optimization, SONAR effectively enables stabilized phase retrieval and suppresses streak artifacts for improved DFCT image quality on a grating-based human-scale prototype.

misc FHM+26

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Preprint

Apr. 2026

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Authors

D. Frey • T. Hiu • J. McGinnis • T. Dorosti • J. Thalhammer • S. Peterhansl • Z. Huang • F. Pfeiffer • D. Rückert • F. Schaff

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Links

URL

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Research Area

 C1 | Medicine