arXiv:2605.28124v1 Announce Type: new Abstract: The goal of this work is to reduce the effect of photon noise in dental cone-beam CT reconstruction. We consider an inverse problem formulation and develop a databased prior. To this end, we simulate fan-beam acquisitions and add photon noise to the projection data. The prior is obtained by training a gradient-step denoiser using reconstructed simulated acquisitions. The trained model is integrated into a plug-and-play gradient-step algorithm to reconstruct images from simulated projections. Experiments on synthetic data demonstrate the denoising
The continuous advancements in AI and computational imaging techniques are enabling new applications previously limited by data quality and processing power. This specific timing reflects ongoing research efforts to apply AI to medical imaging for improved diagnostic capabilities.
Improving the quality of medical imaging like dental Cone-Beam CT scans through AI can lead to more accurate diagnoses, better treatment planning, and reduced patient exposure to radiation. This has direct implications for healthcare efficiency and patient outcomes.
The ability to reconstruct higher quality dental CT scans from noisy data changes the landscape for diagnostic accuracy and potentially expands the utility of existing imaging hardware through software enhancements.
- · Medical AI companies
- · Dental practitioners
- · Patients
- · Medical imaging equipment manufacturers
- · Traditional image reconstruction methods
- · Diagnostic errors from noisy data
Diagnostic precision in dental and potentially other medical fields improves significantly, leading to earlier and more effective intervention.
Reduced need for repeat scans due to poor image quality, decreasing radiation exposure and healthcare costs related to imaging.
AI-enhanced diagnostic tools become standard, pushing healthcare towards more data-driven and automated analysis, potentially integrating with broader AI agent systems for patient care pathways.
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Read at arXiv cs.AI