Generative models provide powerful learned priors for solving challenging inverse problems in medical imaging. In this talk, I will present diffusion-based, physics-informed approaches for CT and PET reconstruction, illustrated through three applications: motion-compensated head cone-beam CT, material decomposition in photon-counting CT, and joint activity-attenuation reconstruction in CT-less...
We present a compact 3D neural network for segmentation of maxillary sinuses from CT scans in children with chronic rhinosinusitis. It comprises 12 convolutional layers totaling 50k parameters, minimal compared to most 3D medical-imaging architectures, and completes the prediction in a few seconds on an average CPU, making the tool accessible for most clinicians. The sinus volume and its...
Data-driven reconstruction for computed tomography has boomed in the last decade. Yet unlike in other AI fields, its adoption in practical CT has been slow. In this talk I'll give a brief overview of the types of reconstruction methods and highlight the current challenges and some progress that we have done recently
We introduce a novel learning framework for accelerated Monte Carlo (MC) dose calculation termed Energy Shifting. This approach leverages deep learning to synthesize complex polyenergetic dose distributions directly from simple monoenergetic inputs under identical beam configurations. Unlike conventional approaches such as denoising or GenAI techniques, our method achieves superior...
Background and Aims: This study introduces graph neural networks (GNNs) to improve prompt-gamma imaging for a 1D coded-mask gamma camera developed by the SiFi-CC collaboration for proton therapy verification.
Methods: The coded-mask detector features LYSO:Ce,Ca scintillating fibers with dual-ended readouts. Coincident detector signals are aggregated into event representations and...
The performance of modern, high-load information systems significantly depends on the efficiency of cache management. Meanwhile, the traditional Least Recently Used and Least Frequently Used policies are based on fixed heuristics, which limits their ability to adapt to changes in query structure and intensity.
To overcome these constraints, a decision-making approach for adding new objects to...
