2 citations · 3 across the 18 of their papers we have counts for
4 papers · 1 filter
Pseudoinverse Diffusion Models for Generative CT Image Reconstruction from Low Dose Data
Matthew Tivnan, Dufan Wu, Quanzheng Li
Score-based diffusion models have significantly advanced generative deep learning for image processing. Measurement conditioned models have also been applied to inverse problems su…
Multi-Source Static CT with Adaptive Fluence Modulation to Minimize Hallucinations in Generative Reconstructions
Matthew Tivnan, Amar Gupta, Kai Yang +2
Multi-source static Computed Tomography (CT) systems have introduced novel opportunities for adaptive imaging techniques. This work presents an innovative method of fluence field m…
Generative Super-Resolution PET Imaging with Fourier Diffusion Models
Matthew Tivnan, Quanzheng Li
Neurological Positron Emission Tomography (PET) is a critical imaging modality for diagnosing and studying neurodegenerative diseases like Alzheimer's disease. However, the inheren…
Hallucination Index: An Image Quality Metric for Generative Reconstruction Models
Matthew Tivnan, Siyeop Yoon, Zhennong Chen +3
Generative image reconstruction algorithms such as measurement conditioned diffusion models are increasingly popular in the field of medical imaging. These powerful models can tran…