4 papers
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…
Volumetric Conditional Score-based Residual Diffusion Model for PET/MR Denoising
Siyeop Yoon, Rui Hu, Yuang Wang +6
PET imaging is a powerful modality offering quantitative assessments of molecular and physiological processes. The necessity for PET denoising arises from the intrinsic high noise…