2 citations · 3 across the 13 of their papers we have counts for
13 papers
HandSplatter: Automated Digital Goniometry from Neural Rendering
Emmett Chen, Neal Chen, Xiang Li +2
Hand and finger disorders are leading contributors to musculoskeletal disability, creating a clinical need for precise methods to quantify joint motion. Range of motion (ROM) serve…
Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
Bartlomiej Sobieski, Matthew Tivnan, Dawid Płudowski +4
Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underlying structural rules, result…
Projection Embedded Diffusion Bridge for CT Reconstruction from Incomplete Data
Yuang Wang, Pengfei Jin, Siyeop Yoon +6
Reconstructing CT images from incomplete projection data remains challenging due to the ill-posed nature of the problem. Diffusion bridge models have recently shown promise in rest…
System-Embedded Diffusion Bridge Models
Bartlomiej Sobieski, Matthew Tivnan, Yuang Wang +5
Solving inverse problems -- recovering signals from incomplete or noisy measurements -- is fundamental in science and engineering. Score-based generative models (SGMs) have recentl…
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface
Siyeop Yoon, Yujin Oh, Pengfei Jin +5
We present Surf2CT, a novel cascaded flow matching framework that synthesizes full 3D computed tomography (CT) volumes of the human torso from external surface scans and simple dem…
Cascaded 3D Diffusion Models for Whole-body 3D 18-F FDG PET/CT synthesis from Demographics
Siyeop Yoon, Sifan Song, Pengfei Jin +6
We propose a cascaded 3D diffusion model framework to synthesize high-fidelity 3D PET/CT volumes directly from demographic variables, addressing the growing need for realistic digi…
Prediction of Frozen Region Growth in Kidney Cryoablation Intervention Using a 3D Flow-Matching Model
Siyeop Yoon, Yujin Oh, Matthew Tivnan +7
This study presents a 3D flow-matching model designed to predict the progression of the frozen region (iceball) during kidney cryoablation. Precise intraoperative guidance is criti…
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…
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…
An Ordinary Differential Equation Sampler with Stochastic Start for Diffusion Bridge Models
Yuang Wang, Pengfei Jin, Li Zhang +3
Diffusion bridge models have demonstrated promising performance in conditional image generation tasks, such as image restoration and translation, by initializing the generative pro…
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…
Measurement Embedded Schrödinger Bridge for Inverse Problems
Yuang Wang, Pengfei Jin, Siyeop Yoon +4
Score-based diffusion models are frequently employed as structural priors in inverse problems. However, their iterative denoising process, initiated from Gaussian noise, often resu…
Implicit Image-to-Image Schrodinger Bridge for Image Restoration
Yuang Wang, Siyeop Yoon, Pengfei Jin +8
Diffusion-based models have demonstrated remarkable effectiveness in image restoration tasks; however, their iterative denoising process, which starts from Gaussian noise, often le…