8 papers
Accelerating Stroke MRI with Diffusion Probabilistic Models through Large-Scale Pre-training and Target-Specific Fine-Tuning
Yamin Arefeen, Sidharth Kumar, Steven Warach +2
Purpose: To develop a data-efficient strategy for accelerated MRI reconstruction with Diffusion Probabilistic Generative Models (DPMs) that enables faster scan times in clinical st…
Theoretical Bounds on Parallel Imaging Implicit Data Crimes in an MRI Reproducing Kernel Hilbert Space
Evan Frenklak, Yamin Arefeen, Jonathan I Tamir
Magnetic Resonance Imaging (MRI) diagnoses and manages a wide range of diseases, yet long scan times drive high costs and limit accessibility. AI methods have demonstrated substant…
Optimizing Sampling Patterns for Compressed Sensing MRI with Diffusion Generative Models
Sriram Ravula, Brett Levac, Yamin Arefeen +3
Magnetic resonance imaging (MRI) is a powerful medical imaging modality, but long acquisition times limit throughput, patient comfort, and clinical accessibility. Diffusion-based g…
Robust multi-coil MRI reconstruction via self-supervised denoising
Asad Aali, Marius Arvinte, Sidharth Kumar +2
We study the effect of incorporating self-supervised denoising as a pre-processing step for training deep learning (DL) based reconstruction methods on data corrupted by Gaussian n…
Diffusion Probabilistic Generative Models for Accelerated, in-NICU Permanent Magnet Neonatal MRI
Yamin Arefeen, Brett Levac, Bhairav Patel +2
Purpose: Magnetic Resonance Imaging (MRI) enables non-invasive assessment of brain abnormalities during early life development. Permanent magnet scanners operating in the neonatal…
A Generative Diffusion Model to Solve Inverse Problems for Robust in-NICU Neonatal MRI
Yamin Arefeen, Brett Levac, Jonathan I. Tamir
We present the first acquisition-agnostic diffusion generative model for Magnetic Resonance Imaging (MRI) in the neonatal intensive care unit (NICU) to solve a range of inverse pro…