Search Results for author:
Found 5 papers, 1 papers with code
Adapt and Diffuse: Sample-adaptive Reconstruction via Latent Diffusion Models
Our framework acts as a wrapper that can be combined with any latent diffusion-based baseline solver, imbuing it with sample-adaptivity and acceleration.
DiracDiffusion: Denoising and Incremental Reconstruction with Assured Data-Consistency
In this work, we propose a novel framework for inverse problem solving, namely we assume that the observation comes from a stochastic degradation process that gradually degrades and noises the original clean image.
HUMUS-Net: Hybrid unrolled multi-scale network architecture for accelerated MRI reconstruction
These models split input images into non-overlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of convolutional architectures.
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 8x
(using extra training data)
Semi-Supervised Learning of Mutually Accelerated MRI Synthesis without Fully-Sampled Ground Truths
Here, we propose a novel semi-supervised deep generative model that instead learns to recover high-quality target images directly from accelerated acquisitions of source and target contrasts.
Progressively Volumetrized Deep Generative Models for Data-Efficient Contextual Learning of MR Image Recovery
Comprehensive demonstrations on mainstream MRI reconstruction and synthesis tasks show that ProvoGAN yields superior performance to state-of-the-art volumetric and cross-sectional models.
