Search Results for author:
Found 11 papers, 6 papers with code
Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context
However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as `spatial context' in this work.
Learning Deep MRI Reconstruction Models from Scratch in Low-Data Regimes
To alleviate error propagation, PSFNet combines its SS and SG priors via a novel parallel-stream architecture with learnable fusion parameters.
Unsupervised Medical Image Translation with Adversarial Diffusion Models
Extensive assessments are reported on the utility of SynDiff against competing GAN and diffusion models in multi-contrast MRI and MRI-CT translation.
Ranked #7 on
Image-to-Image Translation
on IXI
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI Synthesis
As such, pFLSynth enables training of a unified synthesis model that can reliably generalize across multiple sites and translation tasks.
Adaptive Diffusion Priors for Accelerated MRI Reconstruction
A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize data-consistency loss.
Federated Learning of Generative Image Priors for MRI Reconstruction
Specificity in the prior is preserved via a mapper subnetwork that produces site-specific latents.
Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
Supervised reconstruction models are characteristically trained on matched pairs of undersampled and fully-sampled data to capture an MRI prior, along with supervision regarding the imaging operator to enforce data consistency.
Three Dimensional MR Image Synthesis with Progressive Generative Adversarial Networks
Mainstream deep models for three-dimensional MRI synthesis are either cross-sectional or volumetric depending on the input.
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.
A Transfer-Learning Approach for Accelerated MRI using Deep Neural Networks
Methods: Neural networks were trained on thousands of samples from public datasets of either natural images or brain MR images.
