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Found 19 papers, 6 papers with code
Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model
Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality.
Convergence of Nonconvex PnP-ADMM with MMSE Denoisers
This paper presents a theoretical explanation for the observed stability of PnP-ADMM based on the interpretation of the CNN prior as a minimum mean-squared error (MMSE) denoiser.
DiffGEPCI: 3D MRI Synthesis from mGRE Signals using 2.5D Diffusion Model
We introduce a new framework called DiffGEPCI for cross-modality generation in magnetic resonance imaging (MRI) using a 2. 5D conditional diffusion model.
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
Face video restoration (FVR) is a challenging but important problem where one seeks to recover a perceptually realistic face videos from a low-quality input.
DDPET-3D: Dose-aware Diffusion Model for 3D Ultra Low-dose PET Imaging
We extensively evaluated DDPET-3D on 100 patients with 6 different low-dose levels (a total of 600 testing studies), and demonstrated superior performance over previous diffusion models for 3D imaging problems as well as previous noise-aware medical image denoising models.
A Structured Pruning Algorithm for Model-based Deep Learning
There is a growing interest in model-based deep learning (MBDL) for solving imaging inverse problems.
PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction
Ptychography is an imaging technique that captures multiple overlapping snapshots of a sample, illuminated coherently by a moving localized probe.
A Plug-and-Play Image Registration Network
A traditional DL approach to DIR is based on training a convolutional neural network (CNN) to estimate the registration field between two input images.
SINCO: A Novel structural regularizer for image compression using implicit neural representations
Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression.
CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
Self-Supervised Deep Equilibrium Models for Inverse Problems with Theoretical Guarantees
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
SPICE: Self-Supervised Learning for MRI with Automatic Coil Sensitivity Estimation
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
Online Deep Equilibrium Learning for Regularization by Denoising
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
Image Reconstruction for MRI using Deep CNN Priors Trained without Groundtruth
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
Learning Cross-Video Neural Representations for High-Quality Frame Interpolation
This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors.
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Learning-based Motion Artifact Removal Networks (LEARN) for Quantitative $R_2^\ast$ Mapping
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
Deformation-Compensated Learning for Image Reconstruction without Ground Truth
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Deep Image Reconstruction using Unregistered Measurements without Groundtruth
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
