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Found 9 papers, 2 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.
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 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.
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.
Synthetic PET via Domain Translation of 3D MRI
Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT.
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.
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.
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.
