Search Results for author:
Found 27 papers, 15 papers with code
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction
To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains.
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration
Establishing voxelwise semantic correspondence across distinct imaging modalities is a foundational yet formidable computer vision task.
DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction
To these ends, we present a dual-domain self-supervised transformer (DSFormer) for accelerated MC-MRI reconstruction.
Complementary Time-Frequency Domain Networks for Dynamic Parallel MR Image Reconstruction
The iterative model is embedded into a deep recurrent neural network which learns to recover the image via exploiting spatio-temporal redundancies in complementary domains.
Deep Network Interpolation for Accelerated Parallel MR Image Reconstruction
We present a deep network interpolation strategy for accelerated parallel MR image reconstruction.
$Σ$-net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction
Purpose: To systematically investigate the influence of various data consistency layers, (semi-)supervised learning and ensembling strategies, defined in a $\Sigma$-net, for accelerated parallel MR image reconstruction using deep learning.
$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
We explore an ensembled $\Sigma$-net for fast parallel MR imaging, including parallel coil networks, which perform implicit coil weighting, and sensitivity networks, involving explicit sensitivity maps.
Data consistency networks for (calibration-less) accelerated parallel MR image reconstruction
We show that the proposed approaches are competitive relative to the state of the art both quantitatively and qualitatively.
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image
Accelerating the acquisition of magnetic resonance imaging (MRI) is a challenging problem, and many works have been proposed to reconstruct images from undersampled k-space data.
Unsupervised Multi-modal Style Transfer for Cardiac MR Segmentation
In this work, we present a fully automatic method to segment cardiac structures from late-gadolinium enhanced (LGE) images without using labelled LGE data for training, but instead by transferring the anatomical knowledge and features learned on annotated balanced steady-state free precession (bSSFP) images, which are easier to acquire.
k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal Correlations
Experiments conducted on highly undersampled short-axis cardiac cine MRI scans demonstrate that our proposed method outperforms the current state-of-the-art dynamic MR reconstruction approaches both quantitatively and qualitatively.
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic resonance imaging (MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high-quality reconstruction of undersampled multi-coil MR data.
Deep Hashing using Entropy Regularised Product Quantisation Network
In large scale systems, approximate nearest neighbour search is a crucial algorithm to enable efficient data retrievals.
Generalising Deep Learning MRI Reconstruction across Different Domains
We look into robustness of deep learning based MRI reconstruction when tested on unseen contrasts and organs.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation.
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
AGs can be easily integrated into standard CNN models such as VGG or U-Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy.
Deep nested level sets: Fully automated segmentation of cardiac MR images in patients with pulmonary hypertension
In this paper we introduce a novel and accurate optimisation method for segmentation of cardiac MR (CMR) images in patients with pulmonary hypertension (PH).
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
Joint Learning of Motion Estimation and Segmentation for Cardiac MR Image Sequences
Cardiac motion estimation and segmentation play important roles in quantitatively assessing cardiac function and diagnosing cardiovascular diseases.
Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction.
Attention-Gated Networks for Improving Ultrasound Scan Plane Detection
We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance.
Attention U-Net: Learning Where to Look for the Pancreas
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes.
Ranked #1 on
Pancreas Segmentation
on CT-150
Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
In particular, the proposed architecture embeds the structure of the traditional iterative algorithms, efficiently modelling the recurrence of the iterative reconstruction stages by using recurrent hidden connections over such iterations.
A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
Firstly, we show that when each 2D image frame is reconstructed independently, the proposed method outperforms state-of-the-art 2D compressed sensing approaches such as dictionary learning-based MR image reconstruction, in terms of reconstruction error and reconstruction speed.
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction
The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow.
