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20182025
most citedFully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

48 citations · 50 across the 7 of their papers we have counts for

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6 papers · 1 filter

eess.IV2025

SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging

Junkai Liu, Nay Aung, Theodoros N. Arvanitis +4

Magnetic resonance imaging (MRI) provides detailed soft-tissue characteristics that assist in disease diagnosis and screening. However, the accuracy of clinical practice is often h…

eess.IV202048 cited

Fully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

Edward Ferdian, Avan Suinesiaputra, Kenneth Fung +9

Purpose: To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac magnetic resonance tagged…

eess.IV2019

Joint Motion Estimation and Segmentation from Undersampled Cardiac MR Image

Chen Qin, Wenjia Bai, Jo Schlemper +4

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. H…

eess.IV2019

Improving the generalizability of convolutional neural network-based segmentation on CMR images

Chen Chen, Wenjia Bai, Rhodri H. Davies +13

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR…

eess.IV2019

3D Cardiac Shape Prediction with Deep Neural Networks: Simultaneous Use of Images and Patient Metadata

Rahman Attar, Marco Pereanez, Christopher Bowles +4

Large prospective epidemiological studies acquire cardiovascular magnetic resonance (CMR) images for pre-symptomatic populations and follow these over time. To support this approac…

eess.IV20191 cited

High Throughput Computation of Reference Ranges of Biventricular Cardiac Function on the UK Biobank Population Cohort

Rahman Attar, Marco Pereanez, Ali Gooya +6

The exploitation of large-scale population data has the potential to improve healthcare by discovering and understanding patterns and trends within this data. To enable high throug…