activity
20202022
most citedFully Automated Myocardial Strain Estimation from CMR Tagged Images using a Deep Learning Framework in the UK Biobank

48 citations · 80 across the 9 of their papers we have counts for

collaborators

9 papers

eess.IV2022

A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

Inês P. Machado, Esther Puyol-Antón, Kerstin Hammernik +10

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation. However, cine CMR acquisition is inherently slow and in recent decade…

eess.IV2022★ 1 cited

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Simon Dahan, Hao Xu, Logan Z. J. Williams +10

Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attenti…

eess.IV2021

Whole Heart Anatomical Refinement from CCTA using Extrapolation and Parcellation

Hao Xu, Steven A. Niederer, Steven E. Williams +3

Coronary computed tomography angiography (CCTA) provides detailed an-atomical information on all chambers of the heart. Existing segmentation tools can label the gross anatomy, but…

eess.IV2021★ 19 cited

Deep Learning Analysis of Cardiac MRI in Legacy Datasets: Multi-Ethnic Study of Atherosclerosis

Avan Suinesiaputra, Charlene A Mauger, Bharath Ambale-Venkatesh +9

The shape and motion of the heart provide essential clues to understanding the mechanisms of cardiovascular disease. With the advent of large-scale cardiac imaging data, statistica…

eess.IV2021

The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images

Devran Ugurlu, Esther Puyol-Anton, Bram Ruijsink +5

Domain shift refers to the difference in the data distribution of two datasets, normally between the training set and the test set for machine learning algorithms. Domain shift is…

eess.IV2021

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

Ines Machado, Esther Puyol-Anton, Kerstin Hammernik +8

Cine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-spa…

eess.IV2021★ 12 cited

CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images

Ruth P Lim, Stefan Kachel, Adriana DM Villa +5

Objectives: To develop an image-based automatic deep learning method to classify cardiac MR images by sequence type and imaging plane for improved clinical post-processing efficien…

eess.IV2020★ 48 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.IV2020

4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid Dynamics

Edward Ferdian, Avan Suinesiaputra, David Dubowitz +4

4D-flow magnetic resonance imaging (MRI) is an emerging imaging technique where spatiotemporal 3D blood velocity can be captured with full volumetric coverage in a single non-invas…