Applications of artificial intelligence in cardiovascular imaging
M Sermesant, H Delingette, H Cochet, P Jaïs… - Nature Reviews …, 2021 - nature.com
Research into artificial intelligence (AI) has made tremendous progress over the past
decade. In particular, the AI-powered analysis of images and signals has reached human …
decade. In particular, the AI-powered analysis of images and signals has reached human …
[HTML][HTML] Principles of cardiovascular magnetic resonance feature tracking and echocardiographic speckle tracking for informed clinical use
Tissue tracking technology of routinely acquired cardiovascular magnetic resonance (CMR)
cine acquisitions has increased the apparent ease and availability of non-invasive …
cine acquisitions has increased the apparent ease and availability of non-invasive …
Characterization of myocardial motion patterns by unsupervised multiple kernel learning
S Sanchez-Martinez, N Duchateau, T Erdei… - Medical image …, 2017 - Elsevier
We propose an independent objective method to characterize different patterns of functional
responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome …
responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome …
[HTML][HTML] A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: application to cardiac resynchronisation therapy …
We present a framework for combining a cardiac motion atlas with non-motion data. The
atlas represents cardiac cycle motion across a number of subjects in a common space …
atlas represents cardiac cycle motion across a number of subjects in a common space …
Regional multi-view learning for cardiac motion analysis: Application to identification of dilated cardiomyopathy patients
Objective: The aim of this paper is to describe an automated diagnostic pipeline that uses as
input only ultrasound (US) data, but is at the same time informed by a training database of …
input only ultrasound (US) data, but is at the same time informed by a training database of …
[HTML][HTML] Machine learning approaches for myocardial motion and deformation analysis
Information about myocardial motion and deformation is key to differentiate normal and
abnormal conditions. With the advent of approaches relying on data rather than pre …
abnormal conditions. With the advent of approaches relying on data rather than pre …
Characterizing interactions between cardiac shape and deformation by non-linear manifold learning
In clinical routine, high-dimensional descriptors of the cardiac function such as shape and
deformation are reduced to scalars (eg volumes or ejection fraction), which limit the …
deformation are reduced to scalars (eg volumes or ejection fraction), which limit the …
[HTML][HTML] A multimodal spatiotemporal cardiac motion atlas from MR and ultrasound data
Cardiac motion atlases provide a space of reference in which the motions of a cohort of
subjects can be directly compared. Motion atlases can be used to learn descriptors that are …
subjects can be directly compared. Motion atlases can be used to learn descriptors that are …
Open problems in spectral dimensionality reduction
H Strange, R Zwiggelaar - 2014 - Springer
Open Problems in Spectral Dimensionality Reduction Page 1 SPRINGER BRIEFS IN
COMPUTER SCIENCE Harry Strange Reyer Zwiggelaar Open Problems in Spectral …
COMPUTER SCIENCE Harry Strange Reyer Zwiggelaar Open Problems in Spectral …
Infarct localization from myocardial deformation: prediction and uncertainty quantification by regression from a low-dimensional space
N Duchateau, M De Craene, P Allain… - IEEE transactions on …, 2016 - ieeexplore.ieee.org
Diagnosing and localizing myocardial infarct is crucial for early patient management and
therapy planning. We propose a new method for predicting the location of myocardial infarct …
therapy planning. We propose a new method for predicting the location of myocardial infarct …