586 lines
46 KiB
XML
586 lines
46 KiB
XML
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schemename="dnet:dataCite_title">A completely automated pipeline for 3D reconstruction of
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human heart from 2D cine magnetic resonance slices.
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</title>
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<title classid="main title" classname="main title" schemeid="dnet:dataCite_title"
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schemename="dnet:dataCite_title">A completely automated pipeline for 3D reconstruction of
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human heart from 2D cine magnetic resonance slices
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</title>
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URL="https://academic.microsoft.com/#/detail/2693349397">Abhirup Banerjee
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</creator>
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<creator rank="3" URL="https://academic.microsoft.com/#/detail/1425738153">Ernesto Zacur</creator>
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<creator rank="6" URL="https://academic.microsoft.com/#/detail/2043191934">Robin P. Choudhury
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</creator>
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<creator rank="7" URL="https://academic.microsoft.com/#/detail/2166186014">Blanca Rodriguez
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</creator>
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<creator rank="2" URL="https://academic.microsoft.com/#/detail/2889449215">Julia Camps</creator>
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<creator rank="5" URL="https://academic.microsoft.com/#/detail/264390263">Yoram Rudy</creator>
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<creator rank="4" URL="https://academic.microsoft.com/#/detail/2713879776">Christopher M. Andrews
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<description>Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and
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characterization of cardiovascular diseases, since it can identify abnormalities in structure
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and function of the myocardium non-invasively and without the need for ionizing radiation.
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However, in clinical practice, it is commonly acquired as a collection of separated and
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independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a
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completely automated pipeline for generating patient-specific 3D biventricular heart models from
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cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR
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images, segments them using a deep learning-based method to extract the heart contours, and
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aligns the contours in 3D space correcting possible misalignments due to breathing or subject
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motion first using the intensity and contours information from the cine data and next with the
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help of a statistical shape model. Finally, the sparse 3D representation of the contours is used
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to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated
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in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment
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artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the
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final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our
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computational pipeline are used for simulations of electrical activation patterns, showing
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agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented
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here for patient-specific MR imaging-based 3D biventricular representations contribute to the
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efficient realization of precision medicine, enabling the enhanced interpretability of clinical
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data, the digital twin vision through patient-specific image-based modelling and simulation, and
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augmented reality applications.
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This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new
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challenges and opportunities’.
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schemename="dnet:subject_classification_typologies">General Physics and Astronomy
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schemename="dnet:subject_classification_typologies">General Engineering
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</subject>
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<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">General Mathematics
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Pipeline (computing)
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Cine mri
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Structure and function
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Cardiac magnetic resonance
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Magnetic resonance imaging
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="false"
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provenanceaction="sysimport:actionset" trust="0.5338496">medicine.diagnostic_test
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="false"
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provenanceaction="sysimport:actionset" trust="0.5338496">medicine
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Human heart
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Modality (human–computer interaction)
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">3D reconstruction
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Computer science
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</subject>
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<subject classid="MAG" classname="Microsoft Academic Graph classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies">Nuclear magnetic resonance
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</subject>
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<subject classid="SDG" classname="Sustainable Development Goals"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:sdg">3. Good health
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">03 medical and health sciences
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">0302 clinical medicine
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">030218 Nuclear Medicine &
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Medical Imaging
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">03021801 Radiology/Image
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segmentation
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">03021801 Radiology/Image
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segmentation - deep learning/datum
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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||
schemeid="dnet:subject_classification_typologies"
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schemename="dnet:subject_classification_typologies" inferred="true"
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inferenceprovenance="update" provenanceaction="subject:fos">030204 Cardiovascular System
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& Hematology
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</subject>
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||
<subject classid="FOS" classname="Fields of Science and Technology classification"
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schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="true"
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||
inferenceprovenance="update" provenanceaction="subject:fos">03020401 Aging-associated
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diseases/Heart diseases
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</subject>
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<subject classid="FOS" classname="Fields of Science and Technology classification"
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||
schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="true"
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||
inferenceprovenance="update" provenanceaction="subject:fos">030217 Neurology &
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Neurosurgery
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||
</subject>
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||
<subject classid="FOS" classname="Fields of Science and Technology classification"
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||
schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="true"
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||
inferenceprovenance="update" provenanceaction="subject:fos">03021701 Brain/Neural circuits
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||
</subject>
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||
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">Articles
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">Research Articles
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">cardiac mesh reconstruction
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">cine MRI
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">misalignment correction
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">electrophysiological
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||
simulation
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">ECGI
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:actionset" trust="0.9">Heart
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:actionset" trust="0.9">Humans
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:actionset" trust="0.9">Imaging, Three-Dimensional
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
|
||
provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging, Cine
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||
</subject>
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||
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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||
schemename="dnet:subject_classification_typologies" inferred="false"
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||
provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Spectroscopy
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||
</subject>
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||
<language classid="und" classname="Undetermined" schemeid="dnet:languages"
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||
schemename="dnet:languages"/>
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||
<relevantdate classid="created" classname="created" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date">2021-10-25
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||
</relevantdate>
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||
<relevantdate classid="published-online" classname="published-online" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date">2021-10-25
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||
</relevantdate>
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||
<relevantdate classid="published-print" classname="published-print" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date">2021-12-13
|
||
</relevantdate>
|
||
<relevantdate classid="UNKNOWN" classname="UNKNOWN" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date" inferred="false"
|
||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">2021-01-01
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||
</relevantdate>
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||
<relevantdate classid="available" classname="available" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date" inferred="false"
|
||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">2023-01-05
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||
</relevantdate>
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||
<relevantdate classid="Accepted" classname="Accepted" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date" inferred="false"
|
||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">2021-05-28
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||
</relevantdate>
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||
<relevantdate classid="issued" classname="issued" schemeid="dnet:dataCite_date"
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||
schemename="dnet:dataCite_date" inferred="false"
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||
provenanceaction="sysimport:crosswalk:repository" trust="0.9">2023-01-05
|
||
</relevantdate>
|
||
<publisher>The Royal Society</publisher>
|
||
<source>Crossref</source>
|
||
<source/>
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||
<source>Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|
||
</source>
|
||
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||
schemename="dnet:dataCite_resource"/>
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||
<journal issn="1364-503X" eissn="1471-2962" vol="379">Philosophical Transactions of the Royal
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||
Society A: Mathematical, Physical and Engineering Sciences
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||
</journal>
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||
<context id="dth" label="Digital Twins in Health" type="community"></context>
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<context id="EC" label="European Commission" type="funding">
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<category id="EC::H2020" label="Horizon 2020 Framework Programme">
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||
<concept id="EC::H2020::RIA" label="Research and Innovation action"/>
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||
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||
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||
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||
<inferred>true</inferred>
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||
<deletedbyinference>false</deletedbyinference>
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||
<trust>0.8</trust>
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||
<inferenceprovenance>dedup-result-decisiontree-v3</inferenceprovenance>
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type="organization">openorgs____::6a7b1b4c40a067a1f209de6867fe094d
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<country classid="GB" classname="United Kingdom" schemeid="dnet:countries"
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schemename="dnet:countries"/>
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||
<legalname>University of Oxford</legalname>
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||
<legalshortname>University of Oxford</legalshortname>
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||
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doi_dedup___::015b27b0b7c55649236bf23a5c75f817
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schemename="dnet:pid_types" inferred="false" provenanceaction="sysimport:actionset"
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trust="0.9">10.6084/m9.figshare.15656924.v2
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</pid>
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