TY - GEN
T1 - From CMR Image to Patient-Specific Simulation and Population-Based Analysis
T2 - 7th International Workshop on Statistical Atlases and Computational Models of the Heart Imaging and Modelling Challenges, STACOM 2016 Held in Conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
AU - Marciniak, Maciej
AU - Arevalo, Hermenegild
AU - Tfelt-Hansen, Jacob
AU - Jespersen, Thomas
AU - Jabbari, Reza
AU - Glinge, Charlotte
AU - Ahtarovski, Kiril A.
AU - Vejlstrup, Niels
AU - Engstrom, Thomas
AU - Maleckar, Mary M.
AU - McLeod, Kristin
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Cardiac magnetic resonance (CMR) imaging is becoming a routine diagnostic and therapy planning tool for some cardiovascular diseases. It is still challenging to properly analyse the acquired data, and the currently available measures do not exploit the rich characteristics of that data. Advanced analysis and modelling techniques are increasingly used to extract additional information from the images, in order to define metrics describing disease manifestations and to quantitatively compare patients. Many techniques share a common bottleneck caused by the image processing required to segment the images and convert the segmentation to a usable computational domain for analysis/modelling. To address this, we present a comprehensive pipeline to go from CMR images to computational bi-ventricle meshes. The latter can be used for biophysical simulations or statistical shape analysis. The provided tutorial describes each step and the proposed pipeline, which makes use of tools that are available open-source. The pipeline was applied to a data-set of myocardial infarction patients, from late gadolinium enhanced CMR images, to analyse and compare structure in these patients. Examples of applications present the use of the output of the pipeline for patient-specific biophysical simulations and population-based statistical shape analysis.
AB - Cardiac magnetic resonance (CMR) imaging is becoming a routine diagnostic and therapy planning tool for some cardiovascular diseases. It is still challenging to properly analyse the acquired data, and the currently available measures do not exploit the rich characteristics of that data. Advanced analysis and modelling techniques are increasingly used to extract additional information from the images, in order to define metrics describing disease manifestations and to quantitatively compare patients. Many techniques share a common bottleneck caused by the image processing required to segment the images and convert the segmentation to a usable computational domain for analysis/modelling. To address this, we present a comprehensive pipeline to go from CMR images to computational bi-ventricle meshes. The latter can be used for biophysical simulations or statistical shape analysis. The provided tutorial describes each step and the proposed pipeline, which makes use of tools that are available open-source. The pipeline was applied to a data-set of myocardial infarction patients, from late gadolinium enhanced CMR images, to analyse and compare structure in these patients. Examples of applications present the use of the output of the pipeline for patient-specific biophysical simulations and population-based statistical shape analysis.
U2 - 10.1007/978-3-319-52718-5_12
DO - 10.1007/978-3-319-52718-5_12
M3 - Article in proceedings
AN - SCOPUS:85011355676
SN - 9783319527178
T3 - Lecture Notes in Computer Science
SP - 106
EP - 117
BT - Statistical Atlases and Computational Models of the Heart
PB - Springer
Y2 - 17 October 2016 through 21 October 2016
ER -
