TY - GEN
T1 - Statistical Analysis of Left Ventricular Remodeling Following a Myocardial Infarct
AU - Underbjerg Hansen, Cathrine
AU - Micheelsen Lowes, Mathias
AU - Ohrt Johansen, Andreas
AU - Fuglsang Kofoed, Klaus
AU - Tobias Kühl, Jørgen
AU - Aasbjerg Nielsen, Allan
AU - R. Paulsen, Rasmus
AU - Vilsbøll Sundgaard, Josefine
AU - Aavild Sørensen, Kristine
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
PY - 2025
Y1 - 2025
N2 - Myocardial infarct (MI) is a leading cause of mortality worldwide. It is known to cause left ventricular remodeling, characterized by changes in left ventricle myocardial size, shape, and function. Quantifying these changes is crucial for evaluating the progression of heart diseases following MI. Traditional clinical measurements primarily rely on the volume of the left ventricle (LV) from temporal imaging. In this work, we focus on detecting left ventricular remodeling from a single-phase Cardiac Computed Tomography Angiography (CCTA) using statistical shape analysis. Our pipeline consists of a template registration using implicit neural representations, followed by a statistical shape analysis on the mesh of the LV to classify between healthy individuals and infarct individuals with previous MI. Several methods for dimensionality reduction and classification are evaluated, with Partial Least Squares (PLS) regression achieving the highest classification accuracy of 96%. The PLS components can also be interpreted as directions of healthy vs. infarct LV shapes.
AB - Myocardial infarct (MI) is a leading cause of mortality worldwide. It is known to cause left ventricular remodeling, characterized by changes in left ventricle myocardial size, shape, and function. Quantifying these changes is crucial for evaluating the progression of heart diseases following MI. Traditional clinical measurements primarily rely on the volume of the left ventricle (LV) from temporal imaging. In this work, we focus on detecting left ventricular remodeling from a single-phase Cardiac Computed Tomography Angiography (CCTA) using statistical shape analysis. Our pipeline consists of a template registration using implicit neural representations, followed by a statistical shape analysis on the mesh of the LV to classify between healthy individuals and infarct individuals with previous MI. Several methods for dimensionality reduction and classification are evaluated, with Partial Least Squares (PLS) regression achieving the highest classification accuracy of 96%. The PLS components can also be interpreted as directions of healthy vs. infarct LV shapes.
KW - Cardiac Computed Tomography Angiography
KW - Implicit Neural Representations
KW - Partial Least Squares
KW - Statistical Shape Analysis
UR - https://www.scopus.com/pages/publications/105009868203
U2 - 10.1007/978-3-031-95911-0_11
DO - 10.1007/978-3-031-95911-0_11
M3 - Article in proceedings
AN - SCOPUS:105009868203
SN - 978-3-031-95910-3
T3 - Lecture Notes in Computer Science
SP - 147
EP - 160
BT - Image Analysis - 23rd Scandinavian Conference, SCIA 2025, Proceedings
A2 - Petersen, Jens
A2 - Dahl, Vedrana Andersen
PB - Springer
T2 - 23rd Scandinavian Conference on Image Analysis, SCIA 2025
Y2 - 23 June 2025 through 25 June 2025
ER -