@article {25224,
	title = {Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering applied to Healthy and Pathological Aortic Arches},
	journal = {IEEE Transactions on Biomedical Engineering},
	volume = {64},
	year = {2017},
	month = {2017},
	pages = {2373 - 2383},
	publisher = {ACM IEEE},
	keywords = {Medical diagnostic imaging, principal component analysis, Shape, Sociology, Three-dimensional displays},
	doi = {10.1109/TBME.2017.2655364},
	url = {http://ieeexplore.ieee.org/abstract/document/7857751/},
	author = {Jan L {Bruse} and Khushnood, Abbas and Kristin Sarah {Mcleod} and Hopewell N. {Ntsinjana} and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {25225,
	title = {Improving Understanding of Long-Term Cardiac Functional Remodelling via Cross-Sectional Analysis of Polyaffine Motion Parameters},
	journal = {Functional Imaging and Modelling of the Heart},
	year = {2017},
	publisher = { Springer},
	author = {Kristin Sarah {Mcleod} and Sermesant, Maxime and Pennec, Xavier},
	editor = {Pop, Mihaela and Wright, Graham}
}
@book {25028,
	title = {Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges},
	series = {7th International Workshop, STACOM 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers},
	year = {2017},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Berlin Heidelberg},
	doi = {10.1007/978-3-319-52718-5},
	url = {http://www.springer.com/gp/book/9783319527178},
	author = {Mansi, Tommaso and Kristin Sarah {Mcleod} and Pop, Mihaela and Rhode, Kawal and Sermesant, Maxime and Young, Alistair}
}
@inproceedings {23830,
	title = {CMR-based 3D Statistical Shape Modelling Reveals Left Ventricular Morphological Differences Between Healthy Controls and Arterial Switch Operation Survivors},
	journal = {19th Annual Scientific Sessions of the Society for Cardiovascular Magnetic Resonance (SCMR)},
	year = {2016},
	note = {Walking Poster Session},
	month = {01/2016},
	publisher = {Simula},
	author = {Jan L {Bruse} and Hopewell N. {Ntsinjana} and Capelli, Claudio and Biglino, Giovanni and Kristin Sarah {Mcleod} and Sermesant, Maxime and Hsia, Tain-Yen and Schievano, Silvia and Andrew M. {Taylor}},
	editor = {Pennec, Xavier}
}
@article {24892,
	title = {How Successful is Successful? Aortic Arch Shape Following Successful Aortic Coarctation Repair Correlates with Left Ventricular Function},
	journal = {The Journal of Thoracic and Cardiovascular Surgery},
	volume = {153},
	year = {2016},
	pages = {418-427},
	publisher = {Elsevier},
	doi = {http://dx.doi.org/10.1016/j.jtcvs.2016.09.018},
	url = {http://www.jtcvsonline.org/article/S0022-5223(16)31136-9/abstract},
	author = {Jan L {Bruse} and Khushnood, Abbas and Kristin Sarah {Mcleod} and Biglino, Giovanni and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Hsia, Tain-Yen and Schievano, Silvia}
}
@article {24568,
	title = {Looks do matter: Aortic arch shape following hypoplastic left heart syndrome  palliation correlates with cavopulmonary outcomes},
	journal = {Annals of Thoracic Surgery},
	volume = {103},
	number = {2},
	year = {2016},
	pages = {645-654},
	publisher = {Society of Thoracic Surgeons},
	doi = {http://dx.doi.org/10.1016/j.athoracsur.2016.06.041},
	url = {http://www.annalsthoracicsurgery.org/article/S0003-4975(16)30748-2/fulltext},
	author = {Jan L {Bruse} and Cervi, Elena and Kristin Sarah {Mcleod} and Biglino, Giovanni and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia and Hsia, Tain-Yen}
}
@inproceedings {23829,
	title = {Looks Do Matter! Aortic Arch Shape Following Hypoplastic Left Heart Syndrome Palliation Correlates with Cavopulmonary Physiology and Outcomes},
	journal = {52nd Annual Meeting of The Society of Thoracic Surgeons},
	year = {2016},
	month = {01/2016},
	publisher = { },
	author = {Jan L {Bruse} and Cervi, Elena and Kristin Sarah {Mcleod} and Biglino, Giovanni and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia},
	editor = {Hsia, Tain-Yen}
}
@article {24521,
	title = {A statistical shape modelling framework to extract 3D shape biomarkers from medical imaging data: Assessing arch morphology of repaired coarctation of the aorta},
	journal = {BMC Medical Imaging},
	volume = {16},
	number = {1},
	year = {2016},
	month = {06/2016},
	pages = {40},
	publisher = {BMC},
	doi = {10.1186/s12880-016-0142-z},
	url = {http://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-016-0142-z},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Hopewell N. {Ntsinjana} and Capelli, Claudio and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {23823,
	title = {Descriptive and Intuitive Population-Based Cardiac Motion Analysis via Sparsity Constrained Tensor Decomposition.},
	journal = {Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015},
	volume = {9351},
	year = {2015},
	note = {Poster Presentation by M. Sermesant and X. Pennec},
	month = {10/2015},
	pages = {419-426},
	publisher = {Springer International Publishing},
	abstract = {Analysing and understanding population-specific cardiac function is a challenging task due to the complex dynamics observed in both healthy and diseased subjects and the difficulty in quantitatively comparing the motion in different subjects. Affine parameters extracted from a Polyaffine motion model for a group of subjects can be used to represent the 3D motion regionally over time for a group of subjects. We propose to construct from these parameters a 4-way tensor of the rotation, stretch, shear, and translation components of each affine matrix defined in an intuitive coordinate system, stacked per region, for each affine component, over time, and for all subjects. From this tensor, Tucker decomposition can be applied with a constraint of sparsity on the core tensor in order to extract a few key, easily interpretable modes for each subject. Using this construction of a data tensor, the tensors of multiple groups can be stacked and collectively decomposed in order to compare and discriminate the motion by analysing the different loadings of each combination of modes for each group. The proposed method was applied to study and compare left ventricular dynamics for a group of healthy adult subjects and a group of adults with repaired Tetralogy of Fallot.},
	isbn = {978-3-319-24574-4},
	issn = {0302-9743},
	doi = {10.1007/978-3-319-24574-4_50},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-24574-4_50},
	author = {Kristin Sarah {Mcleod} and Sermesant, Maxime and Beerbaum, Philipp and Pennec, Xavier}
}
@inproceedings {23828,
	title = {Making Sense of Streamlines: A Statistical Shape-Modelling Approach to Detect Flow Patterns in the Aorta},
	journal = {3rd CMR 4D Flow Workshop},
	year = {2015},
	note = {Poster Presentation - Jan L. Bruse},
	month = {06/2015},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Pennec, Xavier and Sermesant, Maxime and Hsia, Tain-Yen and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {23826,
	title = {A Non-parametric Statistical Shape Model for Assessment of the Surgically Repaired Aortic Arch in Coarctation of the Aorta: How Normal is Abnormal?},
	journal = {Statistical Atlases and Computational Modeling of the Heart (STACOM 2015)},
	year = {2015},
	note = {Oral Presentation - Jan L. Bruse},
	month = {10/2015},
	publisher = {Lecture Notes in Computer Science, Springer. Verlag},
	edition = {MICCAI Workshop},
	abstract = {Coarctation of the Aorta (CoA) is a cardiac defect that re- quires surgical intervention aiming to restore an unobstructed aortic arch shape. Many patients suffer from complications post-repair, which are commonly associated with arch shape abnormalities. Determining the degree of shape abnormality could improve risk stratification in recom- mended screening procedures. Yet, traditional morphometry struggles to capture the highly complex arch geometries. Therefore, we use a non- parametric Statistical Shape Model based on mathematical currents to fully account for 3D global and regional shape features. By comput- ing a template aorta of a population of healthy subjects and analysing its transformations towards CoA arch shape models using Partial Least Squares regression techniques, we derived a shape vector as a measure of subject-specific shape abnormality. Results were compared to a shape ranking by clinical experts. Our study suggests Statistical Shape Mod- elling to be a promising diagnostic tool for improved screening of complex cardiac defects.\&nbsp;},
	keywords = {Aortic Arch, Coarctation of the Aorta, Mathematical Currents, Non-parametric Statistical Shape Model, Partial Least Square Regression},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Hopewell N. {Ntsinjana} and Capelli, Claudio and Hsia, Tain-Yen and Sermesant, Maxime and Pennec, Xavier and Taylor, Andrew and Schievano, Silvia}
}
@article {23825,
	title = {Spatio-Temporal Tensor Decomposition of a Polyaffine Motion Model for a Better Analysis of Pathological Left Ventricular Dynamics},
	journal = {IEEE Transactions on Medical Imaging, Institute of Electrical and Electronics Engineers},
	volume = {34},
	year = {2015},
	month = {02/2015},
	pages = {1562-1575},
	publisher = {IEEE},
	abstract = {Given that heart disease can cause abnormal mo- tion dynamics over the cardiac cycle, which can then affect cardiac function, understanding and quantifying cardiac motion can provide insight for clinicians to aid in diagnosis, therapy planning, as well as to determine the prognosis for a given patient. The goal of this paper is to extract population-specific cardiac motion patterns from 3D displacements in order to firstly identify the mean motion behaviour in a population and secondly to describe pathology-specific motion patterns in terms of the spatial and temporal aspects of the motion. Since there are common motion patterns observed in patients suffering from the same condition, extracting these patterns can lead towards a better understanding of a disease. Quantifying cardiac motion at a population level is not a simple task since images can vary widely in terms of image quality, size, resolution and pose. To overcome this, we analyse the parameters obtained from a cardiac-specific Polyaffine motion tracking algorithm, which are aligned both spatially and temporally to a common reference space. Once all parameters are aligned, different subjects and different populations can be compared and analysed in the space of Polyaffine transformations by projecting the transformations to a reduced-order subspace in which dominant motion patterns in each population can be extracted and analysed. Using tensor decomposition allows the spatial and temporal aspects to be decoupled in order to study the different components individually. The proposed method was validated on healthy volunteers and Tetralogy of Fallot patients according to known spatial and temporal behaviour for each population. A key advantage of the proposed method is the ability to regenerate motion sequences from the respective models, thus the models can be visualised in terms of the full motion, which allows for better understanding of the motion dynamics of different populations.\&nbsp;},
	author = {Kristin Sarah {Mcleod} and Sermesant, Maxime and Beerbaum, Philipp and Pennec, Xavier}
}
@article {MedIA2014,
	title = {Group-Wise Construction of Reduced Models for Understanding and Characterization of Pulmonary Blood Flows From Medical Images},
	journal = {Medical Image Analysis},
	volume = {18},
	number = {1},
	year = {2014},
	month = {January},
	pages = {63-82},
	publisher = { },
	doi = {10.1016/j.media.2013.09.003},
	author = {Kristin Sarah {Mcleod} and Guibert, Romain and Caiazzo, Alfonso and Mansi, Tommaso and Miguel A. {Fern{\`a}ndez} and Sermesant, Maxime and Pennec, Xavier and Irene E. {Vignon-Clementel} and Boudjemline, Younes and Gerbeau, Jean-Fr{\`e}d{\`e}ric}
}
@inproceedings {Frontiers2014,
	title = {Shape Atlas of the Surgically Reconstructed Aortic Arch in Patients With Hypoplastic Left Heart Syndrome},
	journal = {4th International Conference on Engineering Frontiers in Pediatric and Congenital Heart Disease},
	year = {2014},
	note = {Oral Presentation at the 4th International Conference on Engineering Frontiers in Pediatric and Congenital Heart Disease},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Pennec, Xavier and Sermesant, Maxime and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {WCB2014,
	title = {A Statistical Shape Atlas of the Surgically Reconstructed Aortic Arch in Patients With Hypoplastic Left Heart Syndrome},
	journal = {World Congress of Biomechanics},
	year = {2014},
	note = {Poster Presentation at the World Congress of Biomechanics, Boston 2014},
	month = {June},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Hsia, Tain-Yen and Pennec, Xavier and Sermesant, Maxime and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {Shapesymposium2014,
	title = {Statistical Shape Atlas of the Surgically Reconstructed Aortic Arch in Children With Hypoplastic Left Heart Syndrome},
	journal = {Shape 2014 - Symposium on Statistical Shape Models \& Applications},
	year = {2014},
	note = {Oral Presentation at the Shape Symposium},
	month = {June},
	author = {Jan L {Bruse} and Kristin Sarah {Mcleod} and Biglino, Giovanni and Cervi, Elena and Hsia, Tain-Yen and Pennec, Xavier and Sermesant, Maxime and Andrew M. {Taylor} and Schievano, Silvia}
}
@inproceedings {Simula.SC.204,
	title = {Evolution of Intracellular Ca2+ Waves From About 10,000 RyR Clusters: Towards Solving a Computationally Daunting Task},
	journal = {The Fifth International Conference on Functional Imaging and Modeling of the Heart},
	year = {2009},
	pages = {11-20},
	publisher = {Springer},
	type = {Conference},
	doi = {10.1007/978-3-642-01932-6},
	author = {Li, Pan and Wei, Wenjie and Cai, Xing and Soeller, Christian and Cannell, Mark and Arun V. {Holden}},
	editor = {Ayache, Nicholas and Delingette, Herv{\'e} and Sermesant, Maxime}
}
