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Qingqing Yang - IEEE Xplore Author Profile

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Author details

Qingqing Yang

Publications

7

Citations

11

Publications by Year

20232025

Co-Authors:

Han ChengZhuoting HanXiaoning HeYuan LiHaolin Li

Show All Co-Authors (15)

Qingqing Yang

Affiliation

School of Information Engineering and Automation

Kunming University of Science and Technology

Kunming, China

Yunnan Provincial Key Laboratory of Computer Science

Kunming University of Science and Technology

Kunming, China

Publication Topics

Base Station,
Non-orthogonal Multiple Access,
Non-orthogonal Multiple Access System,
Reconfigurable Intelligent Surface,
Service Quality,
Additive Noise,
Joint Optimization,
Non-convex Problem,
Objective Function,
Optimization Problem,
Channel Gain,
Communication Systems

Biography

Qingqing Yang received the B.Sc. and M.Sc. degrees from Harbin Engineering University, in 2004 and 2007, respectively, and the Ph.D. degree from Kunming University of Science and Technology, in 2020. She is currently a Lecturer and a Master’s Tutor with the School of Information Engineering and Automation, Kunming University of Science and Technology. Her research interests include RIS and emergency communication.(Based on document published on 9 July 2024).

Publications

7

Citations

11

Publications by Year

20232025

Co-Authors:

Han Cheng
Zhuoting Han
Xiaoning He
Yuan Li
Haolin Li

Show All Co-Authors (15)

Author's Published Works

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Showing 1-25 of 43 results

Conferences (27)

Journals (15)

Magazines (1)

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Xavier Pennec(24)
X. Pennec(19)
N. Ayache(11)
Maxime Sermesant(8)
Nicholas Ayache(6)
Asclepios Research Project, INRIA, Sophia-Antipolis, France(5)
INRIA, Sophia-Antipolis, France(3)
Department of Computer Science, University of Copenhagen, Denmark(2)
I.N.R.I.A. Sophia Antipolis, Sophia-Antipolis, France(2)
Centre for Magnetic Resonance, University of Queensland, Brisbane, Australia(2)
IEEE Transactions on Medical Imaging(10)
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro(3)
2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro(3)
3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006.(3)
IEEE Transactions on Biomedical Engineering(3)
IEEE(43)
Arlington, VA, USA(4)
Paris, France(4)
Rotterdam, Netherlands(3)
Bordeaux, France(1)
Boston, MA, USA(1)
Registration Algorithm(10)
Medical Imaging(8)
Rigid Transformation(8)
Deformation Field(7)
Diffusion Tensor Imaging(7)

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Results

Geodesic squared exponential kernel for non-rigid shape registration

Florent Jousse;Xavier Pennec;Hervé Delingette;Matilde Gonzalez

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

Year: 2021 | Conference Paper |

Cited by: Papers (1)

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This work addresses the problem of non-rigid registration of 3D scans, which is at the core of shape modeling techniques. Firstly, we propose a new kernel based on geodesic distances for the Gaussian Process Morphable Models (GPMMs) framework. The use of geodesic distances into the kernel makes it more adapted to the topological and geometric characteristics of the surface and leads to more realis...Show More

Geodesic squared exponential kernel for non-rigid shape registration

Florent Jousse;Xavier Pennec;Hervé Delingette;Matilde Gonzalez

2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021)

Year: 2021 | Conference Paper |

Cardiac Motion Modeling With Parallel Transport And Shape Splines

Nicolas Guigui;Pamela Moceri;Maxime Sermesant;Xavier Pennec

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Year: 2021 | Conference Paper |

Cited by: Papers (4)

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In cases of pressure or volume overload, probing cardiac function may be difficult because of the interactions between shape and deformations. In this work, we use the LDDMM framework and parallel transport to estimate and reorient deformations of the right ventricle. We then propose a normalization procedure for the amplitude of the deformation, and a second-order spline model to represent the fu...Show More

Cardiac Motion Modeling With Parallel Transport And Shape Splines

Nicolas Guigui;Pamela Moceri;Maxime Sermesant;Xavier Pennec

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

Year: 2021 | Conference Paper |

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Cited by: Papers (1178)

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Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the large...Show More

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Olivier Bernard;Alain Lalande;Clement Zotti;Frederick Cervenansky;Xin Yang;Pheng-Ann Heng;Irem Cetin;Karim Lekadir;Oscar Camara;Miguel Angel Gonzalez Ballester;Gerard Sanroma;Sandy Napel;Steffen Petersen;Georgios Tziritas;Elias Grinias;Mahendra Khened;Varghese Alex Kollerathu;Ganapathy Krishnamurthi;Marc-Michel Rohé;Xavier Pennec;Maxime Sermesant;Fabian Isensee;Paul Jäger;Klaus H. Maier-Hein;Peter M. Full;Ivo Wolf;Sandy Engelhardt;Christian F. Baumgartner;Lisa M. Koch;Jelmer M. Wolterink;Ivana Išgum;Yeonggul Jang;Yoonmi Hong;Jay Patravali;Shubham Jain;Olivier Humbert;Pierre-Marc Jodoin

IEEE Transactions on Medical Imaging

Year: 2018 | Volume: 37, Issue: 11 | Journal Article |

Cardiac Motion Evolution Model for Analysis of Functional Changes Using Tensor Decomposition and Cross-Sectional Data

Kristin McLeod;Kristin Tøndel;Lilian Calvet;Maxime Sermesant;Xavier Pennec

IEEE Transactions on Biomedical Engineering

Year: 2018 | Volume: 65, Issue: 12 | Journal Article |

Cited by: Papers (4)

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Cardiac disease can reduce the ability of the ventricles to function well enough to sustain long-term pumping efficiency. Recent advances in cardiac motion tracking have led to improvements in the analysis of cardiac function. We propose a method to study cohort effects related to age with respect to cardiac function. The proposed approach makes use of a recent method for describing cardiac motion...Show More

Cardiac Motion Evolution Model for Analysis of Functional Changes Using Tensor Decomposition and Cross-Sectional Data

Kristin McLeod;Kristin Tøndel;Lilian Calvet;Maxime Sermesant;Xavier Pennec

IEEE Transactions on Biomedical Engineering

Year: 2018 | Volume: 65, Issue: 12 | Journal Article |

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Avan Suinesiaputra;Pierre Ablin;Xènia Albà;Martino Alessandrini;Jack Allen;Wenjia Bai;Serkan Çimen;Peter Claes;Brett R. Cowan;Jan D’hooge;Nicolas Duchateau;Jan Ehrhardt;Alejandro F. Frangi;Ali Gooya;Vicente Grau;Karim Lekadir;Allen Lu;Anirban Mukhopadhyay;Ilkay Oksuz;Nripesh Parajuli;Xavier Pennec;Marco Pereañez;Catarina Pinto;Paolo Piras;Marc-Michel Rohé;Daniel Rueckert;Dennis Säring;Maxime Sermesant;Kaleem Siddiqi;Mahdi Tabassian;Luciano Teresi;Sotirios A. Tsaftaris;Matthias Wilms;Alistair A. Young;Xingyu Zhang;Pau Medrano-Gracia

IEEE Journal of Biomedical and Health Informatics

Year: 2018 | Volume: 22, Issue: 2 | Journal Article |

Cited by: Papers (64)

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Statistical shape modeling is a powerful tool for visualizing and quantifying geometric and functional patterns of the heart. After myocardial infarction (MI), the left ventricle typically remodels in response to physiological challenges. Several methods have been proposed in the literature to describe statistical shape changes. Which method best characterizes the left ventricular remodeling after...Show More

Statistical Shape Modeling of the Left Ventricle: Myocardial Infarct Classification Challenge

Avan Suinesiaputra;Pierre Ablin;Xènia Albà;Martino Alessandrini;Jack Allen;Wenjia Bai;Serkan Çimen;Peter Claes;Brett R. Cowan;Jan D’hooge;Nicolas Duchateau;Jan Ehrhardt;Alejandro F. Frangi;Ali Gooya;Vicente Grau;Karim Lekadir;Allen Lu;Anirban Mukhopadhyay;Ilkay Oksuz;Nripesh Parajuli;Xavier Pennec;Marco Pereañez;Catarina Pinto;Paolo Piras;Marc-Michel Rohé;Daniel Rueckert;Dennis Säring;Maxime Sermesant;Kaleem Siddiqi;Mahdi Tabassian;Luciano Teresi;Sotirios A. Tsaftaris;Matthias Wilms;Alistair A. Young;Xingyu Zhang;Pau Medrano-Gracia

IEEE Journal of Biomedical and Health Informatics

Year: 2018 | Volume: 22, Issue: 2 | Journal Article |

Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches

Jan L. Bruse;Maria A. Zuluaga;Abbas Khushnood;Kristin McLeod;Hopewell N. Ntsinjana;Tain-Yen Hsia;Maxime Sermesant;Xavier Pennec;Andrew M. Taylor;Silvia Schievano

IEEE Transactions on Biomedical Engineering

Year: 2017 | Volume: 64, Issue: 10 | Journal Article |

Cited by: Papers (67)

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Objective: Today's growing medical image databases call for novel processing tools to structure the bulk of data and extract clinically relevant information. Unsupervised hierarchical clustering may reveal clusters within anatomical shape data of patient populations as required for modern precision medicine strategies. Few studies have applied hierarchical clustering techniques to three-dimensiona...Show More

Detecting Clinically Meaningful Shape Clusters in Medical Image Data: Metrics Analysis for Hierarchical Clustering Applied to Healthy and Pathological Aortic Arches

Jan L. Bruse;Maria A. Zuluaga;Abbas Khushnood;Kristin McLeod;Hopewell N. Ntsinjana;Tain-Yen Hsia;Maxime Sermesant;Xavier Pennec;Andrew M. Taylor;Silvia Schievano

IEEE Transactions on Biomedical Engineering

Year: 2017 | Volume: 64, Issue: 10 | Journal Article |

Highly reduced model of the cardiac function for fast simulation

Marc-Michel Rohé;Roch Molléro;Maxime Sermesant;Xavier Pennec

2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

Year: 2016 | Conference Paper |

Cited by: Papers (1)

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In this article we present a drastic dimension reduction method to link the biophysical parameters of an electromechanical model of the heart with a compact representation of cardiac motion. Our approach relies on a projection of the displacement fields along the whole cardiac motion to the space of reduced-polyaffine transformations. Using these transformations, not only we describe the motion us...Show More

Highly reduced model of the cardiac function for fast simulation

Marc-Michel Rohé;Roch Molléro;Maxime Sermesant;Xavier Pennec

2016 IEEE 12th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP)

Year: 2016 | Conference Paper |

Spatio-Temporal Tensor Decomposition of a Polyaffine Motion Model for a Better Analysis of Pathological Left Ventricular Dynamics

Kristin McLeod;Maxime Sermesant;Philipp Beerbaum;Xavier Pennec

IEEE Transactions on Medical Imaging

Year: 2015 | Volume: 34, Issue: 7 | Journal Article |

Cited by: Papers (30)

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Given that heart disease can cause abnormal motion dynamics over the cardiac cycle, understanding and quantifying cardiac motion can provide insight for clinicians to aid with diagnosis, therapy planning, and determining prognosis. The goal of this paper is to extract population-specific motion patterns from 3D displacements in order to identify the mean motion in a population, and to describe pat...Show More

Spatio-Temporal Tensor Decomposition of a Polyaffine Motion Model for a Better Analysis of Pathological Left Ventricular Dynamics

Kristin McLeod;Maxime Sermesant;Philipp Beerbaum;Xavier Pennec

IEEE Transactions on Medical Imaging

Year: 2015 | Volume: 34, Issue: 7 | Journal Article |

Sparsity and scale: Compact representations of deformation for diffeomorphic registration

Stefan Sommer;Mads Nielsen;Xavier Pennec

2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis

Year: 2012 | Conference Paper |

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In order to detect small-scale deformations during disease propagation while allowing large-scale deformation needed for inter-subject registration, we wish to model deformation at multiple scales and represent the deformation at the relevant scales only. With the LDDMM registration framework, enforcing sparsity results in compact representations but with limited ability to represent deformation a...Show More

Sparsity and scale: Compact representations of deformation for diffeomorphic registration

Stefan Sommer;Mads Nielsen;Xavier Pennec

2012 IEEE Workshop on Mathematical Methods in Biomedical Image Analysis

Year: 2012 | Conference Paper |

A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot

T. Mansi;I. Voigt;B. Leonardi;X. Pennec;S. Durrleman;M. Sermesant;H. Delingette;A. M. Taylor;Y. Boudjemline;G. Pongiglione;N. Ayache

IEEE Transactions on Medical Imaging

Year: 2011 | Volume: 30, Issue: 9 | Journal Article |

Cited by: Papers (67) | Patents (1)

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Cardiac remodelling plays a crucial role in heart diseases. Analyzing how the heart grows and remodels over time can provide precious insights into pathological mechanisms, eventually resulting in quantitative metrics for disease evaluation and therapy planning. This study aims to quantify the regional impacts of valve regurgitation and heart growth upon the end-diastolic right ventricle (RV) in p...Show More

A Statistical Model for Quantification and Prediction of Cardiac Remodelling: Application to Tetralogy of Fallot

T. Mansi;I. Voigt;B. Leonardi;X. Pennec;S. Durrleman;M. Sermesant;H. Delingette;A. M. Taylor;Y. Boudjemline;G. Pongiglione;N. Ayache

IEEE Transactions on Medical Imaging

Year: 2011 | Volume: 30, Issue: 9 | Journal Article |

A Nonconservative Lagrangian Framework for Statistical Fluid Registration—SAFIRA

Caroline C. Brun;Natasha Leporé;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Greig de Zubicaray;Katie L. McMahon;Margaret J. Wright;James C. Gee;Paul M. Thompson

IEEE Transactions on Medical Imaging

Year: 2011 | Volume: 30, Issue: 2 | Journal Article |

Cited by: Papers (15)

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In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3-D brain images. This algorithm is named SAFIRA, acronym for statistically-assisted fluid image registration algorithm. A nonstatistical version of this algorithm was implemented , where the deformation was regularized by penalizing deviations from a zero rate ...Show More

A Nonconservative Lagrangian Framework for Statistical Fluid Registration—SAFIRA

Caroline C. Brun;Natasha Leporé;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Greig de Zubicaray;Katie L. McMahon;Margaret J. Wright;James C. Gee;Paul M. Thompson

IEEE Transactions on Medical Imaging

Year: 2011 | Volume: 30, Issue: 2 | Journal Article |

Statistically assisted fluid image registration algorithm - SAFIRA

Caroline C. Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Greig I. de Zubicaray;Katie L. McMahon;Margaret J. Wright;Paul M. Thompson

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

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In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in and re-formulated using Lagrangian mechanics in. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their corresponding deformation matrices are computed in a fir...Show More

Statistically assisted fluid image registration algorithm - SAFIRA

Caroline C. Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Greig I. de Zubicaray;Katie L. McMahon;Margaret J. Wright;Paul M. Thompson

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

Lung CT registration combining intensity, curves and surfaces

Vladlena Gorbunova;Stanley Durrleman;Pechin Lo;Xavier Pennec;Marleen de Bruijne

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

Cited by: Papers (9)

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In this paper we propose a registration method that combines intensity information with geometrical information in the form of curves and surfaces derived from lung CT images. Vessel tree centerlines and lung surfaces were extracted from segmented structures. First, a current-based registration was applied to align the pulmonary vessel tree and the lung surfaces. Subsequently, the resulting deform...Show More

Lung CT registration combining intensity, curves and surfaces

Vladlena Gorbunova;Stanley Durrleman;Pechin Lo;Xavier Pennec;Marleen de Bruijne

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

Parametric regression of 3D medical images through the exploration of non-parametric regression models

Christof Seiler;Xavier Pennec;Mauricio Reyes

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

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Currently there is an increase usage of CT-based bone diagnosis because low-radiation and cost-effective 2D imaging modalities do not provide the necessary 3D information for bone diagnosis. The fundamental objective of our work is to build a model connecting 2D X-ray information to 3D CT information through regression. As a first step we propose an univariate non-parametric regression on individu...Show More

Parametric regression of 3D medical images through the exploration of non-parametric regression models

Christof Seiler;Xavier Pennec;Mauricio Reyes

2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2010 | Conference Paper |

A Lagrangian formulation for statistical fluid registration

Caroline C. Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Greig I. de Zubicaray;Katie L. McMahon;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2009 | Conference Paper |

Cited by: Papers (1)

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We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combine the advantages of a large-deformation fluid match...Show More

A Lagrangian formulation for statistical fluid registration

Caroline C. Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Greig I. de Zubicaray;Katie L. McMahon;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2009 | Conference Paper |

DT-REFinD: Diffusion Tensor Registration With Exact Finite-Strain Differential

B.T. Thomas Yeo;Tom Vercauteren;Pierre Fillard;Jean-Marc Peyrat;Xavier Pennec;Polina Golland;Nicholas Ayache;Olivier Clatz

IEEE Transactions on Medical Imaging

Year: 2009 | Volume: 28, Issue: 12 | Journal Article |

Cited by: Papers (70)

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In this paper, we propose the DT-REFinD algorithm for the diffeomorphic nonlinear registration of diffusion tensor images. Unlike scalar images, deforming tensor images requires choosing both a reorientation strategy and an interpolation scheme. Current diffusion tensor registration algorithms that use full tensor information face difficulties in computing the differential of the tensor reorientat...Show More

DT-REFinD: Diffusion Tensor Registration With Exact Finite-Strain Differential

B.T. Thomas Yeo;Tom Vercauteren;Pierre Fillard;Jean-Marc Peyrat;Xavier Pennec;Polina Golland;Nicholas Ayache;Olivier Clatz

IEEE Transactions on Medical Imaging

Year: 2009 | Volume: 28, Issue: 12 | Journal Article |

A framework for evaluating the impact of compression on registration algorithms without gold standard

Tristan Glatard;Johan Montagnat;Xavier Pennec

2008 15th IEEE International Conference on Image Processing

Year: 2008 | Conference Paper |

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The impact of lossy compression has often been discussed in the medical area. In this study, an evaluation of the impact of lossy compression on the performance of rigid registration algorithms for medical images is proposed. The robustness, repeatability and accuracy of these algorithms is estimated through a statistical procedure for each compression ratio. Results are obtained thanks to a grid ...Show More

A framework for evaluating the impact of compression on registration algorithms without gold standard

Tristan Glatard;Johan Montagnat;Xavier Pennec

2008 15th IEEE International Conference on Image Processing

Year: 2008 | Conference Paper |

A new registration method based on Log-Euclidean Tensor metrics and its application to genetic studies

Caroline Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Grieg de Zubicaray;Katie McMahon;Margie Wright;Marina Barysheva;Arthur W. Toga;Paul M. Thompson

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

Cited by: Papers (5)

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In structural brain MRI, group differences or changes in brain structures can be detected using tensor-based morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerous registration methods that have recently been developed differ in their detection sensitivity ...Show More

A new registration method based on Log-Euclidean Tensor metrics and its application to genetic studies

Caroline Brun;Natasha Lepore;Xavier Pennec;Yi-Yu Chou;Agatha D. Lee;Grieg de Zubicaray;Katie McMahon;Margie Wright;Marina Barysheva;Arthur W. Toga;Paul M. Thompson

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

Best individual template selection from deformation tensor minimization

Natasha Lepore;Caroline Brun;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Xavier Pennec;Katie L. McMahon;Matthew Meredith;Greig I. de Zubicaray;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

Cited by: Papers (10)

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We study the influence of the choice of template in tensor- based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on average than random pairings, consistent with evidence that brain structure is ...Show More

Best individual template selection from deformation tensor minimization

Natasha Lepore;Caroline Brun;Yi-Yu Chou;Agatha D. Lee;Marina Barysheva;Xavier Pennec;Katie L. McMahon;Matthew Meredith;Greig I. de Zubicaray;Margaret J. Wright;Arthur W. Toga;Paul M. Thompson

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

DTI registration with exact finite-strain differential

B.T. Thomas Yeo;Tom Vercauteren;Pierre Fillard;Xavier Pennec;Polina Golland;Nicholas Ayache;Olivier Clatz

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

Cited by: Papers (18)

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We propose an algorithm for the diffeomorphic registration of diffusion tensor images (DTI). Previous DTI registration algorithms using full tensor information suffer from difficulties in computing the differential of the Finite Strain tensor reorientation strategy. We borrow results from computer vision to derive an analytical gradient of the objective function. By leveraging on the closed-form g...Show More

DTI registration with exact finite-strain differential

B.T. Thomas Yeo;Tom Vercauteren;Pierre Fillard;Xavier Pennec;Polina Golland;Nicholas Ayache;Olivier Clatz

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Year: 2008 | Conference Paper |

Articulated Spine Models for 3-D Reconstruction From Partial Radiographic Data

Jonathan Boisvert;Farida Cheriet;Xavier Pennec;Hubert Labelle;Nicholas Ayache

IEEE Transactions on Biomedical Engineering

Year: 2008 | Volume: 55, Issue: 11 | Journal Article |

Cited by: Papers (35) | Patents (1)

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Three-dimensional models of the spine are extremely important to the assessment of spinal deformities. However, it could be difficult in practical situations to obtain enough accurate information to reconstruct complete 3-D models. This paper presents a set of methods to rebuild complete models either from partial 3-D models or from 2-D landmarks. The spine was modeled as an articulated object to ...Show More

Articulated Spine Models for 3-D Reconstruction From Partial Radiographic Data

Jonathan Boisvert;Farida Cheriet;Xavier Pennec;Hubert Labelle;Nicholas Ayache

IEEE Transactions on Biomedical Engineering

Year: 2008 | Volume: 55, Issue: 11 | Journal Article |

A Probabilistic Model to Analyse Workflow Performance on Production Grids

Tristan Glatard;Johan Montagnat;Xavier Pennec

2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID)

Year: 2008 | Conference Paper |

Cited by: Papers (14)

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Production grids are complex and highly variable systems whose behavior is not well understood and difficult to anticipate. The goal of this study is to estimate the impact of the variability of those infrastructures on the performance of workflow-based applications. A probabilistic model of workflows execution time is proposed and evaluated. Results show that the variability of the EGEE grid infr...Show More

A Probabilistic Model to Analyse Workflow Performance on Production Grids

Tristan Glatard;Johan Montagnat;Xavier Pennec

2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID)

Year: 2008 | Conference Paper |

Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models

Jonathan Boisvert;Farida Cheriet;Xavier Pennec;Hubert Labelle;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2008 | Volume: 27, Issue: 4 | Journal Article |

Cited by: Papers (65) | Patents (2)

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This paper introduces a method to analyze the variability of the spine shape and of the spine shape deformations using articulated shape models. The spine shape was expressed as a vector of relative poses between local coordinate systems of neighboring vertebrae. Spine shape deformations were then modeled by a vector of rigid transformations that transforms one spine shape into another. Because ri...Show More

Geometric Variability of the Scoliotic Spine Using Statistics on Articulated Shape Models

Jonathan Boisvert;Farida Cheriet;Xavier Pennec;Hubert Labelle;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2008 | Volume: 27, Issue: 4 | Journal Article |

A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts

Jean-Marc Peyrat;Maxime Sermesant;Xavier Pennec;HervÉ Delingette;Chenyang Xu;Elliot R. McVeigh;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 11 | Journal Article |

Cited by: Papers (104) | Patents (11)

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We propose a unified computational framework to build a statistical atlas of the cardiac fiber architecture from diffusion tensor magnetic resonance images (DT-MRIs). We apply this framework to a small database of nine ex vivo canine hearts. An average cardiac fiber architecture and a measure of its variability are computed using most recent advances in diffusion tensor statistics. This statistica...Show More

A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts

Jean-Marc Peyrat;Maxime Sermesant;Xavier Pennec;HervÉ Delingette;Chenyang Xu;Elliot R. McVeigh;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 11 | Journal Article |

Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics

Pierre Fillard;Xavier Pennec;Vincent Arsigny;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 11 | Journal Article |

Cited by: Papers (186) | Patents (1)

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Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the expense of the image quality. This often results in DTI datasets that are not suitable for complex postprocessing like fiber tracking. We propose a new variational framework to impr...Show More

Clinical DT-MRI Estimation, Smoothing, and Fiber Tracking With Log-Euclidean Metrics

Pierre Fillard;Xavier Pennec;Vincent Arsigny;Nicholas Ayache

IEEE Transactions on Medical Imaging

Year: 2007 | Volume: 26, Issue: 11 | Journal Article |

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