Project

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Novel Image Processing Methods for Fetal MR Imaging: 3D Reconstruction and Segmentation with Soft Priors

Applicant Bach Cuadra Meritxell
Number 141283
Funding scheme Project funding (Div. I-III)
Research institution Département de radiologie médicale Centre Hospitalier Universitaire Vaudois University of Lausanne
Institution of higher education University of Lausanne - LA
Main discipline Electrical Engineering
Start/End 01.10.2012 - 30.09.2016
Approved amount 345'050.00
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All Disciplines (3)

Discipline
Electrical Engineering
Neurophysiology and Brain Research
Biomedical Engineering

Keywords (7)

Convex Minimization ; Sparsity priors; Variational Segmentation; Fetal imaging; Super-resolution Reconstruction; Magnetic Resonance Imaging; Inverse problem

Lay Summary (English)

Lead
Lay summary

Today, medical imaging for human brain studies, neuroimaging, is mostly dedicated to children and adults. Recent advances in clinical imaging of the fetus provide an unprecedented opportunity to image the process of human brain growth in utero.

Despite new fast Magnetic Resonance Imaging (MRI) techniques allow high contrast imaging of fetal brain tissues, clinical acquisitions still have many critical limitations that restrain the use of computer-based methods for large-scale analysis. Consequently, the research project presented here is oriented to develop solid mathematical framework for the fetal MRI reconstruction as well as for robust and accurate segmentation methods of fetuses at second and third trimester of pregnancy.

The automated quantitative analysis of structural fetal MRI in this project will help addressing the fundamental neuroscience question of early brain development. Moreover, in a clinical perspective, our quantitative studies of fetal MRI will help to better characterize the timing and the nature of prenatal pathologies, like for instance ventriculomegaly or intra-uterine growth restriction.


Direct link to Lay Summary Last update: 21.02.2013

Responsible applicant and co-applicants

Employees

Publications

Publication
Automated template-based brain localization and extraction for fetal brain MRI reconstruction
Tourbier Sébastien, Velasco-Annis Clemente, Taimouri Vahid, Hagmann Patric, Meuli Reto, Warfield Simon K., Bach Cuadra Meritxell, Gholipour Ali (2017), Automated template-based brain localization and extraction for fetal brain MRI reconstruction, in NeuroImage, 155, 460-472.
Quantification of Fetal Cortical Folding using Slice-to-Volume Reconstructed MRI and FreeSurfer
Tourbuer S., Schaer M., Warfield S.K., Meuli R., Gholipour A., Bach Cuadra M. (2016), Quantification of Fetal Cortical Folding using Slice-to-Volume Reconstructed MRI and FreeSurfer, in Proceeding of OHBM 22nd Annual Meeting, Organization for Human Brain Mapping, Geneva.
An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization
Tourbier Sébastien, Bresson Xavier, Hagmann Patric, Thiran Jean Philippe, Meuli Reto, Bach Cuadra Meritxell (2015), An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization, in NeuroImage, 584-597.
Automatic brain extraction in fetal MRI using multi-atlas-based segmentation
Tourbier Sébastien, Hagmann Patric, Cagneaux Maud, Guibaud Laurent, Gorthi Subrahmanyam, Schaer Marie, Thiran Jean Philippe, Meuli Reto, Bach Cuadra Meritxell (2015), Automatic brain extraction in fetal MRI using multi-atlas-based segmentation, in Proc. SPIE 9413, Medical Imaging 2015: Image Processing, Orlando, Florida, United StatesProc. SPIE 9413, Medical Imaging 2015: Image Processing, Orlando, Florida, United States.
Fully Automated Fetal Brain MRI Reconstruction
Tourbier Sébastien, Taimouri Vahid, Hagmann Patric, Velasco-Annis Clemente, Meuli Reto, Warfield Simon, Bach Cuadra Meritxell, Gholipour Ali (2015), Fully Automated Fetal Brain MRI Reconstruction, in INTELLIGENT IMAGING LINKING MR ACQUISITION AND PROCESSING Workshop, Munich, GermanyMICCAI, Munich, Germany.
Automated brain extraction in fetal MRI by multi-atlas fusion strategy: Study on healthy and pathological subjects
Tourbier Sébastien, Bresson Xavier, Hagmann Patric, Cagneaux Maud, Schaer Marie, Guibaud Laurent, Thiran Jean-Philippe, Meuli Reto, Bach Cuadra Meritxell (2014), Automated brain extraction in fetal MRI by multi-atlas fusion strategy: Study on healthy and pathological subjects, in 22nd of the International Society for Magnetic Resonance in Medicine, Milano, ItalyProceedings of the International Society for Magnetic Resonance in Medicine, Milano, Italy.
Efficient total variation algorithm for fetal brain MRI reconstruction
Tourbier Sébastien, Bresson Xavier, Hagmann Patric, Thiran Jean Philippe, Meuli Reto A., Bach Cuadra Meritxell (2014), Efficient total variation algorithm for fetal brain MRI reconstruction, in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014, Boston, USASpringer Lecture Notes in Computer Science (LNCS), Boston, USA.
In-vivo 3D Magnetic Resonance Volumetric Analysis of Fetal Cerebellum: From normal to pathology (unilateral cerebellar hypoplasia)
Gianoni Martina, Schaer Marie, Tourbier Sébastien, Vial Yvan, Cagneaux Maud, Hagmann Patric, Meuli Reto, Bach Cuadra Meritxell (2014), In-vivo 3D Magnetic Resonance Volumetric Analysis of Fetal Cerebellum: From normal to pathology (unilateral cerebellar hypoplasia), in 22nd International Society for Magnetic Resonance in Medicine, MilanProceedings of the International Society for Magnetic Resonance in Medicine, Milan, Italy.
Segmentation of fetal pericerebral spaces based on reconstructed high-resolution MRI
Cagneaux Maud, Bach Cuadra Meritxell, Tourbier Sébastien, Schaer Marie, Hannoun S., Guibaud Laurent, Sappey-Marinier D. (2014), Segmentation of fetal pericerebral spaces based on reconstructed high-resolution MRI, in 22nd of the International Society for Magnetic Resonance in Medicine, Milan, ItalyProceedings of the International Society for Magnetic Resonance in Medicine, Italy.
Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.
Ciurte Anca, Bresson Xavier, Cuisenaire Olivier, Houhou Nawal, Nedevschi Sergiu, Thiran Jean-Philippe, Bach Cuadra Meritxell (2014), Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut., in PloS one, 9(7), 100972-100972.
Multiclass total variation clustering
Bresson Xavier, Laurent Thomas, Uminsky David, Von Brecht James H. (2013), Multiclass total variation clustering, in Advances in Neural Information Processing Systems 26, USABurges, C., USA.

Collaboration

Group / person Country
Types of collaboration
Dr. Patric Hagmann, Lausanne University Hospital Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
- Research Infrastructure
Prof. Xavier Bresson, Hong Kong University Hongkong (Asia)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Department of Radiology Children's Hospital United States of America (North America)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Prof. Jean-Philippe Thiran, Ecole Polytechnique Federale de Lausanne Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Research Infrastructure
Prof. Petra Hüppi, Geneva University Hospital Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
Dr. Marie Schaer, Geneva University Hospital Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Dr. Yvan Vial, Lausanne Universtiy Hospital Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication
Prof. Laurent Guibaud, University Hospital Lyon France (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Publication

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
4th Annual Retreat of the CIBM-CHUV-MR Talk given at a conference Fetal brain MR image processing 08.09.2016 Sévrier, France Bach Cuadra Meritxell;
Brain Imaging Workshop Talk given at a conference Compress sensing in clinical MRI 17.03.2014 Lausanne, Switzerland Bresson Xavier;
Colloque de recherche, Département de Radiologie, Hôpital Femme, Mère, Enfant, Lyon Individual talk Nouvelles directions pour l'étude du développement du cerveau foetal: méthodes automatiques de reconstruction et segmentation d'IRM in utero 11.01.2013 Lyon, France Bach Cuadra Meritxell;


Knowledge transfer events

Active participation

Title Type of contribution Date Place Persons involved
Les Mystères de l'UNIL: « TKITOI ? » l’aventure intérieure Performances, exhibitions (e.g. for education institutions) 22.05.2014 Lausanne, Switzerland Bach Cuadra Meritxell; Tourbier Sébastien;


Associated projects

Number Title Start Funding scheme
150828 Development of Advanced Translational High-Field MRI 12.05.2014 R'EQUIP
170894 Towards the fetal connectome: developing diffusion MR image analysis methods for the fetal brain 01.08.2016 International short research visits
182602 Advanced super-resolution reconstruction methods for quantitative magnetic resonance imaging of the developing fetal brain 01.04.2019 Project funding (Div. I-III)

Abstract

The last decades witnessed an impressive development of image rocessing techniques devoted to the study of the human brain. Along with technical improvements and increasing sample size of the studied population, the study of the adult brain anatomy and function progressively shifted to the assessment of earliest stages of cerebral development (i.e. the postnatal brain growth). If most of neuroimaging research studies are conducted in children and adults, recent advances in clinical imaging of the fetus provide an unprecedented opportunity to image the process of the human brain growth in utero. Quantitative studies of structural fetal Magnetic Resonance Imaging (MRI) would be of major importance in addressing the fundamental neuroscience question of early brain development. Moreover, in a clinical perspective, quantitative analysis of fetal MRI would help to better characterize the timing and the nature of prenatal pathologies, like ventriculomegaly, intra-uterine growth restriction or brain alterations in fetuses with congenital heart disease. New fast multi-slice MR techniques allow high contrast imaging of brain tissues but clinical acquisitions still have many critical limitations that restrain the use of computer-based methods for large scale studies. Consequently, the research project presented here, oriented to robust and accurate image processing methods for fetal MRI reconstruction and segmentation, will have an important impact for both neuroscience and clinics. Image processing methods for fetal MRI have to face major challenges like motion artifacts due to fetal movements inside the amniotic cavity, a poor spatial resolution due to fast sequences, the partial volume effect, intensity inhomogeneities, natural local intensity variations and the rapid changing anatomy of the developing fetal brain. The stateof-the-art of reconstruction and segmentation dedicated to fetal MRI is scarce. Reconstruction methods mostly rely on registration-interpolation strategies. However, these methods lack of mathematical proof of convergence and their resultspresent excessive blurring due to scattered data interpolation techniques. State-of-the-art segmentation methods are mostly applied to young fetus and they need anatomical priors and dynamic atlases to succeed. The use of such strong prior information faces a risk of circularity: each brain will be analyzed and deformed using the template of its biological age, potentially biasing the effective developmental delay. Thus, there is a need to develop solid mathematical framework for the fetal MRI reconstruction as well as for robust and accurate segmentation methods of fetuses at second and third trimester of pregnancy.The primary aim of this project resides in developing advanced fetal MR imaging processing, providing significantly improved reconstruction and segmentation methods as compared to state-of-the-art techniques. The originality of our research project resides in the use of soft priors in both reconstruction and segmentation problems. By soft priors, we mean prior information coming from the image data itself, like sparsity constrains, local spatial priors or label priors. Our first contribution will be a general and flexible reconstruction framework for fetal MRI, thanks to the large versatility of the sparsity constraints and of the convex optimization methods. We aim at solving the inverse problem formulation with regularization constraints like Total Variation (TV) or Generalized TV regularization. We are supported by results in other computer vision domains, like denoising or Super Resolution (SR), where these sparsity priors proven sharp edges reconstructed images while being robust to noise and motion-corrupted frames. The problem we are facing in fetal MRIis though more complex and thus further research will be done here, mainly dedicated to the development of iterative schemes and fast minimization algorithms to solve the 3D fetal MRI reconstruction. As regards segmentation, we aim at performing atlas-free segmentation of fetal brain tissue in MRI to avoid the risk of circularity in further quantitativestudies. We will present a flexible non-local variational framework based on a graph representation of the image with patches were any kind of regularization priors can be included, from new sparsity constraints like non-local TV to data driven priors. The use of patches (non-local information) is particularly appropriated in fetal MRI where we aim at preserving tiny structures and well-adapted to any gestational age. The proposed novel segmentation method will represent the second major methodological contribution of this project.
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