Sufficient Condition for Local Invertibility of Spatio-Temporal 4D B-Spline Deformations

Recent advances in medical imaging technologies have made 4D image sequences available in clinical routine. As a consequence, image registration techniques are evolving from alignment of pairs of static volumetric images to spatio-temporal registration of dynamic (4D) images. Since the elastic image registration problem is ill-posed, additional prior information or constraints are usually required to regularize the problem. This work proposes to enforce local invertibility (diffeomorphism) of 4D deformations. A novel sufficient condition for local invertibility over continuous space and time is proposed and a practical regularization prior is designed from the theory. The method has been applied to an image registration (motion tracking) of a dynamic 4D CT image sequence. Results show that using proposed regularizer leads to deformations that are more plausible for respiratory motion than the standard approach without additional temporal regularization.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85901/1/Fessler246.pd

Estimation of smooth growth trajectories with controlled acceleration from time series shape data

pre-printLongitudinal shape analysis often relies on the estimation of a realistic continuous growth scenario from data sparsely distributed in time. In this paper, we propose a new type of growth model para-meterized by acceleration, whereas standard methods typically control the velocity. This mimics the behavior of biological tissue as a mechanical system driven by external forces. The growth trajectories are estimated as smooth flows of deformations, which are twice differentiable. This differs from piecewise geodesic regression, for which the velocity may be discontinuous. We evaluate our approach on a set of anatomical structures of the same subject, scanned 16 times between 4 and 8 years of age. We show our acceleration based method estimates smooth growth, demonstrating improved regularity compared to piecewise geodesic regression. Leave-several-out experiments show that our method is robust to missing observations, as well as being less sensitive to noise, and is therefore more likely to capture the underlying biological growth

3D of brain shape and volume after cranial vault remodeling surgery for craniosynostosis correction in infants

pre-printThe skull of young children is made up of bony plates that enable growth. Craniosynostosis is a birth defect that causes one or more sutures on an infant's skull to close prematurely. Corrective surgery focuses on cranial and orbital rim shaping to return the skull to a more normal shape. Functional problems caused by craniosynostosis such as speech and motor delay can improve after surgical correction, but a post-surgical analysis of brain development in comparison with age-matched healthy controls is necessary to assess surgical outcome. Full brain segmentations obtained from pre- and post-operative computed tomography (CT) scans of 8 patients with single suture sagittal (n=5) and metopic (n=3), non-syndromic craniosynostosis from 41 to 452 days-of-age were included in this study. Age-matched controls obtained via 4D acceleration-based regression of a cohort of 402 full brain segmentations from healthy controls magnetic resonance images (MRI) were also used for comparison (ages 38 to 825 days). 3D point-based models of patient and control cohorts were obtained using SPHARM-PDM shape analysis tool. From a full dataset of regressed shapes, 240 healthy regressed shapes between 30 and 588 days-of-age (time step = 2.34 days) were selected. Volumes and shape metrics were obtained for craniosynostosis and healthy age-matched subjects. Volumes and shape metrics in single suture craniosynostosis patients were larger than age-matched controls for pre- and post-surgery. The use of 3D shape and volumetric measurements show that brain growth is not normal in patients with single suture craniosynostosis

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

3D of brain shape and volume after cranial vault remodeling surgery for craniosynostosis correction in infants

ABSTRACT The skull of young children is made up of bony plates that enable growth. Craniosynostosis is a birth defect that causes one or more sutures on an infant's skull to close prematurely. Corrective surgery focuses on cranial and orbital rim shaping to return the skull to a more normal shape. Functional problems caused by craniosynostosis such as speech and motor delay can improve after surgical correction, but a post-surgical analysis of brain development in comparison with age-matched healthy controls is necessary to assess surgical outcome. Full brain segmentations obtained from pre-and post-operative computed tomography (CT) scans of 8 patients with single suture sagittal (n=5) and metopic (n=3), nonsyndromic craniosynostosis from 41 to 452 days-of-age were included in this study. Age-matched controls obtained via 4D acceleration-based regression of a cohort of 402 full brain segmentations from healthy controls magnetic resonance images (MRI) were also used for comparison (ages 38 to 825 days). 3D point-based models of patient and control cohorts were obtained using SPHARM-PDM shape analysis tool. From a full dataset of regressed shapes, 240 healthy regressed shapes between 30 and 588 days-of-age (time step = 2.34 days) were selected. Volumes and shape metrics were obtained for craniosynostosis and healthy age-matched subjects. Volumes and shape metrics in single suture craniosynostosis patients were larger than age-matched controls for pre-and post-surgery. The use of 3D shape and volumetric measurements show that brain growth is not normal in patients with single suture craniosynostosis

Efficient dense non-rigid registration using the free-form deformation framework

Medical image registration consists of finding spatial correspondences between two images or more. It is a powerful tool which is commonly used in various medical image processing tasks. Even though medical image registration has been an active topic of research for the last two decades, significant challenges in the field remain to be solved. This thesis addresses some of these challenges through extensions to the Free-Form Deformation (FFD) registration framework, which is one of the most widely used and well-established non-rigid registration algorithm. Medical image registration is a computationally expensive task because of the high degrees of freedom of the non-rigid transformations. In this work, the FFD algorithm has been re-factored to enable fast processing, while maintaining the accuracy of the results. In addition, parallel computing paradigms have been employed to provide near real-time image registration capabilities. Further modifications have been performed to improve the registration robustness to artifacts such as tissues non-uniformity. The plausibility of the generated deformation field has been improved through the use of bio-mechanical models based regularization. Additionally, diffeomorphic extensions to the algorithm were also developed. The work presented in this thesis has been extensively validated using brain magnetic resonance imaging of patients diagnosed with dementia or patients undergoing brain resection. It has also been applied to lung X-ray computed tomography and imaging of small animals. Alongside with this thesis an open-source package, NiftyReg, has been developed to release the presented work to the medical imaging community

Proceedings of the Third International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Modelling Biological Shape Variability

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the successful rst edition of this workshop in 20061 and second edition in New-York in 20082, the third edition was held in Toronto on September 22 20113. Contributions were solicited in Riemannian and group theoretical methods, geometric measurements of the anatomy, advanced statistics on deformations and shapes, metrics for computational anatomy, statistics of surfaces, modeling of growth and longitudinal shape changes. 22 submissions were reviewed by three members of the program committee. To guaranty a high level program, 11 papers only were selected for oral presentation in 4 sessions. Two of these sessions regroups classical themes of the workshop: statistics on manifolds and diff eomorphisms for surface or longitudinal registration. One session gathers papers exploring new mathematical structures beyond Riemannian geometry while the last oral session deals with the emerging theme of statistics on graphs and trees. Finally, a poster session of 5 papers addresses more application oriented works on computational anatomy