Mathieu De Craene

Bio: Mathieu De Craene is an academic researcher from Pompeu Fabra University. The author has contributed to research in topics: Population & Medicine. The author has an hindex of 13, co-authored 27 publications receiving 567 citations.

Analysis of intracranial aneurysm wall motion and its effects on hemodynamic patterns

Abstract: Hemodynamics, and in particular Wall Shear Stress (WSS), is thought to play a critical role in the progression and rupture of intracranial aneurysms. Wall motion is related to local biomechanical properties of the aneurysm, which in turn are associated with the amount of damage undergone by the tissue. The underlying hypothesis in this work is that injured regions show differential motion with respect to normal ones, allowing a connection between local wall biomechanics and a potential mechanism of wall injury such as elevated WSS. In a previous work, a novel method was presented combining wall motion estimation using image registration techniques with Computational Fluid Dynamics (CFD) simulations in order to provide realistic intra-aneurysmal flow patterns. It was shown that, when compared to compliant vessels, rigid models tend to overestimate WSS and produce smaller areas of elevated WSS and force concentration, being the observed differences related to the magnitude of the displacements. This work aims to further study the relationships between wall motion, flow patterns and risk of rupture in aneurysms. To this end, four studies containing both 3DRA and DSA studies were analyzed, and an improved version of the method developed previously was applied to cases showing wall motion. A quantification and analysis of the displacement fields and their relationships to flow patterns are presented. This relationship may play an important role in understanding interaction mechanisms between hemodynamics, wall biomechanics, and the effect on aneurysm evolution mechanisms.

Temporal diffeomorphic free-form deformation for strain quantification in 3D-US images

Abstract: This paper presents a new diffeomorphic temporal registration algorithm and its application to motion and strain quantification from a temporal sequence of 3D images. The displacement field is computed by forward eulerian integration of a non-stationary velocity field. The originality of our approach resides in enforcing time consistency by representing the velocity field as a sum of continuous spatiotemporal BSpline kernels. The accuracy of the developed diffeomorphic technique was first compared to a simple pairwise strategy on synthetic US images with known ground truth motion and with several noise levels, being the proposed algorithm more robust to noise than the pairwise case. Our algorithm was then applied to a database of cardiac 3D+t Ultrasound (US) images of the left ventricle acquired from eight healthy volunteers and three Cardiac Resynchronization Therapy (CRT) patients. On healthy cases, the measured regional strain curves provided uniform strain patterns over all myocardial segments in accordance with clinical literature. On CRT patients, the obtained normalization of the strain pattern after CRT agreed with clinical outcome for the three cases.

Decision Forests: A Unified Framework for Classification, Regression, Density Estimation, Manifold, Learning and Semi-supervised Learning

Abstract: This review presents a unified, efficient model of random decision forests which can be applied to a number of machine learning, computer vision, and medical image analysis tasks. Our model extends existing forest-based techniques as it unifies classification, regression, density estimation, manifold learning, semi-supervised learning, and active learning under the same decision forest framework. This gives us the opportunity to write and optimize the core implementation only once, with application to many diverse tasks. The proposed model may be used both in a discriminative or generative way and may be applied to discrete or continuous, labeled or unlabeled data. The main contributions of this review are: (1) Proposing a unified, probabilistic and efficient model for a variety of learning tasks; (2) Demonstrating margin-maximizing properties of classification forests; (3) Discussing probabilistic regression forests in comparison with other nonlinear regression algorithms; (4) Introducing density forests for estimating probability density functions; (5) Proposing an efficient algorithm for sampling from a density forest; (6) Introducing manifold forests for nonlinear dimensionality reduction; (7) Proposing new algorithms for transductive learning and active learning. Finally, we discuss how alternatives such as random ferns and extremely randomized trees stem from our more general forest model. This document is directed at both students who wish to learn the basics of decision forests, as well as researchers interested in the new contributions. It presents both fundamental and novel concepts in a structured way, with many illustrative examples and real-world applications. Thorough comparisons with state-of-the-art algorithms such as support vector machines, boosting and Gaussian processes are presented and relative advantages and disadvantages discussed. The many synthetic examples and existing commercial applications demonstrate the validity of the proposed model and its flexibility.

Normal values for cardiovascular magnetic resonance in adults and children

Abstract: Morphological and functional parameters such as chamber size and function, aortic diameters and distensibility, flow and T1 and T2* relaxation time can be assessed and quantified by cardiovascular magnetic resonance (CMR). Knowledge of normal values for quantitative CMR is crucial to interpretation of results and to distinguish normal from disease. In this review, we present normal reference values for morphological and functional CMR parameters of the cardiovascular system based on the peer-reviewed literature and current CMR techniques and sequences.

Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132.

Abstract: Image registration and fusion algorithms exist in almost every software system that creates or uses images in radiotherapy. Most treatment planning systems support some form of image registration and fusion to allow the use of multimodality and time-series image data and even anatomical atlases to assist in target volume and normal tissue delineation. Treatment delivery systems perform registration and fusion between the planning images and the in-room images acquired during the treatment to assist patient positioning. Advanced applications are beginning to support daily dose assessment and enable adaptive radiotherapy using image registration and fusion to propagate contours and accumulate dose between image data taken over the course of therapy to provide up-to-date estimates of anatomical changes and delivered dose. This information aids in the detection of anatomical and functional changes that might elicit changes in the treatment plan or prescription. As the output of the image registration process is always used as the input of another process for planning or delivery, it is important to understand and communicate the uncertainty associated with the software in general and the result of a specific registration. Unfortunately, there is no standard mathematical formalism to perform this for real-world situations where noise, distortion, and complex anatomical variations can occur. Validation of the software systems performance is also complicated by the lack of documentation available from commercial systems leading to use of these systems in undesirable ‘black-box’ fashion. In view of this situation and the central role that image registration and fusion play in treatment planning and delivery, the Therapy Physics Committee of the American Association of Physicists in Medicine commissioned Task Group 132 to review current approaches and solutions for image registration (both rigid and deformable) in radiotherapy and to provide recommendations for quality assurance and quality control of these clinical processes.

Hemodynamics of Cerebral Aneurysms.

Abstract: The initiation and progression of cerebral aneurysms are degenerative processes of the arterial wall driven by a complex interaction of biological and hemodynamic factors. Endothelial cells on the artery wall respond physiologically to blood-flow patterns. In normal conditions, these responses are associated with nonpathological tissue remodeling and adaptation. The combination of abnormal blood patterns and genetics predisposition could lead to the pathological formation of aneurysms. Here, we review recent progress on the basic mechanisms of aneurysm formation and evolution, with a focus on the role of hemodynamic patterns.