Estanislao Oubel

CFD analysis incorporating the influence of wall motion: application to intracranial aneurysms

Haemodynamics, and in particular wall shear stress, is thought to play a critical role in the progression and rupture of intracranial aneurysms. A novel method is presented that combines image-based wall motion estimation obtained through non-rigid registration with computational fluid dynamics (CFD) simulations in order to provide realistic intra-aneurysmal flow patterns and understand the effects of deforming walls on the haemodynamic patterns. In contrast to previous approaches, which assume rigid walls or ad hoc elastic parameters to perform the CFD simulations, wall compliance has been included in this study through the imposition of measured wall motions. This circumvents the difficulties in estimating personalized elasticity properties. Although variations in the aneurysmal haemodynamics were observed when incorporating the wall motion, the overall characteristics of the wall shear stress distribution do not seem to change considerably. Further experiments with more cases will be required to establish the clinical significance of the observed variations.

Efficient computational fluid dynamics mesh generation by image registration

Most implementations of computational fluid dynamics (CFD) solutions require a discretisation or meshing of the solution domain. The production from a medical image of a computationally efficient mesh representing the structures of interest can be time consuming and labour-intensive, and remains a major bottleneck in the clinical application of CFD. This paper presents a method for deriving a patient-specific mesh from a medical image. The method uses volumetric registration of a pseudo-image, produced from an idealised template mesh, with the medical image. The registration algorithm used is robust and computationally efficient. The accuracy of the new algorithm is measured in terms of the distance between a registered surface and a known surface, for image data derived from casts of the lumen of two different vessels. The true surface is identified by laser profiling. The average distance between the surface points measured by the laser profiler and the surface of the mapped mesh is better than 0.2 mm. For the images analysed, the new algorithm is shown to be 2-3 times more accurate than a standard published algorithm based on maximising normalised mutual information. Computation times are approximately 18 times faster for the new algorithm than the standard algorithm. Examples of the use of the algorithm on two clinical examples are also given. The registration methodology lends itself immediately to the construction of dynamic mesh models in which vessel wall motion is obtained directly using registration.

A statistical shape model of the heart and its application to model-based segmentation

In the present paper we describe the automatic construction of a statistical shape model of the whole heart built from a training set of 100 Multi-Slice Computed Tomography (MSCT) studies of pathologic and asymptomatic patients, including 15 (temporal) cardiac phases each. With these data sets we were able to build a compact and representative shape model of both inter-subject and temporal variability. A practical limitation in building statistical shape models, and in particular point distribution models (PDM), is the manual delineation of the training set. A key advantage of the proposed method is to overcome this limitation by not requiring them. Another one is the use of MSCT images, which thanks to their excellent anatomical depiction, have allowed for a realistic heart representation, including the four chambers and connected vasculature. The generalization ability of the shape model permits its deformation to unseen anatomies with an acceptable accuracy. Moreover, its compactness allows for having a reduced set of parameters to describe the modeled population. By varying these parameters, the statistical model can generate a set of valid examples. This is especially useful for the generation of synthetic populations of cardiac shapes, that may correspond e.g. to healthy or diseased cases. Finally, an illustrative example of the use of the constructed shape model for cardiac segmentation is provided.

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

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.

Computational mouse atlases and their application to automatic assessment of craniofacial dysmorphology caused by the Crouzon mutation Fgfr2C342Y

Crouzon syndrome is characterized by premature fusion of sutures and synchondroses. Recently, the first mouse model of the syndrome was generated, having the mutation Cys342Tyr in Fgfr2c, equivalent to the most common human Crouzon/Pfeiffer syndrome mutation. In this study, a set of micro‐computed tomography (CT) scannings of the skulls of wild‐type mice and Crouzon mice were analysed with respect to the dysmorphology caused by Crouzon syndrome. A computational craniofacial atlas was built automatically from the set of wild‐type mouse micro‐CT volumes using (1) affine and (2) non‐rigid image registration. Subsequently, the atlas was deformed to match each subject from the two groups of mice. The accuracy of these registrations was measured by a comparison of manually placed landmarks from two different observers and automatically assessed landmarks. Both of the automatic approaches were within the interobserver accuracy for normal specimens, and the non‐rigid approach was within the interobserver accuracy for the Crouzon specimens. Four linear measurements, skull length, height and width and interorbital distance, were carried out automatically using the two different approaches. Both automatic approaches assessed the skull length, width and height accurately for both groups of mice. The non‐rigid approach measured the interorbital distance accurately for both groups while the affine approach failed to assess this parameter for both groups. Using the full capability of the non‐rigid approach, local displacements obtained when registering the non‐rigid wild‐type atlas to a non‐rigid Crouzon mouse atlas were determined on the surface of the wild‐type atlas. This revealed a 0.6‐mm bending in the nasal region and a 0.8‐mm shortening of the zygoma, which are similar to characteristics previously reported in humans. The most striking finding of this analysis was an angulation of approximately 0.6 mm of the cranial base, which has not been reported in humans. Comparing the two different methodologies, it is concluded that the non‐rigid approach is the best way to assess linear skull parameters automatically. Furthermore, the non‐rigid approach is essential when it comes to analysing local, non‐linear shape differences.

Complex wavelets for registration of tagged MRI sequences

Tagged magnetic resonance imaging (MRI) is currently the reference MR modality for myocardial motion and strain analysis. Mutual information (MI) based non rigid registration has proven to be an accurate method to retrieve cardiac deformation fields. However, this technique ignores high frequency information in tags. In a previous work this information was included by using feature vectors formed with wavelet coefficients and kNN graphs to estimate alphaMI. It was shown that cardiac motion estimation was feasible with these features. In this work, features were derived from complex wavelet transform (CWT), which is shift invariant and provides more high frequency subimages than conventional wavelets. Results show that lower errors are obtained with respect to the use of pixel intensity

A point-wise quantification of asymmetry using deformation fields: application to the study of the Crouzon mouse model

This paper introduces a novel approach to quantify asymmetry in each point of a surface. The measure is based on analysing displacement vectors resulting from nonrigid image registration. A symmetric atlas, generated from control subjects is registered to a given subject image. A comparison of the resulting displacement vectors on the left and right side of the symmetry plane, gives a point-wise measure of asymmetry. The asymmetry measure was applied to the study of Crouzon syndrome using Micro CT scans of genetically modified mice. Crouzon syndrome is characterised by the premature fusion of cranial sutures, which gives rise to a highly asymmetric growth. Quantification and localisation of this asymmetry is of high value with respect to surgery planning and treatment evaluation. Using the proposed method, asymmetry was calculated in each point of the surface of Crouzon mice and wild-type mice (controls). Asymmetry appeared in similar regions for the two groups but the Crouzon mice were found significantly more asymmetric. The localisation ability of the method was in good agreement with ratings from a clinical expert. Validating the quantification ability is a less trivial task due to the lack of a gold standard. Nevertheless, a comparison with a different, but less accurate measure of asymmetry revealed good correlation.

MULTIVIEW REGISTRATION OF CARDIAC TAGGING MRI IMAGES

This paper introduces a new method based on k-nearest neighbors graphs (KNNG) for bringing into alignment multiple views of the same scene acquired at two different time points. This framework is applied to cardiac motion estimation from tagging MRI sequences. Features acquired in each view are collected in a high dimensional feature space and an efficient estimator of alpha-joint entropy (alphaJE) is used for selecting the optimal alignment. In order to register 4D datasets, an analytical expression of the alphaJE estimator was derived, enabling a fast implementation of gradient based optimization. The technique was tested in a set of six sequences and the results compared with respect to manual measurements made at tag crossing points, obtaining good accuracy and low processing times compared to published state of the art methods

Multi-sequence Registration of Cine, Tagged and Delay-Enhancement MRI with Shift Correction and Steerable Pyramid-Based Detagging

In this work, we present a registration framework for cardiac cine MRI (cMRI), tagged (tMRI) and delay-enhancement MRI (deMRI), where the two main issues to find an accurate alignment between these images have been taking into account: the presence of tags in tMRI and respiration artifacts in all sequences. A steerable pyramid image decomposition has been used for detagging purposes since it is suitable to extract high-order oriented structures by directional adaptive filtering. Shift correction of cMRI is achieved by firstly maximizing the similarity between the Long Axis and Short Axis cMRI. Subsequently, these shift-corrected images are used as target images in a rigid registration procedure with their corresponding tMRI/deMRI in order to correct their shift. The proposed registration framework has been evaluated by 840 registration tests, considerably improving the alignment of the MR images (mean RMS error of 2.04mm vs. 5.44mm).

Sparse statistical deformation model for the analysis of craniofacial malformations in the Crouzon mouse

Crouzon syndrome is characterised by the premature fusion of cranial sutures. Recently the first genetic Crouzon mouse model was generated. In this study, Micro CT skull scannings of wild-type mice and Crouzon mice were investigated. Using nonrigid registration, a wild-type craniofacial mouse atlas was built. The atlas was registered to all mice providing parameters controlling the deformations for each subject. Our previous PCA-based statistical deformation model on these parameters revealed only one discriminating mode of variation. Aiming at distributing the discriminating variation over more modes we built a different model using Independent Component Analysis (ICA). Here, we focus on a third method, sparse PCA (SPCA), which aims at approximating the properties of a standard PCA while introducing sparse modes of variation. The results show that SPCA outperforms both ICA and PCA with respect to the Fisher discriminant, although many similarities are found with respect to ICA.