Temporal Diffeomorphic Free-Form Deformation for Strain Quantification in 3D-US Images

International audienceThis 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 B-Spline 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 height 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

Automatic whole heart segmentation based on image registration

Whole heart segmentation can provide important morphological information of the heart, potentially enabling the development of new clinical applications and the planning and guidance of cardiac interventional procedures. This information can be extracted from medical images, such as these of magnetic resonance imaging (MRI), which is becoming a routine modality for the determination of cardiac morphology. Since manual delineation is labour intensive and subject to observer variation, it is highly desirable to develop an automatic method. However, automating the process is complicated by the large shape variation of the heart and limited quality of the data. The aim of this work is to develop an automatic and robust segmentation framework from cardiac MRI while overcoming these difficulties. The main challenge of this segmentation is initialisation of the substructures and inclusion of shape constraints. We propose the locally affine registration method (LARM) and the freeform deformations with adaptive control point status to tackle the challenge. They are applied to the atlas propagation based segmentation framework, where the multi-stage scheme is used to hierarchically increase the degree of freedom. In this segmentation framework, it is also needed to compute the inverse transformation for the LARM registration. Therefore, we propose a generic method, using Dynamic Resampling And distance Weighted interpolation (DRAW), for inverting dense displacements. The segmentation framework is validated on a clinical dataset which includes nine pathologies. To further improve the nonrigid registration against local intensity distortions in the images, we propose a generalised spatial information encoding scheme and the spatial information encoded mutual information (SIEMI) registration. SIEMI registration is applied to the segmentation framework to improve the accuracy. Furthermore, to demonstrate the general applicability of SIEMI registration, we apply it to the registration of cardiac MRI, brain MRI, and the contrast enhanced MRI of the liver. SIEMI registration is shown to perform well and achieve significantly better accuracy compared to the registration using normalised mutual information

Multi-modality cardiac image computing: a survey

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future

Non-rigid medical image registration with extended free form deformations: modelling general tissue transitions

Image registration seeks pointwise correspondences between the same or analogous objects in different images. Conventional registration methods generally impose continuity and smoothness throughout the image. However, there are cases in which the deformations may involve discontinuities. In general, the discontinuities can be of different types, depending on the physical properties of the tissue transitions involved and boundary conditions. For instance, in the respiratory motion the lungs slide along the thoracic cage following the tangential direction of their interface. In the normal direction, however, the lungs and the thoracic cage are constrained to be always in contact but they have different material properties producing different compression or expansion rates. In the literature, there is no generic method, which handles different types of discontinuities and considers their directional dependence. The aim of this thesis is to develop a general registration framework that is able to correctly model different types of tissue transitions with a general formalism. This has led to the development of the eXtended Free Form Deformation (XFFD) registration method. XFFD borrows the concept of the interpolation method from the eXtended Finite Element method (XFEM) to incorporate discontinuities by enriching B-spline basis functions, coupled with extra degrees of freedom. XFFD can handle different types of discontinuities and encodes their directional-dependence without any additional constraints. XFFD has been evaluated on digital phantoms, publicly available 3D liver and lung CT images. The experiments show that XFFD improves on previous methods and that it is important to employ the correct model that corresponds to the discontinuity type involved at the tissue transition. The effect of using incorrect models is more evident in the strain, which measures mechanical properties of the tissues

Cardiac motion and deformation estimation in tagged magnetic resonance imaging

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Electrónica Médica)Cardiovascular diseases are the main cause of death in Europe, with an estimate of 4.3 million deaths each year. The assessment of the regional wall deformation is a relevant clinical indicator, and can be used to detect several cardiac lesions. Nowadays, this study can be performed using several image modalities. In the current thesis, we focus on tagged Magnetic Resonance imaging (t-MRI) technique. Such technique allows acquiring images with tags on the myocardium, which deform with the muscle. The present thesis intends to assess the left ventricle (LV) deformation using radial and circumferential strain. To compute such strain values, both endo- and epicardial contours of the LV are required. As such, a new framework to automatically assess the LV function is proposed. This framework presents: (i) an automatic segmentation technique, based on a tag suppression strategy followed by an active contour segmentation method, and (ii) a tracking approach to extract myocardial deformation, based on a non-rigid registration method. The automatic segmentation uses the B-spline Explicit Active Surface framework, which was previously applied in ultra-sound and cine-MRI images. In both cases, a real-time and accurate contour was achieved. Regarding the registration step, starting from a state-of-art approach, termed sequential 2D, we suggest a new method (termed sequential 2D+t), where the temporal information is included on the model. The tracking methods were first tested on synthetic data to study the registration parameters influence. Furthermore, the proposed and original methods were applied on porcine data with myocardial ischemia. Both methods were able to detect dysfunctional regions. A comparison between the strain curve in the sequential 2D and sequential 2D+t strategies was also shown. As conclusion, a smoothing effect in the strain curve was detected in the sequential 2D+t strategy. The validation of the segmentation approach uses a human dataset. A comparison between the manual contour and the proposed segmentation method results was performed. The results, suggest that proposed method has an acceptable performance, removing the tedious task related with manual segmentation and the intra-observer variability. Finally, a comparison between the proposed framework and the currently available commercial software was performed. The commercial software results were obtained from core-lab analysis. An acceptable result (r = 0.601) was achieved when comparing the strain peak values. Importantly, the proposed framework appears to present a more acceptable result.As doenças cardiovasculares são a principal causa de morte na Europa, com aproximadamente 4.7 milhões de mortes por ano. A avaliação da deformação do miocárdio a um nível local é um importante indicador clínico e pode ser usado para a deteção de lesões cardíacas. Este estudo é normalmente realizado usando várias modalidades de imagem médica. Nesta tese, a Resonância Magnética (RM) marcada foi a técnica selecionada. Estas imagens têm marcadores no músculo cardíaco, os quais se deformam com o miocárdio e podem ser usados para o estudo da deformação cardíaca. Nesta tese, pretende-se estudar a deformação radial e circunferencial do ventrículo esquerdo (VE). Assim, um contorno do endo- e epicárdio no VE é essencial. Desta forma, uma ferramenta para o estudo da deformação do VE foi desenvolvida. Esta possui: (i) um método de segmentação automático, usando uma estratégia de supressão dos marcadores, seguido de uma segmentação c um contorno ativo, e (ii) um método de tracking para determinação da deformação cardíaca, baseado em registo não rígido. A segmentação automática utiliza a ferramenta B-spline Explicit Active Surface, que foi previamente aplicada em imagens de ultrassons e cine-RM. Em ambos os casos, uma segmentação em tempo real e com elevada exatidão foi alcançada. Vários esquemas de registo foram apresentados. Neste ponto, começando com uma técnica do estado da arte (designada de sequencial 2D), uma nova metodologia foi proposta (sequencial 2D+t), onde a informação temporal é incorporada no modelo. De forma a analisar a influência dos parâmetros do registo, estes foram estudados num dataset sintético. De seguida, os diferentes esquemas de registo foram testados num dataset suíno com isquemia. Ambos os métodos foram capazes de detetar as regiões disfuncionais. De igual forma, utilizando as curvas de deformação obtidas para cada um dos métodos propostos, foi possível observar uma suavização na direção temporal para o método sequencial 2D+t. Relativamente à segmentação, esta foi validada com um dataset humano. Um contorno manual foi comparado com o obtido pelo método proposto. Os resultados sugerem que a nova estratégia é aceitável, sendo mais rápida do que a realização de um contorno manual e eliminando a variabilidade entre observadores. Por fim, realizou-se uma comparação entre a ferramenta proposta e um software comercial (com análise de core-lab). A comparação entre os valores de pico da deformação exibe uma correlação plausível (r=0.601). Contudo, é importante notar, que a nova ferramenta tende a apresentar um resultado mais aceitável

Fast 4D Ultrasound Registration for Image Guided Liver Interventions

Liver problems are a serious health issue. The common liver problems are hepatitis, fatty liver, liver cancer and liver damage caused by alcohol abuse. Continuous, long term disease may cause a condition of the liver known as the Liver Cirrhosis. Liver cirrhosis makes the liver scarred and hardened up causing portal hypertension. In such a situation the collateral vessels try to bypass the liver as blood cannot freely flow through the liver; causing internal bleeding. One of the treatments of portal hypertension is Transjugular intrahepatic portosystemic shunt (TIPS). In a TIPS procedure a tract in the liver is created that shortcuts two veins in the liver, reducing the portal hypertension. Radiofrequency ablation (RFA) is use for the treatment of liver cancer. In RFA, a needle electrode is placed through the skin into the liver tumor. High-frequency electrical currents are passed through the electrode, creating heat that destroys the cancer cells, without damaging the surrounding liver tissues. TIPS and RFA are minimally invasive procedures, where small incisions are made to perform the surgery and are alternative to open surgery. A minimally invasive alternative has large potential in reducing complication rates, minimizing surgical trauma and reducing hospital stay. However, in these procedures, due to lack of direct eyesight, three-dimensional imaging information about the anatomy and instruments during the intervention is required. The most difficult part of these procedures is the interpretation and selection of oblique views for needle/instrument insertion and target visualization. In our work we develop and evaluate techniques that enable the effective use of 3D ultrasound for image guided interventions. Ultrasound is low cost, mobile and unlike CT and X-rays does not use any harmful radiation in the imaging process. During these procedures, breathing shifts the region of interest and makes it difficult to constantly focus on a region of interest. We provide an approach to correct for the motion due to breathing. Additionally, we propose a method for image fusion of interventional ultrasound and preoperative imaging modalities such as CT for cases where the lesions are visible in CT but not visible in ultrasound. Incorporating CT data during intervention additionally adds greater definition and precision to the ultrasound based navigation system. Concluding, in this thesis, we presented methods and evaluated their accuracies that demonstrate the use of real-time 3D US and its fusion with CT in potentially improving image guidance in minimally invasive US guided liver interventions

ADVANCED MOTION MODELS FOR RIGID AND DEFORMABLE REGISTRATION IN IMAGE-GUIDED INTERVENTIONS

Image-guided surgery (IGS) has been a major area of interest in recent decades that continues to transform surgical interventions and enable safer, less invasive procedures. In the preoperative contexts, diagnostic imaging, including computed tomography (CT) and magnetic resonance (MR) imaging, offers a basis for surgical planning (e.g., definition of target, adjacent anatomy, and the surgical path or trajectory to the target). At the intraoperative stage, such preoperative images and the associated planning information are registered to intraoperative coordinates via a navigation system to enable visualization of (tracked) instrumentation relative to preoperative images. A major limitation to such an approach is that motions during surgery, either rigid motions of bones manipulated during orthopaedic surgery or brain soft-tissue deformation in neurosurgery, are not captured, diminishing the accuracy of navigation systems. This dissertation seeks to use intraoperative images (e.g., x-ray fluoroscopy and cone-beam CT) to provide more up-to-date anatomical context that properly reflects the state of the patient during interventions to improve the performance of IGS. Advanced motion models for inter-modality image registration are developed to improve the accuracy of both preoperative planning and intraoperative guidance for applications in orthopaedic pelvic trauma surgery and minimally invasive intracranial neurosurgery. Image registration algorithms are developed with increasing complexity of motion that can be accommodated (single-body rigid, multi-body rigid, and deformable) and increasing complexity of registration models (statistical models, physics-based models, and deep learning-based models). For orthopaedic pelvic trauma surgery, the dissertation includes work encompassing: (i) a series of statistical models to model shape and pose variations of one or more pelvic bones and an atlas of trajectory annotations; (ii) frameworks for automatic segmentation via registration of the statistical models to preoperative CT and planning of fixation trajectories and dislocation / fracture reduction; and (iii) 3D-2D guidance using intraoperative fluoroscopy. For intracranial neurosurgery, the dissertation includes three inter-modality deformable registrations using physic-based Demons and deep learning models for CT-guided and CBCT-guided procedures

Multi-Modal Similarity Learning for 3D Deformable Registration of Medical Images

Alors que la perspective de la fusion d images médicales capturées par des systèmes d imageries de type différent est largement contemplée, la mise en pratique est toujours victime d un obstacle théorique : la définition d une mesure de similarité entre les images. Des efforts dans le domaine ont rencontrés un certain succès pour certains types d images, cependant la définition d un critère de similarité entre les images quelle que soit leur origine et un des plus gros défis en recalage d images déformables. Dans cette thèse, nous avons décidé de développer une approche générique pour la comparaison de deux types de modalités donnés. Les récentes avancées en apprentissage statistique (Machine Learning) nous ont permis de développer des solutions innovantes pour la résolution de ce problème complexe. Pour appréhender le problème de la comparaison de données incommensurables, nous avons choisi de le regarder comme un problème de plongement de données : chacun des jeux de données est plongé dans un espace commun dans lequel les comparaisons sont possibles. A ces fins, nous avons exploré la projection d un espace de données image sur l espace de données lié à la seconde image et aussi la projection des deux espaces de données dans un troisième espace commun dans lequel les calculs sont conduits. Ceci a été entrepris grâce à l étude des correspondances entre les images dans une base de données images pré-alignées. Dans la poursuite de ces buts, de nouvelles méthodes ont été développées que ce soit pour la régression d images ou pour l apprentissage de métrique multimodale. Les similarités apprises résultantes sont alors incorporées dans une méthode plus globale de recalage basée sur l optimisation discrète qui diminue le besoin d un critère différentiable pour la recherche de solution. Enfin nous explorons une méthode qui permet d éviter le besoin d une base de données pré-alignées en demandant seulement des données annotées (segmentations) par un spécialiste. De nombreuses expériences sont conduites sur deux bases de données complexes (Images d IRM pré-alignées et Images TEP/Scanner) dans le but de justifier les directions prises par nos approches.Even though the prospect of fusing images issued by different medical imagery systems is highly contemplated, the practical instantiation of it is subject to a theoretical hurdle: the definition of a similarity between images. Efforts in this field have proved successful for select pairs of images; however defining a suitable similarity between images regardless of their origin is one of the biggest challenges in deformable registration. In this thesis, we chose to develop generic approaches that allow the comparison of any two given modality. The recent advances in Machine Learning permitted us to provide innovative solutions to this very challenging problem. To tackle the problem of comparing incommensurable data we chose to view it as a data embedding problem where one embeds all the data in a common space in which comparison is possible. To this end, we explored the projection of one image space onto the image space of the other as well as the projection of both image spaces onto a common image space in which the comparison calculations are conducted. This was done by the study of the correspondences between image features in a pre-aligned dataset. In the pursuit of these goals, new methods for image regression as well as multi-modal metric learning methods were developed. The resulting learned similarities are then incorporated into a discrete optimization framework that mitigates the need for a differentiable criterion. Lastly we investigate on a new method that discards the constraint of a database of images that are pre-aligned, only requiring data annotated (segmented) by a physician. Experiments are conducted on two challenging medical images data-sets (Pre-Aligned MRI images and PET/CT images) to justify the benefits of our approach.CHATENAY MALABRY-Ecole centrale (920192301) / SudocSudocFranceF