32 research outputs found

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

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    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

    3-D lung deformation and function from respiratory-gated 4-D x-ray CT images : application to radiation treatment planning.

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    Many lung diseases or injuries can cause biomechanical or material property changes that can alter lung function. While the mechanical changes associated with the change of the material properties originate at a regional level, they remain largely asymptomatic and are invisible to global measures of lung function until they have advanced significantly and have aggregated. In the realm of external beam radiation therapy of patients suffering from lung cancer, determination of patterns of pre- and post-treatment motion, and measures of regional and global lung elasticity and function are clinically relevant. In this dissertation, we demonstrate that 4-D CT derived ventilation images, including mechanical strain, provide an accurate and physiologically relevant assessment of regional pulmonary function which may be incorporated into the treatment planning process. Our contributions are as follows: (i) A new volumetric deformable image registration technique based on 3-D optical flow (MOFID) has been designed and implemented which permits the possibility of enforcing physical constraints on the numerical solutions for computing motion field from respiratory-gated 4-D CT thoracic images. The proposed optical flow framework is an accurate motion model for the thoracic CT registration problem. (ii) A large displacement landmark-base elastic registration method has been devised for thoracic CT volumetric image sets containing large deformations or changes, as encountered for example in registration of pre-treatment and post-treatment images or multi-modality registration. (iii) Based on deformation maps from MOFIO, a novel framework for regional quantification of mechanical strain as an index of lung functionality has been formulated for measurement of regional pulmonary function. (iv) In a cohort consisting of seven patients with non-small cell lung cancer, validation of physiologic accuracy of the 4-0 CT derived quantitative images including Jacobian metric of ventilation, Vjac, and principal strains, (V?1, V?2, V?3, has been performed through correlation of the derived measures with SPECT ventilation and perfusion scans. The statistical correlations with SPECT have shown that the maximum principal strain pulmonary function map derived from MOFIO, outperforms all previously established ventilation metrics from 40-CT. It is hypothesized that use of CT -derived ventilation images in the treatment planning process will help predict and prevent pulmonary toxicity due to radiation treatment. It is also hypothesized that measures of regional and global lung elasticity and function obtained during the course of treatment may be used to adapt radiation treatment. Having objective methods with which to assess pre-treatment global and regional lung function and biomechanical properties, the radiation treatment dose can potentially be escalated to improve tumor response and local control

    An image segmentation and registration approach to cardiac function analysis using MRI

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    Cardiovascular diseases (CVDs) are one of the major causes of death in the world. In recent years, significant progress has been made in the care and treatment of patients with such diseases. A crucial factor for this progress has been the development of magnetic resonance (MR) imaging which makes it possible to diagnose and assess the cardiovascular function of the patient. The ability to obtain high-resolution, cine volume images easily and safely has made it the preferred method for diagnosis of CVDs. MRI is also unique in its ability to introduce noninvasive markers directly into the tissue being imaged(MR tagging) during the image acquisition process. With the development of advanced MR imaging acquisition technologies, 3D MR imaging is more and more clinically feasible. This recent development has allowed new potentially 3D image analysis technologies to be deployed. However, quantitative analysis of cardiovascular system from the images remains a challenging topic. The work presented in this thesis describes the development of segmentation and motion analysis techniques for the study of the cardiac anatomy and function in cardiac magnetic resonance (CMR) images. The first main contribution of the thesis is the development of a fully automatic cardiac segmentation technique that integrates and combines a series of state-of-the-art techniques. The proposed segmentation technique is capable of generating an accurate 3D segmentation from multiple image sequences. The proposed segmentation technique is robust even in the presence of pathological changes, large anatomical shape variations and locally varying contrast in the images. Another main contribution of this thesis is the development of motion tracking techniques that can integrate motion information from different sources. For example, the radial motion of the myocardium can be tracked easily in untagged MR imaging since the epi- and endocardial surfaces are clearly visible. On the other hand, tagged MR imaging allows easy tracking of both longitudinal and circumferential motion. We propose a novel technique based on non-rigid image registration for the myocardial motion estimation using both untagged and 3D tagged MR images. The novel aspect of our technique is its simultaneous use of complementary information from both untagged and 3D tagged MR imaging. The similarity measure is spatially weighted to maximise the utility of information from both images. The thesis also proposes a sparse representation for free-form deformations (FFDs) using the principles of compressed sensing. The sparse free-form deformation (SFFD) model can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D CMR image sequences. Compared to the standard FFD approach, a significant increase in registration accuracy can be observed in datasets with discontinuous motion patterns. Both the segmentation and motion tracking techniques presented in this thesis have been applied to clinical studies. We focus on two important clinical applications that can be addressed by the techniques proposed in this thesis. The first clinical application aims at measuring longitudinal changes in cardiac morphology and function during the cardiac remodelling process. The second clinical application aims at selecting patients that positively respond to cardiac resynchronization therapy (CRT). The final chapter of this thesis summarises the main conclusions that can be drawn from the work presented here and also discusses possible avenues for future research

    Diffeomorphic image registration with applications to deformation modelling between multiple data sets

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    Over last years, the diffeomorphic image registration algorithms have been successfully introduced into the field of the medical image analysis. At the same time, the particular usability of these techniques, in majority derived from the solid mathematical background, has been only quantitatively explored for the limited applications such as longitudinal studies on treatment quality, or diseases progression. The thesis considers the deformable image registration algorithms, seeking out those that maintain the medical correctness of the estimated dense deformation fields in terms of the preservation of the object and its neighbourhood topology, offer the reasonable computational complexity to satisfy time restrictions coming from the potential applications, and are able to cope with low quality data typically encountered in Adaptive Radiotherapy (ART). The research has led to the main emphasis being laid on the diffeomorphic image registration to achieve one-to-one mapping between images. This involves introduction of the log-domain parameterisation of the deformation field by its approximation via a stationary velocity field. A quantitative and qualitative examination of existing and newly proposed algorithms for pairwise deformable image registration presented in this thesis, shows that the log-Euclidean parameterisation can be successfully utilised in the biomedical applications. Although algorithms utilising the log-domain parameterisation have theoretical justification for maintaining diffeomorphism, in general, the deformation fields produced by them have similar properties as these estimated by classical methods. Having this in mind, the best compromise in terms of the quality of the deformation fields has been found for the consistent image registration framework. The experimental results suggest also that the image registration with the symmetrical warping of the input images outperforms the classical approaches, and simultaneously can be easily introduced to most known algorithms. Furthermore, the log-domain implicit group-wise image registration is proposed. By linking the various sets of images related to the different subjects, the proposed image registration approach establishes a common subject space and between-subject correspondences therein. Although the correspondences between groups of images can be found by performing the classic image registration, the reference image selection (not required in the proposed implementation), may lead to a biased mean image being estimated and the corresponding common subject space not adequate to represent the general properties of the data sets. The approaches to diffeomorphic image registration have been also utilised as the principal elements for estimating the movements of the organs in the pelvic area based on the dense deformation field prediction system driven by the partial information coming from the specific type of the measurements parameterised using the implicit surface representation, and recognising facial expressions where the stationary velocity fields are used as the facial expression descriptors. Both applications have been extensively evaluated based on the real representative data sets of three-dimensional volumes and two-dimensional images, and the obtained results indicate the practical usability of the proposed techniques

    Development of acquisition system and algorithms for registration towards modeling displacement and deformation of the contour on the digital image

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    Centralna tema ovog rada je primena sistema za akviziciju slike u cilju procene i modelovanja deformacija i pomeranja objekata koji su snimljeni. Glavna metoda koja je pri tom korišćena je metoda registracije slika. Sam postupak registracije podrazumeva skup algoritama i metoda kojim se vrši pronalaženje transformacije koja preslikava prostor jedne slike u prostor druge. Ukoliko se radi o slikama istog objekta u različitim položajima ili konfiguracijama moguće je odrediti pomeranja i deformacije željene tačke poznavanjem ove transformacije. U radu su opisani već postojeći algoritmi, sa svojim najznačajnijim svojstvima. Na bazi ovih osobina razvijen je metod registracije baziran na rešavanju Laplasove jednačine za elektrostatičko polje. Ovakav pristup je moguć zahvaljujući činjenici da gradijent deformacija odgovara linijama elektrostatičkog polja, koje je dobijeno rešavanjem Laplasove jednačine i zadovoljava sva bitna svojstva koja treba da ima registraciona transformacija. Ove osobine se odnose na glatkost polja deformacije, postojanje inverzne funkcije i zabranu ukrštanja linija polja. Sam postupak rešavanja navedene jednačine i određivanje tražene transformaicje sproveden je primenom metode konačnih elemenata pri čemu je korišćena formulacija minimuma energija sistema. Jedna od inspiracija za rad na metodama registracije slike bio je i problem procene mehaničkih karakteristika tkiva aorte sa aneurizmom. U radu je opisana realizacija i način rada sistema koji je iskorišćen za karakterizaciju mehaničkih svojstava aorte, koji kao izlazne podatke daje informaciju o pomeranjima skupa tačaka tkiva kao i o vrednostima pritiska fluida koji izaziva ta pomeranja. Deformacije su procenjene primenom metoda segmentacije slike i izdvajanja ivica nakon čega je primenjen metod registracije slike kojom je određena deformacija tačaka tkiva u određenim vremenskim trenucima. Na osnovu ovih vrednosti primenom genetskog algoritma određena je vrednost Jangovog modula tkiva pri čemu je korišćen mehanički model deformacije tkiva. Analiza hoda upotrebom slika hoda je takođe jedan od izazova kada je u pitanju neinvazivna dijagnostika i praćenje stanja dijagnostifiko- vanih kao i zdravih subjekata. U ovom radu je prikazan postupak određivanja mehaničkog naprezanja hrskavice primenom slika snimljenih kamerom i vrednostima sile normalne reakcije podloge koja nastaje tokom hoda. Za procenu deformacija hrskavice korišćeni su algoritmi registracije slike između slika dobijenih sa kamere i slika dobijenih kompjuterizovanom tomografijom. Postupkom optimizacije procenjeni su i mehanički parametri hrskavice (Jangov modul i Poasonov koeficijent).The main aim of this thesis is the application of image acquisition system for the purpose of assessing and modeling the deformation and displacement of the objects acquired in digital images. The technique used in the study is method of image registration. The procedure of the registration includes a set of algorithms and methods which performs the assessment of transformation that maps the space of one image to another one. If there are images of the same object in different positions or configurations it is possible to determine the displacement and deformation of the desired point of understanding this transformation. The thesis describes the existing algorithms, along with their most important properties. The novel algorithms for image registration is developed based of solving the Laplace equation for electrostatic field. This approach is possible due to the fact that the transformation which corresponds to the deformation gradient field lines of the electrostatic field, which is obtained by solving the Laplace equation satisfies all essential features that should have the registration transformation. These properties are related to the smoothness of the deformation field, the existence of an inverse function of the prohibition of crossing the line field. The procedure for solving the above equation and determining the required transformation was conducted using finite element method with use of a formulation of minimum energy of the system. The motivation for this thesis was consideration problem of evaluation mechanical properties of tissues affected aortic aneurysm. The paper describes the implementation and operation of the system that was used to characterize the mechanical properties of the aorta, which as output data provides information about a set of deformation points on the tissue surface as well as the values of applied fluid pressure. Strains at the certain moment of time were estimated using the image segmentation method and edges extraction, and finally image registration is applied. Using strain values in the mechanical model of tissue, and genetic algorithm as optimization technique, the Young's modulus is assessment. Gait analysis based on the images data is also one of the challenges in non-invasive diagnosis and monitoring of both diagnosed patients and healthy subjects.. This thesis presents a method for determining the mechanical stress of the cartilage using the camera image, and the values of the normal ground reaction force, which is generated during the walk, for assessment of cartilage deformation algorithms were used image registration of images obtained from the camera and the images obtained by computed tomography. Mechanical parameters of cartilage (Young's modulus and Poisson's ratio) are evaluated in the optimization process

    A physically based trunk soft tissue modeling for scoliosis surgery planning systems

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    One of the major concerns of scoliotic patients undergoing spinal correction surgery is the trunk's external appearance after the surgery. This paper presents a novel incremental approach for simulating postoperative trunk shape in scoliosis surgery. Preoperative and postoperative trunk shapes data were obtained using three-dimensional medical imaging techniques for seven patients with adolescent idiopathic scoliosis. Results of qualitative and quantitative evaluations, based on the comparison of the simulated and actual postoperative trunk surfaces, showed an adequate accuracy of the method. Our approach provides a candidate simulation tool to be used in a clinical environment for the surgery planning process.IRSC / CIH

    A Survey on Deep Learning in Medical Image Registration: New Technologies, Uncertainty, Evaluation Metrics, and Beyond

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    Over the past decade, deep learning technologies have greatly advanced the field of medical image registration. The initial developments, such as ResNet-based and U-Net-based networks, laid the groundwork for deep learning-driven image registration. Subsequent progress has been made in various aspects of deep learning-based registration, including similarity measures, deformation regularizations, and uncertainty estimation. These advancements have not only enriched the field of deformable image registration but have also facilitated its application in a wide range of tasks, including atlas construction, multi-atlas segmentation, motion estimation, and 2D-3D registration. In this paper, we present a comprehensive overview of the most recent advancements in deep learning-based image registration. We begin with a concise introduction to the core concepts of deep learning-based image registration. Then, we delve into innovative network architectures, loss functions specific to registration, and methods for estimating registration uncertainty. Additionally, this paper explores appropriate evaluation metrics for assessing the performance of deep learning models in registration tasks. Finally, we highlight the practical applications of these novel techniques in medical imaging and discuss the future prospects of deep learning-based image registration

    Variational methods for shape and image registrations.

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    Estimating and analysis of deformation, either rigid or non-rigid, is an active area of research in various medical imaging and computer vision applications. Its importance stems from the inherent inter- and intra-variability in biological and biomedical object shapes and from the dynamic nature of the scenes usually dealt with in computer vision research. For instance, quantifying the growth of a tumor, recognizing a person\u27s face, tracking a facial expression, or retrieving an object inside a data base require the estimation of some sort of motion or deformation undergone by the object of interest. To solve these problems, and other similar problems, registration comes into play. This is the process of bringing into correspondences two or more data sets. Depending on the application at hand, these data sets can be for instance gray scale/color images or objects\u27 outlines. In the latter case, one talks about shape registration while in the former case, one talks about image/volume registration. In some situations, the combinations of different types of data can be used complementarily to establish point correspondences. One of most important image analysis tools that greatly benefits from the process of registration, and which will be addressed in this dissertation, is the image segmentation. This process consists of localizing objects in images. Several challenges are encountered in image segmentation, including noise, gray scale inhomogeneities, and occlusions. To cope with such issues, the shape information is often incorporated as a statistical model into the segmentation process. Building such statistical models requires a good and accurate shape alignment approach. In addition, segmenting anatomical structures can be accurately solved through the registration of the input data set with a predefined anatomical atlas. Variational approaches for shape/image registration and segmentation have received huge interest in the past few years. Unlike traditional discrete approaches, the variational methods are based on continuous modelling of the input data through the use of Partial Differential Equations (PDE). This brings into benefit the extensive literature on theory and numerical methods proposed to solve PDEs. This dissertation addresses the registration problem from a variational point of view, with more focus on shape registration. First, a novel variational framework for global-to-local shape registration is proposed. The input shapes are implicitly represented through their signed distance maps. A new Sumof- Squared-Differences (SSD) criterion which measures the disparity between the implicit representations of the input shapes, is introduced to recover the global alignment parameters. This new criteria has the advantages over some existing ones in accurately handling scale variations. In addition, the proposed alignment model is less expensive computationally. Complementary to the global registration field, the local deformation field is explicitly established between the two globally aligned shapes, by minimizing a new energy functional. This functional incrementally and simultaneously updates the displacement field while keeping the corresponding implicit representation of the globally warped source shape as close to a signed distance function as possible. This is done under some regularization constraints that enforce the smoothness of the recovered deformations. The overall process leads to a set of coupled set of equations that are simultaneously solved through a gradient descent scheme. Several applications, where the developed tools play a major role, are addressed throughout this dissertation. For instance, some insight is given as to how one can solve the challenging problem of three dimensional face recognition in the presence of facial expressions. Statistical modelling of shapes will be presented as a way of benefiting from the proposed shape registration framework. Second, this dissertation will visit th
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