224 research outputs found
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
Efficient convolution-based pairwise elastic image registration on three multimodal similarity metrics
Producción CientíficaThis paper proposes a complete convolutional formulation for 2D multimodal pairwise image registration problems based on free-form deformations. We have reformulated in terms of discrete 1D convolutions the evaluation of spatial transformations, the regularization term, and their gradients for three different multimodal registration metrics, namely, normalized cross correlation, mutual information, and normalized mutual information. A sufficient condition on the metric gradient is provided for further extension to other metrics. The proposed approach has been tested, as a proof of concept, on contrast-enhanced first-pass perfusion cardiac magnetic resonance images. Execution times have been compared with the corresponding execution times of the classical tensor product formulation, both on CPU and GPU. The speed-up achieved by using convolutions instead of tensor products depends on the image size and the number of control points considered, the larger those magnitudes, the greater the execution time reduction. Furthermore, the speed-up will be more significant when gradient operations constitute the major bottleneck in the optimization process.Ministerio de Economía, Industria y Competitividad (grants TEC2017-82408-R and PID2020-115339RB-I00)ESAOTE Ltd (grant 18IQBM
Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms
Image registration is an essential technique to obtain point correspondences between anatomical structures from different images. Conventional non-rigid registration methods assume a continuous and smooth deformation field throughout the image. However, the deformation field at the interface of different organs is not necessarily continuous, since the organs may slide over or separate from each other. Therefore, imposing continuity and smoothness ubiquitously would lead to artifacts and increased errors near the discontinuity interface.
In computational mechanics, the eXtended Finite Element Method (XFEM) was introduced to handle discontinuities without using computational meshes that conform to the discontinuity geometry. Instead, the interpolation bases themselves were enriched with discontinuous functional terms. Borrowing this concept, we propose a multiresolution eXtented Free-Form Deformation (XFFD) framework that seamlessly integrates within and extends the standard Free-Form Deformation (FFD) approach. Discontinuities are incorporated by enriching the B-spline basis functions coupled with extra degrees of freedom, which are only introduced near the discontinuity interface. In contrast with most previous methods, restricted to sliding motion, no ad hoc penalties or constraints are introduced to reduce gaps and overlaps. This allows XFFD to describe more general discontinuous motions. In addition, we integrate XFFD into a rigorously formulated multiresolution framework by introducing an exact parameter upsampling method.
The proposed method has been evaluated in two publicly available datasets: 4D pulmonary CT images from the DIR-Lab dataset and 4D CT liver datasets. The XFFD achieved a Target Registration Error (TRE) of 1.17 ± 0.85 mm in the DIR-lab dataset and 1.94 ± 1.01 mm in the liver dataset, which significantly improves on the performance of the state-of-the-art methods handling discontinuities
Sliding at first order: Higher-order momentum distributions for discontinuous image registration
In this paper, we propose a new approach to deformable image registration
that captures sliding motions. The large deformation diffeomorphic metric
mapping (LDDMM) registration method faces challenges in representing sliding
motion since it per construction generates smooth warps. To address this issue,
we extend LDDMM by incorporating both zeroth- and first-order momenta with a
non-differentiable kernel. This allows to represent both discontinuous
deformation at switching boundaries and diffeomorphic deformation in
homogeneous regions. We provide a mathematical analysis of the proposed
deformation model from the viewpoint of discontinuous systems. To evaluate our
approach, we conduct experiments on both artificial images and the publicly
available DIR-Lab 4DCT dataset. Results show the effectiveness of our approach
in capturing plausible sliding motion
Analysis of contrast-enhanced medical images.
Early detection of human organ diseases is of great importance for the accurate diagnosis and institution of appropriate therapies. This can potentially prevent progression to end-stage disease by detecting precursors that evaluate organ functionality. In addition, it also assists the clinicians for therapy evaluation, tracking diseases progression, and surgery operations. Advances in functional and contrast-enhanced (CE) medical images enabled accurate noninvasive evaluation of organ functionality due to their ability to provide superior anatomical and functional information about the tissue-of-interest. The main objective of this dissertation is to develop a computer-aided diagnostic (CAD) system for analyzing complex data from CE magnetic resonance imaging (MRI). The developed CAD system has been tested in three case studies: (i) early detection of acute renal transplant rejection, (ii) evaluation of myocardial perfusion in patients with ischemic heart disease after heart attack; and (iii), early detection of prostate cancer. However, developing a noninvasive CAD system for the analysis of CE medical images is subject to multiple challenges, including, but are not limited to, image noise and inhomogeneity, nonlinear signal intensity changes of the images over the time course of data acquisition, appearances and shape changes (deformations) of the organ-of-interest during data acquisition, determination of the best features (indexes) that describe the perfusion of a contrast agent (CA) into the tissue. To address these challenges, this dissertation focuses on building new mathematical models and learning techniques that facilitate accurate analysis of CAs perfusion in living organs and include: (i) accurate mathematical models for the segmentation of the object-of-interest, which integrate object shape and appearance features in terms of pixel/voxel-wise image intensities and their spatial interactions; (ii) motion correction techniques that combine both global and local models, which exploit geometric features, rather than image intensities to avoid problems associated with nonlinear intensity variations of the CE images; (iii) fusion of multiple features using the genetic algorithm. The proposed techniques have been integrated into CAD systems that have been tested in, but not limited to, three clinical studies. First, a noninvasive CAD system is proposed for the early and accurate diagnosis of acute renal transplant rejection using dynamic contrast-enhanced MRI (DCE-MRI). Acute rejection–the immunological response of the human immune system to a foreign kidney–is the most sever cause of renal dysfunction among other diagnostic possibilities, including acute tubular necrosis and immune drug toxicity. In the U.S., approximately 17,736 renal transplants are performed annually, and given the limited number of donors, transplanted kidney salvage is an important medical concern. Thus far, biopsy remains the gold standard for the assessment of renal transplant dysfunction, but only as the last resort because of its invasive nature, high cost, and potential morbidity rates. The diagnostic results of the proposed CAD system, based on the analysis of 50 independent in-vivo cases were 96% with a 95% confidence interval. These results clearly demonstrate the promise of the proposed image-based diagnostic CAD system as a supplement to the current technologies, such as nuclear imaging and ultrasonography, to determine the type of kidney dysfunction. Second, a comprehensive CAD system is developed for the characterization of myocardial perfusion and clinical status in heart failure and novel myoregeneration therapy using cardiac first-pass MRI (FP-MRI). Heart failure is considered the most important cause of morbidity and mortality in cardiovascular disease, which affects approximately 6 million U.S. patients annually. Ischemic heart disease is considered the most common underlying cause of heart failure. Therefore, the detection of the heart failure in its earliest forms is essential to prevent its relentless progression to premature death. While current medical studies focus on detecting pathological tissue and assessing contractile function of the diseased heart, this dissertation address the key issue of the effects of the myoregeneration therapy on the associated blood nutrient supply. Quantitative and qualitative assessment in a cohort of 24 perfusion data sets demonstrated the ability of the proposed framework to reveal regional perfusion improvements with therapy, and transmural perfusion differences across the myocardial wall; thus, it can aid in follow-up on treatment for patients undergoing the myoregeneration therapy. Finally, an image-based CAD system for early detection of prostate cancer using DCE-MRI is introduced. Prostate cancer is the most frequently diagnosed malignancy among men and remains the second leading cause of cancer-related death in the USA with more than 238,000 new cases and a mortality rate of about 30,000 in 2013. Therefore, early diagnosis of prostate cancer can improve the effectiveness of treatment and increase the patient’s chance of survival. Currently, needle biopsy is the gold standard for the diagnosis of prostate cancer. However, it is an invasive procedure with high costs and potential morbidity rates. Additionally, it has a higher possibility of producing false positive diagnosis due to relatively small needle biopsy samples. Application of the proposed CAD yield promising results in a cohort of 30 patients that would, in the near future, represent a supplement of the current technologies to determine prostate cancer type. The developed techniques have been compared to the state-of-the-art methods and demonstrated higher accuracy as shown in this dissertation. The proposed models (higher-order spatial interaction models, shape models, motion correction models, and perfusion analysis models) can be used in many of today’s CAD applications for early detection of a variety of diseases and medical conditions, and are expected to notably amplify the accuracy of CAD decisions based on the automated analysis of CE images
Two-stage motion correction for super-resolution ultrasound imaging in human lower limb
The structure of microvasculature cannot be resolved using conventional ultrasound imaging due to the fundamental diffraction limit at clinical ultrasound frequencies. It is possible to overcome this resolution limitation by localizing individual microbubbles through multiple frames and forming a super-resolved image, which usually requires seconds to minutes of acquisition. Over this time interval, motion is inevitable and tissue movement is typically a combination of large and small scale tissue translation and deformation. Therefore, super-resolution imaging is prone to motion artefacts as other imaging modalities based on multiple acquisitions are. This study investigates the feasibility of a two-stage motion estimation method, which is a combination of affine and non-rigid estimation, for super-resolution ultrasound imaging. Firstly, the motion correction accuracy of the proposed method is evaluated using simulations with increasing complexity of motion. A mean absolute error of 12.2 μm was achieved in simulations for the worst case scenario. The motion correction algorithm was then applied to a clinical dataset to demonstrate its potential to enable in vivo super-resolution ultrasound imaging in the presence of patient motion. The size of the identified microvessels from the clinical super-resolution images were measured to assess the feasibility of the two-stage motion correction method, which reduced the width of the motion blurred microvessels approximately 1.5-fold
Respiratory-induced organ motion compensation for MRgHIFU
Summary: High Intensity Focused Ultrasound is an emerging non-invasive technology for the precise
thermal ablation of pathological tissue deep within the body. The fitful, respiratoryinduced
motion of abdominal organs, such as of the liver, renders targeting challenging.
The work in hand describes methods for imaging, modelling and managing respiratoryinduced
organ motion. The main objective is to enable 3D motion prediction of liver
tumours for the treatment with Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU).
To model and predict respiratory motion, the liver motion is initially observed in 3D
space. Fast acquired 2D magnetic resonance images are retrospectively reconstructed
to time-resolved volumes, thus called 4DMRI (3D + time). From these volumes, dense
deformation fields describing the motion from time-step to time-step are extracted using
an intensity-based non-rigid registration algorithm. 4DMRI sequences of 20 subjects,
providing long-term recordings of the variability in liver motion under free breathing,
serve as the basis for this study.
Based on the obtained motion data, three main types of models were investigated and
evaluated in clinically relevant scenarios. In particular, subject-specific motion models,
inter-subject population-based motion models and the combination of both are compared
in comprehensive studies. The analysis of the prediction experiments showed that
statistical models based on Principal Component Analysis are well suited to describe
the motion of a single subject as well as of a population of different and unobserved
subjects. In order to enable target prediction, the respiratory state of the respective
organ was tracked in near-real-time and a temporal prediction of its future position is
estimated. The time span provided by the prediction is used to calculate the new target
position and to readjust the treatment focus. In addition, novel methods for faster
acquisition of subject-specific 3D data based on a manifold learner are presented and
compared to the state-of-the art 4DMRI method.
The developed methods provide motion compensation techniques for the non-invasive
and radiation-free treatment of pathological tissue in moving abdominal organs for
MRgHIFU. ---------- Zusammenfassung: High Intensity Focused Ultrasound ist eine aufkommende, nicht-invasive Technologie
für die präzise thermische Zerstörung von pathologischem Gewebe im Körper. Die
unregelmässige ateminduzierte Bewegung der Unterleibsorgane, wie z.B. im Fall der
Leber, macht genaues Zielen anspruchsvoll. Die vorliegende Arbeit beschreibt Verfahren
zur Bildgebung, Modellierung und zur Regelung ateminduzierter Organbewegung.
Das Hauptziel besteht darin, 3D Zielvorhersagen für die Behandlung von Lebertumoren
mittels Magnetic Resonance guided High Intensity Focused Ultrasound
(MRgHIFU) zu ermöglichen.
Um die Atembewegung modellieren und vorhersagen zu können, wird die Bewegung
der Leber zuerst im dreidimensionalen Raum beobachtet. Schnell aufgenommene 2DMagnetresonanz-
Bilder wurden dabei rückwirkend zu Volumen mit sowohl guter zeitlicher
als auch räumlicher Auflösung, daher 4DMRI (3D + Zeit) genannt, rekonstruiert.
Aus diesen Volumen werden Deformationsfelder, welche die Bewegung von Zeitschritt
zu Zeitschritt beschreiben, mit einem intensitätsbasierten, nicht-starren Registrierungsalgorithmus
extrahiert. 4DMRI-Sequenzen von 20 Probanden, welche Langzeitaufzeichungen
von der Variabilität der Leberbewegung beinhalten, dienen als Grundlage für
diese Studie.
Basierend auf den gewonnenen Bewegungsdaten wurden drei Arten von Modellen
in klinisch relevanten Szenarien untersucht und evaluiert. Personen-spezifische Bewegungsmodelle,
populationsbasierende Bewegungsmodelle und die Kombination beider
wurden in umfassenden Studien verglichen. Die Analyse der Vorhersage-Experimente
zeigte, dass statistische Modelle basierend auf Hauptkomponentenanalyse gut geeignet
sind, um die Bewegung einer einzelnen Person sowie einer Population von unterschiedlichen
und unbeobachteten Personen zu beschreiben. Die Bewegungsvorhersage basiert
auf der Abschätzung der Organposition, welche fast in Echtzeit verfolgt wird. Die durch
die Vorhersage bereitgestellte Zeitspanne wird verwendet, um die neue Zielposition zu
berechnen und den Behandlungsfokus auszurichten. Darüber hinaus werden neue Methoden
zur schnelleren Erfassung patienten-spezifischer 3D-Daten und deren Rekonstruktion
vorgestellt und mit der gängigen 4DMRI-Methode verglichen. Die entwickelten Methoden beschreiben Techniken zur nichtinvasiven und strahlungsfreien
Behandlung von krankhaftem Gewebe in bewegten Unterleibsorganen mittels
MRgHIFU
Nonrigid Image Registration Using Physically Based Models
It is well known that biological structures such as human brains, although may contain the same global structures, differ in shape, orientation, and fine structures across individuals and at different times. Such variabilities during registration are usually represented by nonrigid transformations. This research seeks to address this issue by developing physically based models in which transformations are constructed to obey certain physical laws. In this thesis, a novel registration technique is presented based on the physical behavior of particles. Regarding the image as a particle system without mutual interaction, we simulate the registration process by a set of free particles moving toward the target positions under applied forces. The resulting partial differential equations are a nonlinear hyperbolic system whose solution describes the spatial transformation between the images to be registered. They can be numerically solved using finite difference methods. This technique extends existing physically based models by completely excluding mutual interaction and highly localizing image deformations. We demonstrate its performance on a variety of images including two-dimensional and three-dimensional, synthetic and clinical data. Deformable images are achieved with sharper edges and clearer texture at less computational cost
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