Elastic brain image registration using mutual information

Image Registration is the determination of a geometrical transformation that aligns points in one image of an object with corresponding points in another image. The source image is geometrically transformed to match the target image. The geometric transformation can be rigid or non-rigid. Rigid transformations preserve straight lines and angles between straight lines. The basic rigid transformations are rotation, scaling and translation. In this thesis non-rigid registration using B-splines is the method being used to take into account the elastic change in the brain structure. The B-spline equation is a type of curved transformation that does not preserve the straightness of lines, as is the case with rigid transformation. A similarity measure is based on similar pixel values in the image pairs. It is used as a cost function to measure the similarity between the source and target image. Mutual information is a similarity measure based on the probability density function. Optimization of both rigid and non-rigid registration techniques is performed to obtain the registration parameters that define the best geometrical transformation. The parameters are optimized based on the mutual information. Neurosurgery is an application of image registration and requires accurate surgical planning and guidance because of complex and delicate structures in the brain. Over the course of the surgery, the brain changes its shape in reaction to mechanical and physiological changes associated with the surgery such as loss of cerebrospinal fluid and gravity forces

2d-3d rigid registration of x-ray fluoroscopy and ct images using mutual information and sparsely sampled histogram estimators

The registration of pre-operative volumetric datasets to intra-operative two-dimensional images provides an improved way of verifying patient position and medical instrument location. In applications from orthopedics to neurosurgery, it has a great value in maintaining up-to-date information about changes due to intervention. We propose a mutual information-based registration algorithm which establishes the proper alignment via a stochastic gradient ascent strategy. Our main contribution lies in estimating probability density measures of image intensities with a sparse histogramming method which could lead to potential speedup over existing registration procedures and deriving the gradient estimates required by the maximization procedure. Experimental results are presented on fluoroscopy and CT datasets of a real skull, and on a CT-derived dataset of a real skull, a plastic skull and a plastic lumbar spine segment

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

3D Shape Reconstruction of Knee Bones from Low Radiation X-ray Images Using Deep Learning

Understanding the bone kinematics of the human knee during dynamic motions is necessary to evaluate the pathological conditions, design knee prosthesis, orthosis and surgical treatments such as knee arthroplasty. Also, knee bone kinematics is essential to assess the biofidelity of the computational models. Kinematics of the human knee has been reported in the literature either using in vitro or in vivo methodologies. In vivo methodology is widely preferred due to biomechanical accuracies. However, it is challenging to obtain the kinematic data in vivo due to limitations in existing methods. One of the several existing methods used in such application is using X-ray fluoroscopy imaging, which allows for the non-invasive quantification of bone kinematics. In the fluoroscopy imaging method, due to procedural simplicity and low radiation exposure, single-plane fluoroscopy (SF) is the preferred tool to study the in vivo kinematics of the knee joint. Evaluation of the three-dimensional (3D) kinematics from the SF imagery is possible only if prior knowledge of the shape of the knee bones is available. The standard technique for acquiring the knee shape is to either segment Magnetic Resonance (MR) images, which is expensive to procure, or Computed Tomography (CT) images, which exposes the subjects to a heavy dose of ionizing radiation. Additionally, both the segmentation procedures are time-consuming and labour-intensive. An alternative technique that is rarely used is to reconstruct the knee shape from the SF images. It is less expensive than MR imaging, exposes the subjects to relatively lower radiation than CT imaging, and since the kinematic study and the shape reconstruction could be carried out using the same device, it could save a considerable amount of time for the researchers and the subjects. However, due to low exposure levels, SF images are often characterized by a low signal-to-noise ratio, making it difficult to extract the required information to reconstruct the shape accurately. In comparison to conventional X-ray images, SF images are of lower quality and have less detail. Additionally, existing methods for reconstructing the shape of the knee remain generally inconvenient since they need a highly controlled system: images must be captured from a calibrated device, care must be taken while positioning the subject's knee in the X-ray field to ensure image consistency, and user intervention and expert knowledge is required for 3D reconstruction. In an attempt to simplify the existing process, this thesis proposes a new methodology to reconstruct the 3D shape of the knee bones from multiple uncalibrated SF images using deep learning. During the image acquisition using the SF, the subjects in this approach can freely rotate their leg (in a fully extended, knee-locked position), resulting in several images captured in arbitrary poses. Relevant features are extracted from these images using a novel feature extraction technique before feeding it to a custom-built Convolutional Neural Network (CNN). The network, without further optimization, directly outputs a meshed 3D surface model of the subject's knee joint. The whole procedure could be completed in a few minutes. The robust feature extraction technique can effectively extract relevant information from a range of image qualities. When tested on eight unseen sets of SF images with known true geometry, the network reconstructed knee shape models with a shape error (RMSE) of 1.91± 0.30 mm for the femur, 2.3± 0.36 mm for the tibia and 3.3± 0.53 mm for the patella. The error was calculated after rigidly aligning (scale, rotation, and translation) each of the reconstructed shape models with the corresponding known true geometry (obtained through MRI segmentation). Based on a previous study that examined the influence of reconstructed shape accuracy on the precision of the evaluation of tibiofemoral kinematics, the shape accuracy of the proposed methodology might be adequate to precisely track the bone kinematics, although further investigation is required

Regression learning for 2D/3D image registration

Image registration is a common technique in medical image analysis. The goal of image registration is to discover the underlying geometric transformation of target objects or regions appearing in two images. This dissertation investigates image registration methods for lung Image-Guided Radiation Therapy (IGRT). The goal of lung IGRT is to lay the radiation beam on the ever-changing tumor centroid but avoid organs at risk under the patient's continuous respiratory motion during the therapeutic procedure. To achieve this goal, I developed regression learning methods that compute the patient's 3D deformation between a treatment-time acquired x-ray image and a treatment-planning CT image (2D/3D image registration) in real-time. The real-time computation involves learning x-ray to 3D deformation regressions from a simulated patient-specific training set that captures credible deformation variations obtained from the patient's Respiratory-Correlated CT (RCCT) images. At treatment time, the learned regressions can be applied efficiently to the acquired x-ray image to yield an estimation of the patient's 3D deformation. In this dissertation, three regression learning methods - linear, non-linear, and locally-linear regression learning methods are presented to approach this 2D/3D image registration problem.Doctor of Philosoph

Spatio-temporal data fusion in cerebral angiography

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 153-167).This thesis provides a framework for generating the previously unobtained high resolution time sequences of 3D images that show the dynamics of cerebral blood flow. These sequences allow image feedback during medical procedures that can facilitate the detection and observation of stenosis, aneurysms, and clots. The 3D time series is constructed by fusing together a single static 3D image with one or more time sequence of 2D projections. The fusion process utilizes a variational approach that constrains the volumes to have both smoothly varying regions separated by edges and sparse regions of non-zero support. Results are presented on both clinical and simulated phantom data sets. The 3D time series results are visualized using the following tools: time series of intensity slices, synthetic X-rays from an arbitrary view, time series of isosurfaces, and 3D surfaces that show arrival times of contrast using color. This thesis also details the different steps needed to prepare the two classes of data. In addition to the spatio-temporal data fusion algorithm, three new algorithms are presented: a single pass groupwise registration algorithm for registering the time series, a 2D-3D registration algorithm for registering the time series with respect to the 3D volume, and a modified adaptive version of the Cusum algorithm used for determining arrival times of contrast within the 2D time sequences.by Andrew David Copeland.Ph.D

Simulation morpho-fonctionnelle et indices temporels quantifiés de cohérence articulaire. Application à la qualité de mouvements réels et simulés

The muscoloskeletal system is the subject of several studies, on the one hand to increase the basic medical knowledge, on the other hand for morphological or functional parameters to be taken into account as part of routine clinical rehabilitation or protocols of navigated surgery. The main objective of this thesis is to better describe and quantify the behavior of joint reports during a movement. We have decided to describe the joint by the bias of the geometry adopting morpho-functional concept that links the morphology of the joint surface to the function of the joint. We proposed an original kinematic modeling of the movement of flexion/extension of the knee based solely on the 3D model of the joint obtained by CT scan or MRI. This model is based mainly on the assumption that the knee does not have a single fixed axis of rotation passing through the condyles but an axis of rotation which varies during movement . The geometric approach is also the basis of our method to qualify and quantify joint's congruence during movement. Thus to quality a motion we performed a time analysis of the relative positions of the bones in the joint looking specifically temporal distributions of distance between the articular surfaces . All these temporal distributions are grouped on the original graph called Figure of Articular Coherence (FoAC). To quantify the observations related to this qualitative tool (FoAC) we completed the implementation of a second original descriptor : the Index of articular Articular (IoAC). These tools are information carriers such as the presence of collision or dislocation during movement and were used as well to account for the quality of a joint motion or for comparing different surgery protocols. The description of joint have been treated from the point of view of the kinematic, this work could be coupled with dynamic models taking into account external forces and constraints imposed by muscles and ligaments.L'appareil locomoteur humain fait l'objet d'un grand nombre d'études, d'une part pour augmenter la connaissance médicale fondamentale, d'autre part pour obtenir des paramètres morphologiques ou fonctionnels à prendre en compte dans le cadre de routines cliniques de rééducation ou de protocoles de chirurgie naviguée. L'objectif principal de ces travaux de thèse est de mieux décrire et quantifier le comportement des rapports articulaires au cours d'un mouvement. La description a été menée du point de vue de la géométrie des structures osseuses en adoptant le concept morpho-fonctionnel qui unit la morphologie de la surface articulaire à la fonction de l'articulation. Nous avons ainsi proposé une modélisation cinématique originale du mouvement de flexion/extension du genou en nous basant uniquement sur le modèle 3D de l'articulation obtenu par le biais d'imageurs IRM ou Scanner. Cette modélisation repose principalement sur l'hypothèse que le genou ne possède pas un seul axe fixe de rotation passant par les extrémités des condyles mais un axe de rotation qui varie au cours du mouvement. L'approche géométrique est également à la base de notre méthode de quantification des rapports articulaires au cours du mouvement. Ainsi pour rendre compte de la qualité d'un mouvement nous avons effectué une analyse temporelle des positions relatives des os dans l'articulation en regardant plus précisément les distributions temporelles des distances entre les surfaces articulaires. L'ensemble de ces distributions temporelles sont regroupées sur un graphique original appelé Figure de Cohérence Articulaire (FoAC). Afin de quantifier les observations liées à cet outil qualitatif (FoAC) nous l¿avons complété par la mise en place d¿un deuxième descripteur original l'Indice de Cohérence Articulaire (IoAC). Ces outils sont porteurs d'informations telles que la présence de collision ou de dislocation au cours du mouvement et ont été utilisés aussi bien pour rendre compte de la qualité d'un mouvement articulaire que pour comparer différents protocoles d'acquisitions cinématiques ou différents protocoles chirurgicaux. La description des rapports articulaires ayant été traitée du point de vue de la cinématique, ces travaux pourront être couplés à des modèles dynamiques tenant compte aussi bien des forces extérieures que des contraintes imposées par les muscles et les ligaments