757 research outputs found
3D nonrigid medical image registration using a new information theoretic measure.
International audienceThis work presents a novel method for the nonrigid registration of medical images based on the Arimoto entropy, a generalization of the Shannon entropy. The proposed method employed the Jensen-Arimoto divergence measure as a similarity metric to measure the statistical dependence between medical images. Free-form deformations were adopted as the transformation model and the Parzen window estimation was applied to compute the probability distributions. A penalty term is incorporated into the objective function to smooth the nonrigid transformation. The goal of registration is to optimize an objective function consisting of a dissimilarity term and a penalty term, which would be minimal when two deformed images are perfectly aligned using the limited memory BFGS optimization method, and thus to get the optimal geometric transformation. To validate the performance of the proposed method, experiments on both simulated 3D brain MR images and real 3D thoracic CT data sets were designed and performed on the open source elastix package. For the simulated experiments, the registration errors of 3D brain MR images with various magnitudes of known deformations and different levels of noise were measured. For the real data tests, four data sets of 4D thoracic CT from four patients were selected to assess the registration performance of the method, including ten 3D CT images for each 4D CT data covering an entire respiration cycle. These results were compared with the normalized cross correlation and the mutual information methods and show a slight but true improvement in registration accuracy
Parallel Computation of Nonrigid Image Registration
Automatic intensity-based nonrigid image registration brings significant impact in medical applications such as multimodality fusion of images, serial comparison for monitoring disease progression or regression, and minimally invasive image-guided interventions. However, due to memory and compute intensive nature of the operations, intensity-based image registration has remained too slow to be practical for clinical adoption, with its use limited primarily to as a pre-operative too. Efficient registration methods can lead to new possibilities for development of improved and interactive intraoperative tools and capabilities.
In this thesis, we propose an efficient parallel implementation for intensity-based three-dimensional nonrigid image registration on a commodity graphics processing unit. Optimization techniques are developed to accelerate the compute-intensive mutual information computation. The study is performed on the hierarchical volume subdivision-based algorithm, which is inherently faster than other nonrigid registration algorithms and structurally well-suited for data-parallel computation platforms. The proposed implementation achieves more than 50-fold runtime improvement over a standard implementation on a CPU. The execution time of nonrigid image registration is reduced from hours to minutes while retaining the same level of registration accuracy
Registration of Standardized Histological Images in Feature Space
In this paper, we propose three novel and important methods for the
registration of histological images for 3D reconstruction. First, possible
intensity variations and nonstandardness in images are corrected by an
intensity standardization process which maps the image scale into a standard
scale where the similar intensities correspond to similar tissues meaning.
Second, 2D histological images are mapped into a feature space where continuous
variables are used as high confidence image features for accurate registration.
Third, we propose an automatic best reference slice selection algorithm that
improves reconstruction quality based on both image entropy and mean square
error of the registration process. We demonstrate that the choice of reference
slice has a significant impact on registration error, standardization, feature
space and entropy information. After 2D histological slices are registered
through an affine transformation with respect to an automatically chosen
reference, the 3D volume is reconstructed by co-registering 2D slices
elastically.Comment: SPIE Medical Imaging 2008 - submissio
Numerical Methods for Pulmonary Image Registration
Due to complexity and invisibility of human organs, diagnosticians need to
analyze medical images to determine where the lesion region is, and which kind
of disease is, in order to make precise diagnoses. For satisfying clinical
purposes through analyzing medical images, registration plays an essential
role. For instance, in Image-Guided Interventions (IGI) and computer-aided
surgeries, patient anatomy is registered to preoperative images to guide
surgeons complete procedures. Medical image registration is also very useful in
surgical planning, monitoring disease progression and for atlas construction.
Due to the significance, the theories, methods, and implementation method of
image registration constitute fundamental knowledge in educational training for
medical specialists. In this chapter, we focus on image registration of a
specific human organ, i.e. the lung, which is prone to be lesioned. For
pulmonary image registration, the improvement of the accuracy and how to obtain
it in order to achieve clinical purposes represents an important problem which
should seriously be addressed. In this chapter, we provide a survey which
focuses on the role of image registration in educational training together with
the state-of-the-art of pulmonary image registration. In the first part, we
describe clinical applications of image registration introducing artificial
organs in Simulation-based Education. In the second part, we summarize the
common methods used in pulmonary image registration and analyze popular papers
to obtain a survey of pulmonary image registration
Diffeomorphic demons using normalized mutual information, evaluation on multimodal brain MR images
The demons algorithm is a fast non-parametric non-rigid registration method. In recent years great efforts have been made to improve the approach; the state of the art version yields symmetric inverse-consistent largedeformation diffeomorphisms. However, only limited work has explored inter-modal similarity metrics, with no practical evaluation on multi-modality data. We present a diffeomorphic demons implementation using the analytical gradient of Normalised Mutual Information (NMI) in a conjugate gradient optimiser. We report the first qualitative and quantitative assessment of the demons for inter-modal registration. Experiments to spatially normalise real MR images, and to recover simulated deformation fields, demonstrate (i) similar accuracy from NMI-demons and classical demons when the latter may be used, and (ii) similar accuracy for NMI-demons on T1w-T1w and T1w-T2w registration, demonstrating its potential in multi-modal scenarios
Multimodality and Nonrigid Image Registration with Application to Diffusion Tensor Imaging
The great challenge in image registration is to devise computationally efficient algorithms for aligning images
so that their details overlap accurately. The first problem addressed in this thesis is multimodality
medical image registration, which we formulate as an optimization problem in the information-theoretic setting.
We introduce a viable and practical image registration method by maximizing a generalized entropic
dissimilarity measure using a modified simultaneous perturbation stochastic approximation algorithm. The
feasibility of the proposed image registration approach is demonstrated through extensive experiments.
The rest of the thesis is devoted to nonrigid medical image registration. We propose an informationtheoretic
framework by optimizing a non-extensive entropic similarity measure using the quasi-Newton
method as an optimization scheme and cubic B-splines for modeling the nonrigid deformation field between
the fixed and moving 3D image pairs. To achieve a compromise between the nonrigid registration accuracy
and the associated computational cost, we implement a three-level hierarchical multi-resolution approach in
such a way that the image resolution is increased in a coarse to fine fashion. The feasibility and registration
accuracy of the proposed method are demonstrated through experimental results on a 3D magnetic resonance
data volume and also on clinically acquired 4D computed tomography image data sets. In the same vein,
we extend our nonrigid registration approach to align diffusion tensor images for multiple components by
enabling explicit optimization of tensor reorientation. Incorporating tensor reorientation in the registration
algorithm is pivotal in wrapping diffusion tensor images. Experimental results on diffusion-tensor image
registration indicate the feasibility of the proposed approach and a much better performance compared to
the affine registration method based on mutual information, not only in terms of registration accuracy in the
presence of geometric distortions but also in terms of robustness in the presence of Rician noise
The Utility of Deformable Image Registration for Small Artery Visualisation in Contrast-Enhanced Whole Body MR Angiography
Purpose;
An investigation was carried out into the effect of three image registration techniques on the diagnostic image quality of contrast-enhanced magnetic resonance angiography (CE-MRA) images.
Methods Whole-body CE-MRA data from the lower legs of 27 patients recruited onto a study of asymptomatic atherosclerosis were processed using three deformable image registration algorithms. The resultant diagnostic image quality was evaluated qualitatively in a clinical evaluation by four expert observers, and quantitatively by measuring contrast-to-noise ratios and volumes of blood vessels, and assessing the techniquesâ ability to correct for varying degrees of motion.
Results The first registration algorithm (âAIRâ) introduced significant stenosis-mimicking artefacts into the blood vesselsâ appearance, observed both qualitatively (clinical evaluation) and quantitatively (vessel volume measurements). The other two algorithms (âSlicerâ and âSEMIâ) based on the normalised mutual information (NMI) concept and designed specifically to deal with variations in signal intensity as found in contrast-enhanced image data, did not suffer from this serious issue but were rather found to significantly improve the diagnostic image quality both qualitatively and quantitatively, and demonstrated a significantly improved ability to deal with the common problem of patient motion.
Conclusions This work highlights both the significant benefits to be gained through the use of suitable registration algorithms and the deleterious effects of an inappropriate choice of algorithm for contrast-enhanced MRI data. The maximum benefit was found in the lower legs, where the small arterial vessel diameters and propensity for leg movement during image acquisitions posed considerable problems in making accurate diagnoses from the un-registered images
Nonrigid registration of three-dimensional ultrasound and magnetic resonance images of the carotid arteries
Atherosclerosis at the carotid bifurcation can result in cerebral emboli, which in turn can block the blood supply to the brain causing ischemic strokes. Noninvasive imaging tools that better characterize arterial wall, and atherosclerotic plaque structure and composition may help to determine the factors which lead to the development of unstable lesions, and identify patients at risk of plaque disruption and stroke. Carotid magnetic resonance (MR) imaging allows for the characterization of carotid vessel wall and plaque composition, the characterization of normal and pathological arterial wall, the quantification of plaque size, and the detection of plaque integrity. On the other hand, various ultrasound (US) measurements have also been used to quantify atherosclerosis, carotid stenosis, intima-media thickness, total plaque volume, total plaque area, and vessel wall volume. Combining the complementary information provided by 3D MR and US carotid images may lead to a better understanding of the underlying compositional and textural factors that define plaque and wall vulnerability, which may lead to better and more effective stroke prevention strategies and patient management. Combining these images requires nonrigid registration to correct the nonlinear misalignments caused by relative twisting and bending in the neck due to different head positions during the two image acquisition sessions. The high degree of freedom and large number of parameters associated with existing nonrigid image registration methods causes several problems including unnatural plaque morphology alteration, high computational complexity, and low reliability. Thus, a twisting and bending model was used with only six parameters to model the normal movement of the neck for nonrigid registration. The registration technique was evaluated using 3D US and MR carotid images at two field strengths, 1.5 and 3.0 T, of the same subject acquired on the same day. The mean registration error between the segmented carotid artery wall boundaries in the target US image and the registered MR images was calculated using a distance-based error metric after applying a twisting and bending model based nonrigid registration algorithm. An average registration error of 1.4 +/- 0.3 mm was obtained for 1.5 T MR and 1.5 +/- 0.4 mm for 3.0 T MR, when registered with 3D US images using the nonrigid registration technique presented in this paper. Visual inspection of segmented vessel surfaces also showed a substantial improvement of alignment with this nonrigid registration technique compared to rigid registration
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