197 research outputs found

    Design of a multimodal rendering system

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    This paper addresses the rendering of aligned regular multimodal datasets. It presents a general framework of multimodal data fusion that includes several data merging methods. We also analyze the requirements of a rendering system able to provide these different fusion methods. On the basis of these requirements, we propose a novel design for a multimodal rendering system. The design has been implemented and proved showing to be efficient and flexible.Postprint (published version

    Applications of a Biomechanical Patient Model for Adaptive Radiation Therapy

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    Biomechanical patient modeling incorporates physical knowledge of the human anatomy into the image processing that is required for tracking anatomical deformations during adaptive radiation therapy, especially particle therapy. In contrast to standard image registration, this enforces bio-fidelic image transformation. In this thesis, the potential of a kinematic skeleton model and soft tissue motion propagation are investigated for crucial image analysis steps in adaptive radiation therapy. The first application is the integration of the kinematic model in a deformable image registration process (KinematicDIR). For monomodal CT scan pairs, the median target registration error based on skeleton landmarks, is smaller than (1.6 ± 0.2) mm. In addition, the successful transferability of this concept to otherwise challenging multimodal registration between CT and CBCT as well as CT and MRI scan pairs is shown to result in median target registration error in the order of 2 mm. This meets the accuracy requirement for adaptive radiation therapy and is especially interesting for MR-guided approaches. Another aspect, emerging in radiotherapy, is the utilization of deep-learning-based organ segmentation. As radiotherapy-specific labeled data is scarce, the training of such methods relies heavily on augmentation techniques. In this work, the generation of synthetically but realistically deformed scans used as Bionic Augmentation in the training phase improved the predicted segmentations by up to 15% in the Dice similarity coefficient, depending on the training strategy. Finally, it is shown that the biomechanical model can be built-up from automatic segmentations without deterioration of the KinematicDIR application. This is essential for use in a clinical workflow

    Advances in Groupwise Image Registration

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    Advances in Groupwise Image Registration

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    Medical image registration using Edgeworth-based approximation of Mutual Information

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    International audienceWe propose a new similarity measure for iconic medical image registration, an Edgeworth-based third order approximation of Mutual Information (MI) and named 3-EMI. Contrary to classical Edgeworth-based MI approximations, such as those proposed for inde- pendent component analysis, the 3-EMI measure is able to deal with potentially correlated variables. The performance of 3-EMI is then evaluated and compared with the Gaussian and B-Spline kernel-based estimates of MI, and the validation is leaded in three steps. First, we compare the intrinsic behavior of the measures as a function of the number of samples and the variance of an additive Gaussian noise. Then, they are evaluated in the context of multimodal rigid registration, using the RIRE data. We finally validate the use of our measure in the context of thoracic monomodal non-rigid registration, using the database proposed during the MICCAI EMPIRE10 challenge. The results show the wide range of clinical applications for which our measure can perform, including non-rigid registration which remains a challenging problem. They also demonstrate that 3-EMI outperforms classical estimates of MI for a low number of samples or a strong additive Gaussian noise. More generally, our measure gives competitive registration results, with a much lower numerical complexity compared to classical estimators such as the reference B-Spline kernel estimator, which makes 3-EMI a good candidate for fast and accurate registration tasks

    Optimized monomodal image registration using cuckoo search algorithm

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    Medical image registration, which is employed in analyzing the similarity merits in helping the diagnosis is an important part of the medical image analysis. The process involves combining two or more images in order to provide more information. Therefore, there is a need for a method that can produce an image as a registration result that can produce more information without any loss of the input information and without any redundancy. The accuracy and computation time of the existing picture registration approach are now in question, although they could be improved if an optimization methodology is applied. Hence, this research proposed an enhancement of the image registration process focusing on monomodal registration by incorporating an optimization method called Cuckoo Search (CS) algorithm with Levy flight generation. This method was used to find the optimum parameter value (Gradient Magnitude Tolerance, Minimum Step Length, Maximum Step Length) and it was tested to brain, breast and kidney cancer that are captured on Magnetic Resonance Imaging (MRI) image. The performance of the proposed method was then compared with standard monomodal registration. For all the cases investigated, the experimental results were validated by measuring the following: Mutual Information (MI), Normalized Mutual Information (NMI), Mean Square Error (MSE), Coefficient Correlation (CC) and Central Processing Unit run-time. The results of the study illustrated that the proposed method achieved the best 2% improvement in MI, NMI, MSE, CC results. In addition, the proposed method reduced about 40% in Central Processing Unit run-time as compared to the benchmarks methods. This indicates that the proposed method has the potential to provide faster and better medical image registration results

    Groupwise Non-Rigid Registration with Deep Learning: An Affordable Solution Applied to 2D Cardiac Cine MRI Reconstruction

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    Groupwise image (GW) registration is customarily used for subsequent processing in medical imaging. However, it is computationally expensive due to repeated calculation of transformations and gradients. In this paper, we propose a deep learning (DL) architecture that achieves GW elastic registration of a 2D dynamic sequence on an affordable average GPU. Our solution, referred to as dGW, is a simplified version of the well-known U-net. In our GW solution, the image that the other images are registered to, referred to in the paper as template image, is iteratively obtained together with the registered images. Design and evaluation have been carried out using 2D cine cardiac MR slices from 2 databases respectively consisting of 89 and 41 subjects. The first database was used for training and validation with 66.6–33.3% split. The second one was used for validation (50%) and testing (50%). Additional network hyperparameters, which are—in essence—those that control the transformation smoothness degree, are obtained by means of a forward selection procedure. Our results show a 9-fold runtime reduction with respect to an optimization-based implementation; in addition, making use of the well-known structural similarity (SSIM) index we have obtained significative differences with dGW with respect to an alternative DL solution based on Voxelmorph

    Methods for three-dimensional Registration of Multimodal Abdominal Image Data

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    Multimodal image registration benefits the diagnosis, treatment planning and the performance of image-guided procedures in the liver, since it enables the fusion of complementary information provided by pre- and intrainterventional data about tumor localization and access. Although there exist various registration methods, approaches which are specifically optimized for the registration of multimodal abdominal scans are only scarcely available. The work presented in this thesis aims to tackle this problem by focusing on the development, optimization and evaluation of registration methods specifically for the registration of multimodal liver scans. The contributions to the research field of medical image registration include the development of a registration evaluation methodology that enables the comparison and optimization of linear and non-linear registration algorithms using a point-based accuracy measure. This methodology has been used to benchmark standard registration methods as well as novel approaches that were developed within the frame of this thesis. The results of the methodology showed that the employed similarity measure used during the registration has a major impact on the registration accuracy of the method. Due to this influence, two alternative similarity metrics bearing the potential to be used on multimodal image data are proposed and evaluated. The first metric relies on the use of gradient information in form of Histograms of Oriented Gradients (HOG) whereas the second metric employs a siamese neural network to learn a similarity measure directly on the image data. The evaluation showed, that both metrics could compete with state of the art similarity measures in terms of registration accuracy. The HOG-metric offers the advantage that it does not require ground truth data to learn a similarity estimation, but instead it is applicable to various data sets with the sole requirement of distinct gradients. However, the Siamese metric is characterized by a higher robustness for large rotations than the HOG-metric. To train such a network, registered ground truth data is required which may be critical for multimodal image data. Yet, the results show that it is possible to apply models trained on registered synthetic data on real patient data. The last part of this thesis focuses on methods to learn an entire registration process using neural networks, thereby offering the advantage to replace the traditional, time-consuming iterative registration procedure. Within the frame of this thesis, the so-called VoxelMorph network which was originally proposed for monomodal, non-linear registration learning is extended for affine and multimodal registration learning tasks. This extension includes the consideration of an image mask during metric evaluation as well as loss functions for multimodal data, such as the pretrained Siamese metric and a loss relying on the comparison of deformation fields. Based on the developed registration evaluation methodology, the performance of the original network as well as the extended variants are evaluated for monomodal and multimodal registration tasks using multiple data sets. With the extended network variants, it is possible to learn an entire multimodal registration process for the correction of large image displacements. As for the Siamese metric, the results imply a general transferability of models trained with synthetic data to registration tasks including real patient data. Due to the lack of multimodal ground truth data, this transfer represents an important step towards making Deep Learning based registration procedures clinically usable
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