7,567 research outputs found
Heritability analysis of surface-based cortical thickness estimation on a large twin cohort
No full textThe aim of this paper is to assess the heritability of cerebral cortex, based on measurements of grey matter (GM) thickness derived from structural MR images (sMRI). With data acquired from a large twin cohort (328 subjects), an automated method was used to estimate the cortical thickness, and EM-ICP surface registration algorithm was used to establish the correspondence of cortex across the population. An ACE model was then employed to compute the heritability of cortical thickness. Heritable cortical thickness measures various cortical regions, especially in frontal and parietal lobes, such as bilateral postcentral gyri, superior occipital gyri, superior parietal gyri, precuneus, the orbital part of the right frontal gyrus, right medial superior frontal gyrus, right middle occipital gyrus, right paracentral lobule, left precentral gyrus, and left dorsolateral superior frontal gyrus
A multi-view approach to multi-modal MRI cluster ensembles
No full textIt has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work proposes a multi-view consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information
Deep anisotropic dry etching of silicon microstructures by high-density plasmas
No full textThis thesis deals with the dry etching of deep anisotropic microstructures in monocrystalline silicon by high-density plasmas. High aspect ratio trenches are necessary in the fabrication of sensitive inertial devices such as accellerometers and gyroscopes. The etching of silicon in fluorine-based plasmas is isotropic. To obtain anisotropy the addition of sidewall passivation is necessary. This is achieved with both oxygen passivation at low temperatures and fluorocarbon passivation at room temperature. A quantitative approach was pursued to explain the etching mechanism. The etch results were analysed using the measured plasma species fluxes and the surface composition. Moreover, the transport of the plasma species in narrow anisotropic structures is a fundamental factor determining the etch rate and the profile evolution. The experimental methods such as the etching equipment, plasma diagnostics, surface analysis and sample preparation are described in chapter 2. Three etching processes were investigated: the cryogenic etching process with oxygen passivation at low temperatures, the Bosch process with fluorocarbon passivation at room temperature and the novel triple pulse process that was developed in our laboratory. The polymer deposition mechanism and the characteristic role of the ions are also explained. The cryogenic etching process is discussed in chapter 3. Fluorine radicals, oxygen radicals and ion bombardment are responsible for the three main sub-processes, that is, etching, sidewall passivation and depassivation of the trench bottom, respectively. Etching experiments with an extremely low ion-to-radical flux ratio were used to reveal the etching mechanism. Crystal orientation dependent etching leading to Si(111) crystal facets is observed in a surface kinetics controlled regime. By varying the plasma conditions it is possible to adjust the etching mechanism from fluorine-limited to ion-limited. Controlled etching is obtained because the etching is tuned from aspect ratio dependent in the fluorine-limited domain to aspect ratio independent in the ion-limited domain. The transport of radicals in high aspect ratio trenches is an important limiting factor and was investigated with special structures. The etch results are described by an analytic model that is based on the surface site balance of fluorine and oxygen radicals. The results are further explained with a Monte Carlo simulation model. The Bosch process is clarified in chapter 4. The anisotropy of the etched structures is controlled by balancing the etching and passivation pulse. However, the maximal obtainable aspect ratio is limited by convergence of the trench sidewalls due to excessive passivation. The maximal obtainable aspect ratio increases if the ion-to-radical flux ratio increases. The transport of ions is an important limiting factor in the depassivation of the bottom of the trench. Divergence of the ion beam leads to a reduction of the ion flux, so that the fluorocarbon passivation is insufficiently removed near the base of the sidewalls. The average ion angle was measured and correlated to the maximal obtainable aspect ratio. The Bosch process was improved at the depassivation side with the triple pulse process and at the passivation side with preferential sidewall deposition. The triple pulse process that is described in chapter 5 has the aim to improve the depassivation in deep trenches. The three main sub-processes are decoupled using a separate depassivation pulse directly after the etching and passivation pulses. The fluorocarbon passivation is efficiently removed with low-pressure, high-density, oxygen-based plasmas. The investigated plasma chemistries include O2, CO2 and SO2. The triple pulse process leads to better profile control with a straight trench bottom. However, the maximal obtainable aspect ratio is comparable to the Bosch process because a larger etch depth and a small lateral etch cancel out. The polymer deposition mechanism is treated in chapter 6 with the aim to understand the fluorocarbon passivation in deep trenches. The deposition on plane surfaces and on special structures was investigated to distinguish between the radical-induced and ion-enhanced components. A simple analytical model, which explains the main deposition characteristics, was developed. Preferential sidewall deposition is obtained for higher ion fluxes and higher bias voltages where sputtering plays an important role. In this case no fluorocarbon passivation has to be removed from the bottom of the trench. The trench profile was optimised in the Bosch process by tuning the bias voltage during etching and passivation independently. It resulted in perfectly anisotropic trenches but the maximal obtainable aspect ratio was still limited by a small lateral etch. The characteristic role of the ions in the etching mechanism is explained in chapter 7. Ion-induced etching of both SiC in a SF6-O2 plasma and Si in a Cl2 plasma were investigated. The impact of the ions on the profile evolution can be examined more explicitly because spontaneous chemical reactions are absent for these plasma-material systems. The etching mechanism varies from fluorine-limited to ion-limited depending on the radical-to-ion flux ratio. Microtrenches are observed for an ion-limited etching mechanism. Fluorine-limited SiC etching is aspect ratio dependent in contrast to ion-limited SiC etching, which is aspect ratio independent. The etching of high aspect ratio SiC structures is limited by the positive sidewall taper. This is presumably caused by insufficient removal of the thin fluorocarbon layer on the surface. Si etching in a Cl2 plasma is always aspect ratio independent in contrast to SiC etching because of the low reaction probability. The conclusions and recommendations of this thesis are given in chapter 8.Applied Science
Registration of organs with sliding interfaces and changing topologies
No full textSmoothness and continuity assumptions on the deformation field in deformable image registration do not hold for applications where the imaged objects have sliding interfaces. Recent extensions to deformable image registration that accommodate for sliding motion of organs are limited to sliding motion along approximately planar surfaces or cannot model sliding that changes the topological configuration in case of multiple organs. We propose a new extension to free-form image registration that is not limited in this way. Our method uses a transformation model that consists of uniform B-spline transformations for each organ region separately, which is based on segmentation of one image. Since this model can create overlapping regions or gaps between regions, we introduce a penalty term that minimizes this undesired effect. The penalty term acts on the surfaces of the organ regions and is optimized simultaneously with the image similarity. To evaluate our method registrations were performed on publicly available inhale-exhale CT scans for which performances of other methods are known. Target registration errors are computed on dense landmark sets that are available with these datasets. On these data our method outperforms the other methods in terms of target registration error and, where applicable, also in terms of overlap and gap volumes. The approximation of the other methods of sliding motion along planar surfaces is reasonably well suited for the motion present in the lung data. The ability of our method to handle sliding along curved boundaries and for changing region topology configurations was demonstrated on synthetic images. © 2014 SPIE
Surface-based reconstruction and diffusion MRI in the assessment of gray and white matter damage in multiple sclerosis
No full textRobust temporal alignment of multimodal cardiac sequences
No full text© 2015 SPIE. Given the dynamic nature of cardiac function, correct temporal alignment of pre-operative models and intraoperative images is crucial for augmented reality in cardiac image-guided interventions. As such, the current study focuses on the development of an image-based strategy for temporal alignment of multimodal cardiac imaging sequences, such as cine Magnetic Resonance Imaging (MRI) or 3D Ultrasound (US). First, we derive a robust, modality-independent signal from the image sequences, estimated by computing the normalized cross-correlation between each frame in the temporal sequence and the end-diastolic frame. This signal is a resembler for the left-ventricle (LV) volume curve over time, whose variation indicates different temporal landmarks of the cardiac cycle. We then perform the temporal alignment of these surrogate signals derived from MRI and US sequences of the same patient through Dynamic Time Warping (DTW), allowing to synchronize both sequences. The proposed framework was evaluated in 98 patients, which have undergone both 3D+t MRI and US scans. The end-systolic frame could be accurately estimated as the minimum of the image-derived surrogate signal, presenting a relative error of 1.6 ± 1.9% and 4.0 ± 4.2% for the MRI and US sequences, respectively, thus supporting its association with key temporal instants of the cardiac cycle. The use of DTW reduces the desynchronization of the cardiac events in MRI and US sequences, allowing to temporally align multimodal cardiac imaging sequences. Overall, a generic, fast and accurate method for temporal synchronization of MRI and US sequences of the same patient was introduced. This approach could be straightforwardly used for the correct temporal alignment of pre-operative MRI information and intra-operative US images.status: Publishe
Improving the robustness of interventional 4D ultrasound segmentation through the use of personalized prior shape models
No full text© 2015 SPIE. While fluoroscopy is still the most widely used imaging modality to guide cardiac interventions, the fusion of pre-operative Magnetic Resonance Imaging (MRI) with real-time intra-operative ultrasound (US) is rapidly gaining clinical acceptance as a viable, radiation-free alternative. In order to improve the detection of the left ventricular (LV) surface in 4D ultrasound, we propose to take advantage of the pre-operative MRI scans to extract a realistic geometrical model representing the patients cardiac anatomy. This could serve as prior information in the interventional setting, allowing to increase the accuracy of the anatomy extraction step in US data. We have made use of a real-time 3D segmentation framework used in the recent past to solve the LV segmentation problem in MR and US data independently and we take advantage of this common link to introduce the prior information as a soft penalty term in the ultrasound segmentation algorithm. We tested the proposed algorithm in a clinical dataset of 38 patients undergoing both MR and US scans. The introduction of the personalized shape prior improves the accuracy and robustness of the LV segmentation, as supported by the error reduction when compared to core lab manual segmentation of the same US sequences.status: Publishe
Supervised local error estimation for nonlinear image registration using convolutional neural networks
No full textError estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.</p
Automatic brain extraction in fetal MRI using multi-atlas-based segmentation
No full textIn fetal brain MRI, most of the high-resolution reconstruction algorithms rely on brain segmentation as a preprocessing step. Manual brain segmentation is however highly time-consuming and therefore not a realistic solution. In this work, we assess on a large dataset the performance of Multiple Atlas Fusion (MAF) strategies to automatically address this problem. Firstly, we show that MAF significantly increase the accuracy of brain segmentation as regards single-atlas strategy. Secondly, we show that MAF compares favorably with the most recent approach (Dice above 0.90). Finally, we show that MAF could in turn provide an enhancement in terms of reconstruction quality
Personalized connectome fingerprints: Their importance in cognition from childhood to adult years
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