Dissertations / Theses on the topic 'Magnetic Resonance Imaging data'
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Acosta, Mena Dionisio M. "Statistical classification of magnetic resonance imaging data." Thesis, University of Sussex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390913.
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Rydell, Joakim. "Advanced MRI Data Processing." Doctoral thesis, Linköping : Department of Biomedical Engineering, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10038.
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Hotrakool, Wattanit. "Compressed sensing for functional magnetic resonance imaging data." Thesis, University of Sheffield, 2016. http://etheses.whiterose.ac.uk/15704/.
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This thesis addresses the possibility of applying the compressed sensing (CS) framework to Functional Magnetic Resonance Imaging (fMRI) acquisition. The fMRI is one of the non-invasive neuroimaging technique that allows the brain activity to be captured and analysed in a living body. One disadvantage of fMRI is the trade-off between the spatial and temporal resolution of the data. To keep the experiments within a reasonable length of time, the current acquisition technique sacrifices the spatial resolution in favour of the temporal resolution. It is possible to improve this trade-off using compressed sensing. The main contribution of this thesis is to propose a novel reconstruction method, named Referenced Compressed Sensing, which exploits the redundancy between a signal and a correlated reference by using their distance as an objective function. The compressed video sequences reconstructed using Referenced CS have at least 50% higher in terms of Peak Signal-to-Noise Ratio (PSNR) compared to state-of-the-art conventional reconstruction methods. This thesis also addresses two issues related to Referenced CS. Firstly, the relationship between the reference and the reconstruction performance is studied. To maintain the high-quality references, the Running Gaussian Average (RGA) reference estimator is proposed. The reconstructed results have at least 3dB better PSNR performance with the use of RGA references. Secondly, the Referenced CS with Least Squares is proposed. This study shows that by incorporating the correlated reference, it is possible to perform a linear reconstruction as opposed to the iterative reconstruction commonly used in CS. This approach gives at least 19% improvement in PSNR compared to the state of the art, while reduces the computation time by at most 1200 times. The proposed method is applied to the fMRI data. This study shows that, using the same amount of samples, the data reconstructed using Referenced CS has higher resolution than the conventional acquisition technique and has on average 50% higher PSNR than state-of-the-art reconstructions. Lastly, to enhance the feature of interest in the fMRI data, the baseline independent (BI) analysis is proposed. Using the BI analysis shows up to 25% improvement in the accuracy of the Referenced CS feature.
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Wu, Bing. "Exploiting data sparsity in parallel magnetic resonance imaging." Thesis, University of Canterbury. Electrical and Computer Engineering, 2010. http://hdl.handle.net/10092/3914.
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Magnetic resonance imaging (MRI) is a widely employed imaging modality that allows observation of the interior of human body. Compared to other imaging modalities such
as the computed tomography (CT), MRI features a relatively long scan time that gives rise to many potential issues. The advent of parallel MRI, which employs multiple receiver
coils, has started a new era in speeding up the scan of MRI by reducing the number of data acquisitions. However, the finally recovered images from under-sampled data sets often
suffer degraded image quality.
This thesis explores methods that incorporate prior knowledge of the image to be reconstructed to achieve improved image recovery in parallel MRI, following the philosophy that ‘if some prior knowledge of the image to be recovered is known, the image could be recovered better than without’. Specifically, the prior knowledge of image sparsity is utilized. Image sparsity exists in different domains. Image sparsity in the image domain refers to the fact that the imaged object only occupies a portion of the imaging field of view; image sparsity may also exist in a transform domain for which there is a high level of energy
concentration in the image transform. The use of both types of sparsity is considered in this thesis.
There are three major contributions in this thesis. The first contribution is the development of ‘GUISE’. GUISE employs an adaptive sampling design method that achieves better exploitation of image domain sparsity in parallel MRI. Secondly, the development of ‘PBCS’ and ‘SENSECS’. PBCS achieves better exploitation of transform domain sparsity by incorporating a prior estimate of the image to be recovered. SENSECS is an application of PBCS that achieves better exploitation of transform domain sparsity in parallel MRI. The third contribution is the
implementation of GUISE and PBCS in contrast enhanced MR angiography (CE MRA). In their applications in CE MRA, GUISE and PBCS share the common ground of exploiting the high sparsity of the contrast enhanced angiogram.
The above developments are assessed in various ways using both simulated and experimental data. The potential extensions of these methods are also suggested.
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Maas, Luis C. (Luis Carlos). "Processing strategies for functional magnetic resonance imaging data sets." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/85262.
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Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 1999.Includes bibliographical references (leaves 108-118).
by Luis Carlos Maas, III.
Ph.D.
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Bernier, Jessica Ashley. "A RECONSTRUCTION PROGRAM FOR RADIAL MAGNETIC RESONANCE IMAGING DATA." Thesis, The University of Arizona, 2009. http://hdl.handle.net/10150/192289.
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Hamilton, Lei Hou. "Reduced-data magnetic resonance imaging reconstruction methods: constraints and solutions." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42707.
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Imaging speed is very important in magnetic resonance imaging (MRI), especially in dynamic cardiac applications, which involve respiratory motion and heart motion. With the introduction of reduced-data MR imaging methods, increasing acquisition speed has become possible without requiring a higher gradient system. But these reduced-data imaging methods carry a price for higher imaging speed. This may be a signal-to-noise ratio (SNR) penalty, reduced resolution, or a combination of both. Many methods sacrifice edge information in favor of SNR gain, which is not preferable for applications which require accurate detection of myocardial boundaries. The central goal of this thesis is to develop novel reduced-data imaging methods to improve reconstructed image performance. This thesis presents a novel reduced-data imaging method, PINOT (Parallel Imaging and NOquist in Tandem), to accelerate MR imaging. As illustrated by a variety of computer simulated and real cardiac MRI data experiments, PINOT preserves the edge details, with flexibility of improving SNR by regularization. Another contribution is to exploit the data redundancy from parallel imaging, rFOV and partial Fourier methods. A Gerchberg Reduced Iterative System (GRIS), implemented with the Gerchberg-Papoulis (GP) iterative algorithm is introduced. Under the GRIS, which utilizes a temporal band-limitation constraint in the image reconstruction, a variant of Noquist called iterative implementation iNoquist (iterative Noquist) is proposed. Utilizing a different source of prior information, first combining iNoquist and Partial Fourier technique (phase-constrained iNoquist) and further integrating with parallel imaging methods (PINOT-GRIS) are presented to achieve additional acceleration gains.
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Lin, Qihua. "Bayesian hierarchial spatiotemporal modeling of functional magnetic resonance imaging data." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3245023.
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Thesis (Ph.D. in Statistical Science)--S.M.U., 2007.Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 67-12, Section: B, page: 7154. Adviser: Richard F. Gunst. Includes bibliographical references.
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Takahashi, Atsushi M. (Atsushi Mark). "Rapid data acquisition and selective excitation in magnetic resonance imaging." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28930.
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Many of the problems faced in magnetic resonance imaging and angiography are due to hardware limitations of the scanners used. The use of multidimensional excitation pulses, and new, fast acquisition sequences such as echo-planar and spiral imaging, put demands on the gradient systems and although technology is progressing in these areas, performance must be carefully monitored to ensure artefact-free images. This thesis deals with four distinct aspects of magnetic resonance imaging (MRI).A simple hardware modification to decrease the minimum achievable echo-time of our MRI scanner was designed, built, programmed and tested and was found to improve the quality of inflow angiograms significantly. Further improvements were demonstrated with the use of on-resonance (binomial) magnetization transfer saturation contrast enhancement pulses.
A method for measuring the k-space trajectories of gradient waveforms was adopted, modified, validated and used to measure the k-space trajectories of gradient waveforms used for selective excitation and spiral image acquisition. Distortion was observed even when gradient waveforms were designed within specifications of the manufacturer.
The literature reports the application of the k-space model, usually associated with image acquisition, to the design of multidimensional selective excitation pulses. This thesis demonstrates theoretically and experimentally the modification of the design procedure to compensate RF envelopes for distortion of the k-space trajectories of the accompanying gradient waveforms by using measured k-space data.
The correction of spiral image reconstruction algorithms to compensate for k-space trajectory distortion was also demonstrated theoretically and experimentally.
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Bell, William L. Jr. "Three-Dimensional Segmentation and Visualization of Magnetic Resonance Imaging Data." UNF Digital Commons, 1996. http://digitalcommons.unf.edu/etd/28.
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In this thesis, I shall study and compare various methods for manipulating two- and three-dimensional image data produced with a nuclear magnetic resonance scanner. In particular, I will examine ways of focusing upon specific structures internal to the object under study (segmentation); and will explore means of rendering realistic images of these structures on a computer screen using depth-cueing, shading, and ray-casting techniques.The 3DHEAD volumetric dataset used for this project was created with the Siemens Magnetom and was provided courtesy of Siemens Medical Systems, Inc., Iselin, NJ. This dataset consists of 109 slices of a human head, with each slice stored consecutively as a 256 x 256 array. Each pixel is represented by two consecutive bytes, which make one binary integer. (A similar dataset of a human knee is also available.) The 3DHEAD dataset requires about 14 Mb of disk space uncompressed. The programs which manipulate this data are MS-DOS-based and were written and compiled using Microsoft QuickC version 2.51. The 2-D programs were executed on a CompuAdd 486DXl2-50 with 8 Mb of RAM, running MS-DOS version 6.22; the 3-D programs were executed on a 133 MHz Pentium clone with 48 Mb of RAM, running the DOS shell of Microsoft Windows 95.Our immediate objectives are to produce pleasing and informative 2-D and 3-D pictures of the internal structure of some component of the human head: for example, the brain.We need to remove from the original dataset all of the data which do not represent the brain. Then, for the 3-D images, we need to render the remaining data in such a way that it possesses depth and realism.
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Friman, Ola. "Adaptive analysis of functional MRI data /." Linköping : Univ, 2003. http://www.bibl.liu.se/liupubl/disp/disp2003/tek836s.pdf.
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Diaz, De Grenu Ballestero Lara Zurine. "Optimisation of MRI data for dementia studies." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648904.
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Derbyshire, John Andrew. "Echo-planar anemometry using conventional magnetic resonance imaging hardware." Thesis, University of Cambridge, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364590.
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Hellström, Karlsson Rebecca, and Tobias Peterson. "Extracting Cardiac and Respiratory Self-Gating Signals from Magnetic Resonance Imaging Data." Thesis, KTH, Skolan för teknik och hälsa (STH), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-171570.
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Motion artefacts due to cardiac and respiratory motion present a daily challenge in cardiac Magnetic Resonance Imaging (MRI), and many different motion correction procedures are used in clinical routine imaging. To reduce motion artefacts further, patients are required to hold their breath during parts of the data acquisition, which is physically straining – especially when done repetitively. Self-Gating (SG) is a method that extracts cardiac and respiratory motion information from the MRI data in the form of signals, called SG signals, and uses them to divide the data into the specific cardiac and respiratory phases it was acquired from. This method both avoids motion artefacts and allow for free-breathing acquisition. This project’s goal was to find a method for extracting cardiac and respiratory SG signals from MRI data. The data was acquired with a golden angle radial acquisition method for 3-dimensional (3D) scans. Extraction of the raw signal was tested for both raw k-space data and high temporal resolution image series, where the images were reconstructed using a sliding window reconstruction. Filters were then applied to isolate the cardiac and respiratory information, to create separate cardiac and respiratory SG signals. Thereafter trigger points marking the beginning of the cardiac and respiratory cycles were generated. The trigger points were compared against ECG and respiratory trigger points provided by the MR scanner. The conclusion was that the SG signals based on k-space data was functional on the scans from the evaluated subjects and the most effective choice of the two options, but image based SG signals may prove to be functional after further studies.Rörelseartefakter på grund av hjärt- och respirationsrörelser är idag vardagliga utmaningar inom magnetresonanstomografi (MR) av hjärtat, och många olika metoder används för att eliminera rörelseartefakterna. Patienterna behöver dessutom hålla andan under delar av dataupptagningen, vilket är fysiskt ansträngande – speciellt när det sker upprepade gånger. Self-Gating (SG) är en metod som extraherar information hjärt- och respirationsrytm från MR-datan i form av signaler, kallade SG signaler, och använder dem för att dela in datan i de specifika hjärt- respektive respirationsfaser som var när datan upptogs. Denna metod både undviker rörelseartefakter och tillåter fri andning under dataupptagningen. Målet med det här projektet var att hitta en metod för att extrahera SG signaler för hjärt- och respirationsrytm från MR-data. Datan samlades in med en golden angle radial-upptagning för 3- dimensionella (3D) scanningar. Extraheringen av den råa signalen testades på både rå k-space data och på bildserier av 3D-bilder med hög tidsupplösning, där bilderna var rekonstruerade med en sliding window rekonstruktion. Därefter applicerades filter för att isolera hjärt- och respirationsinformationen, för att få separata SG signaler med endast hjärt- respektive respirationsrytmer. Till slut genererades triggerpunkter för att markera början av hjärt- respektive respirationscyklerna. Dessa jämfördes med triggerpunkter uppmätta med EKG och andningskudde i magnetkameran. Slutsatsen för projektet var att SG signalerna som baserades på k-space data var funktionell för de scanningar som testades och det mest effektiva alternativet, men SG signalerna som baserades på bilder kan visa sig fungera efter mer studier.
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Thomas, Scott J. 1961. "Forward modeling and data acquisition for high-accuracy electromagnetic subsurface imaging." Thesis, The University of Arizona, 1989. http://hdl.handle.net/10150/558108.
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Ash, Thomas William John. "Use of statistical classifiers in the analysis of fMRI data." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609710.
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Perez, Carlos Arturo. "Discovering causal relationships from fMRI data." [Pensacola, Fla.] : University of West Florida, 2009. http://purl.fcla.edu/fcla/etd/WFE0000189.
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Thesis (M.S.)--University of West Florida, 2009.Submitted to the Dept. of Computer Science. Title from title page of source document. Document formatted into pages; contains 90 pages. Includes bibliographical references.
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Smith, Norman Ronald. "Fast and automatic techniques for 3D visualization of MRI data." Thesis, Imperial College London, 1998. http://hdl.handle.net/10044/1/11916.
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Cui, Shenshen. "Fully Automatic Segmentation of White Matter Lesions from Multispectral Magnetic Resonance Imaging Data." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122650.
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A fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1,T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter,grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuro radiologists.
Key Words: White matter lesion, automatic segmentation, graph cuts, MRI, PIVUS
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Bishop, Jonathan Eric. "Techniques for improved time and data efficiency in Fourier-encoded magnetic resonance imaging." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape16/PQDD_0021/NQ28271.pdf.
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Collins, D. Louis. "3D model-based segmentation of individual brain structures from magnetic resonance imaging data." Thesis, McGill University, 1994. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28716.
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This thesis addresses a specific problem of model-based segmentation; namely, the automatic identification and delineation of gross anatomical structures of the human brain based on their appearance in magnetic resonance images (MRI). The approach developed in this thesis depends on a general, iterative, hierarchical registration procedure and a 3-D digital model of human brain anatomy that contains both volumetric intensity-based data and geometric atlas data that co-exist in a brain-based stereotaxic coordinate system. The model contains features derived from an MRI atlas of gross neuroanatomy, that is the result of an intensity average of 305 brains created with an automatic stereotaxic registration procedure developed here.The objective of this thesis is achieved by inverting the traditional segmentation strategy. Instead of matching geometric contours from an idealized atlas directly to the MRI data, segmentation is achieved by identifying the spatial transformation that, under certain constraints, best maps corresponding features between the model and a particular volumetric data set. After automatic recovery of the linear registration transform, the 3-D non-linear transformation is recovered by estimating the local deformation fields, recursively selected by stepping through the entire target volume in a 3D grid pattern, using cross-correlation of invariant intensity features derived from image data. This registration process is performed hierarchically, with each step in decreasing scale refining the fit of the previous step and providing input to the next. When completed, atlas contours defined in the model are mapped through the recovered transformation to segment structures in the original data set and identify them by name.
Experiments for registration and segmentation are presented using simple phantoms, a realistic digital brain phantom as well as human MRI data. The algorithm is used to estimate neuro-anatomical variability, to automatically segment cerebral structures and to create probabilistic representations of the same structures. Validation with manual methods shows that the procedure performs well, is objective and its implementation robust.
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Wright, Katherine L. "Measuring Perfusion with Magnetic Resonance Imaging using Novel Data Acquisition and Reconstruction Strategies." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1412786849.
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Herterich, Rebecka, and Anna Sumarokova. "Coil Sensitivity Estimation and Intensity Normalisation for Magnetic Resonance Imaging." Thesis, KTH, Medicinteknik och hälsosystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263149.
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The quest for improved efficiency in magnetic resonance imaging has motivated the development of strategies like parallel imaging where arrays of multiple receiver coils are operated simultaneously in parallel. The objective of this project was to find an estimation of phased-array coil sensitivity profiles of magnetic resonance images of the human body. These sensitivity maps can then be used to perform an intensity inhomogeneity correction of the images. Through investigative work in Matlab, a script was developed that uses data embedded in raw data from a magnetic resonance scan, to generate coil sensitivities for each voxel of the volume of interest and recalculate them to two-dimensional sensitivity maps of the corresponding diagnostic images. The resulting mapped sensitivity profiles can be used in Sensitivity Encoding where a more exact solution can be obtained using the carefully estimated sensitivity maps of the images.Inom magnetresonanstomografi eftersträvas förbättrad effektivitet, villket bidragit till utvecklingen av strategier som parallell imaging, där arrayer av flera mottagarspolar andvänds samtidigt. Syftet med detta projekt var att uppskattamottagarspolarnas känslighetskarta för att utnyttja dem till i metoder inom magnetresonansavbildning. Dessa känslighetskartor kan användas för att utföra intensitetsinhomogenitetskorrigering av bilderna. Genom utforskande arbete i Matlab utvecklades ett skript som tillämpar inbyggd rådata, från en magnetiskresonansavbildning för att generera spolens känslighet för varje voxel av volymen och omberäkna dem till tvådimensionella känslighetskartor av motsvarande diagnostiska bilder. De resulterande kartlagda känslighetsprofilerna kan användas i känslighetskodning, där en mer exakt lösning kan erhållas med hjälp av de noggrant uppskattade känslighetskartorna.
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Barron, Nicholas Henry. "An Analysis of an Advanced Software Business Model for Magnetic Resonance Imaging Data Post Processing." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1459422647.
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Michael, Andrew M. "Imaging schizophrenia : data fusion approaches to characterize and classify /." Online version of thesis, 2009. http://hdl.handle.net/1850/9673.
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Monnot, Cyril Gerard Valery. "Development of a data analysis platform for characterizing functional connectivity networks in rodents." Thesis, KTH, Skolan för teknik och hälsa (STH), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124391.
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This document addresses the development and implementation of a routine for analyzing resting-state functional Magnetic Resonance Imaging (rs-fMRI) data in rodents. Even though resting-state connectivity is studied in humans already for several years with diverse applications in mental disorders or degenerative brain diseases, the interest for this modality is much more recent and less common in rodents. The goal of this project is to set an ensemble of tools in order to be able for the experimental MR team of KERIC to analyze rs-fMRI in rodents in a well defined and easy way. During this project several critical choices have been done, one of them is to use the Independent Component Analysis (ICA) in order to process the data rather than a seed-based approach. Also it was decided to use medetomidine as anesthesia rather than isoflurane for the experiments. The routine developed during this project was applied for a project studying the effects of running on an animal model of depression. The routine is composed of several steps, the preprocessing of the data mainly realized with SPM8, the processing using GIFT and the postprocessing which is some statistic tests on the results from GIFT in order to reveal differences between groups using the 2nd level analysis from SPM8 and the testing the correlations between components using the FNC toolbox.Detta dokument behandlar utvecklingen och implementeringen av en rutin för att analysera bilder från resting-state funktionell Magnetisk Resonenstomografi i gnagare. Även om resting-state connectivity studerats i människor i några år, med olika applikationer i psykiska störningar och neurodegenerativa sjukdomar, är intresset för detta område är betydligt nyare bland experimentell förskare som arbetar med gnagare. Målet av denna projekt är att inställa en procedur så att KERICs experimentell MR team kan lätt analysera resting-state funktionnell MRT data. Under denna projekt har olika viktiga val gjorts, en av dem är att använda Independent Component Analysis procedur för att analysera data framför en seed-baserad teknik. En andra var att använda för anestesi medetomidin och inte isofluran för experiment. Rutinen som var utvecklad under denna projekt blev användad på data från en projekt som studerar effekter av löpning på depression hos råttorna. Rutinen är delad i några delar, den första är att förbehandla data främst med SPM8, den andra är att använda GIFT för att behandla data och den sista är att testa statistiskt resultat från ICA med SPM8 och att testa korrelation mellan komponenter med FNC.
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Laruelo, Fernandez Andrea. "Integration of magnetic resonance spectroscopic imaging into the radiotherapy treatment planning." Thesis, Toulouse 3, 2016. http://www.theses.fr/2016TOU30126/document.
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L'objectif de cette thèse est de proposer de nouveaux algorithmes pour surmonter les limitations actuelles et de relever les défis ouverts dans le traitement de l'imagerie spectroscopique par résonance magnétique (ISRM). L'ISRM est une modalité non invasive capable de fournir la distribution spatiale des composés biochimiques (métabolites) utilisés comme biomarqueurs de la maladie. Les informations fournies par l'ISRM peuvent être utilisées pour le diagnostic, le traitement et le suivi de plusieurs maladies telles que le cancer ou des troubles neurologiques. Cette modalité se montre utile en routine clinique notamment lorsqu'il est possible d'en extraire des informations précises et fiables. Malgré les nombreuses publications sur le sujet, l'interprétation des données d'ISRM est toujours un problème difficile en raison de différents facteurs tels que le faible rapport signal sur bruit des signaux, le chevauchement des raies spectrales ou la présence de signaux de nuisance. Cette thèse aborde le problème de l'interprétation des données d'ISRM et la caractérisation de la rechute des patients souffrant de tumeurs cérébrales. Ces objectifs sont abordés à travers une approche méthodologique intégrant des connaissances a priori sur les données d'ISRM avec une régularisation spatio-spectrale. Concernant le cadre applicatif, cette thèse contribue à l'intégration de l'ISRM dans le workflow de traitement en radiothérapie dans le cadre du projet européen SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) financé par la Commission européenne (FP7-PEOPLE-ITN)The aim of this thesis is to propose new algorithms to overcome the current limitations and to address the open challenges in the processing of magnetic resonance spectroscopic imaging (MRSI) data. MRSI is a non-invasive modality able to provide the spatial distribution of relevant biochemical compounds (metabolites) commonly used as biomarkers of disease. Information provided by MRSI can be used as a valuable insight for the diagnosis, treatment and follow-up of several diseases such as cancer or neurological disorders. Obtaining accurate and reliable information from in vivo MRSI signals is a crucial requirement for the clinical utility of this technique. Despite the numerous publications on the topic, the interpretation of MRSI data is still a challenging problem due to different factors such as the low signal-to-noise ratio (SNR) of the signals, the overlap of spectral lines or the presence of nuisance components. This thesis addresses the problem of interpreting MRSI data and characterizing recurrence in tumor brain patients. These objectives are addressed through a methodological approach based on novel processing methods that incorporate prior knowledge on the MRSI data using a spatio-spectral regularization. As an application, the thesis addresses the integration of MRSI into the radiotherapy treatment workflow within the context of the European project SUMMER (Software for the Use of Multi-Modality images in External Radiotherapy) founded by the European Commission (FP7-PEOPLE-ITN framework)
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Remes, J. (Jukka). "Method evaluations in spatial exploratory analyses of resting-state functional magnetic resonance imaging data." Doctoral thesis, Oulun yliopisto, 2013. http://urn.fi/urn:isbn:9789526202228.
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Abstract Resting-state (RS) measurements during functional magnetic resonance imaging (fMRI) have become an established approach for studying spontaneous brain activity. RS-fMRI results are often obtained using explorative approaches like spatial independent component analysis (sICA). These approaches and their software implementations are rarely evaluated extensively or specifically concerning RS-fMRI. Trust is placed in the software that they will work according to the published method descriptions. Many methods and parameters are used despite the lack of test data, and the validity of the underlying models remains an open question. A substantially greater number of evaluations would be needed to ensure the quality of exploratory RS-fMRI analyses. This thesis investigates the applicability of sICA methodology and software in the RS-fMRI context. The experiences were used to formulate general guidelines to facilitate future method evaluations. Additionally, a novel multiple comparison correction (MCC) method, Maxmad, was devised for adjusting evaluation results statistically. With regard to software considerations, the source code of FSL Melodic, popular sICA software, was analyzed against its published method descriptions. Unreported and unevaluated details were found, which implies that one should not automatically assume a correspondence between the literature and the software implementations. The method implementations should rather be subjected to independent reviews. An experimental contribution of this thesis is that the credibility of the emerging sliding window sICAs has been improved by the validation of sICA related preprocessing procedures. In addition to that, the estimation accuracy regarding the results in existing RS-fMRI sICA literature was also shown not to suffer even though repeatability tools like Icasso have not been used in their computation. Furthermore, the evidence against conventional sICA model suggests the consideration of different approaches to analysis of RS-fMRI. The guidelines developed for facilitation of evaluations include adoption of 1) open software development (improved error detection), 2) modular software designs (easier evaluations), 3) data specific evaluations (increased validity), and 4) extensive coverage of parameter space (improved credibility). The proposed Maxmad MCC addresses a statistical problem arising from broad evaluations. Large scale cooperation efforts are proposed concerning evaluations in order to improve the credibility of exploratory RS-fMRI methodsTiivistelmä Aivoista toiminnallisella magneettikuvantamisella (engl. functional magnetic resonance imaging, fMRI) lepotilassa tehdyt mittaukset ovat saaneet vakiintuneen aseman spontaanin aivotoiminnan tutkimuksessa. Lepotilan fMRI:n tulokset saadaan usein käyttämällä exploratiivisia menetelmiä, kuten spatiaalista itsenäisten komponenttien analyysia (engl. spatial independent component analysis, sICA). Näitä menetelmiä ja niiden ohjelmistototeutuksia evaluoidaan harvoin kattavasti tai erityisesti lepotilan fMRI:n kannalta. Ohjelmistojen luotetaan toimivan menetelmäkuvausten mukaisesti. Monia menetelmiä ja parametreja käytetään testidatan puuttumisesta huolimatta, ja myös menetelmien taustalla olevien mallien pätevyys on edelleen epäselvä asia. Eksploratiivisten lepotilan fMRI-datan analyysien laadun varmistamiseksi tarvittaisiin huomattavasti nykyistä suurempi määrä evaluaatioita. Tämä väitöskirja tutki sICA-menetelmien ja -ohjelmistojen soveltuvuutta lepotilan fMRI-tutkimuksiin. Kokemuksien perusteella luotiin yleisiä ohjenuoria helpottamaan tulevaisuuden menetelmäevaluaatioita. Lisäksi väitöskirjassa kehitettiin uusi monivertailukorjausmenetelmä, Maxmad, evaluaatiotulosten tilastolliseen korjaukseen. Tunnetun sICA-ohjelmiston, FSL Melodicin, lähdekoodi analysoitiin suhteessa julkaistuihin menetelmäkuvauksiin. Analyysissa ilmeni aiemmin raportoimattomia ja evaluoimattomia menetelmäyksityiskohtia, mikä tarkoittaa, ettei kirjallisuudessa olevien menetelmäkuvausten ja niiden ohjelmistototeutusten välille pitäisi automaattisesti olettaa vastaavuutta. Menetelmätoteutukset pitäisi katselmoida riippumattomasti. Väitöskirjan kokeellisena panoksena parannettiin liukuvassa ikkunassa suoritettavan sICA:n uskottavuutta varmistamalla sICA:n esikäsittelyjen oikeellisuus. Lisäksi väitöskirjassa näytettiin, että aiempien sICA-tulosten tarkkuus ei ole kärsinyt, vaikka niiden estimoinnissa ei ole käytetty toistettavuustyökaluja, kuten Icasso-ohjelmistoa. Väitöskirjan tulokset kyseenalaistavat myös perinteisen sICA-mallin, minkä vuoksi tulisi harkita siitä poikkeavia lähtökohtia lepotilan fMRI-datan analyysiin. Evaluaatioiden helpottamiseksi kehitetyt ohjeet sisältävät seuraavat periaatteet: 1) avoin ohjelmistokehitys (parantunut virheiden havaitseminen), 2) modulaarinen ohjelmistosuunnittelu (nykyistä helpommin toteutettavat evaluaatiot), 3) datatyyppikohtaiset evaluaatiot (parantunut validiteetti) ja 4) parametriavaruuden laaja kattavuus evaluaatioissa (parantunut uskottavuus). Ehdotettu Maxmad-monivertailukorjaus tarjoaa ratkaisuvaihtoehdon laajojen evaluaatioiden tilastollisiin haasteisiin. Jotta lepotilan fMRI:ssä käytettävien exploratiivisten menetelmien uskottavuus paranisi, väitöskirjassa ehdotetaan laaja-alaista yhteistyötä menetelmien evaluoimiseksi
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McLeish, Kate. "Combining data acquisition and post-processing techniques for magnetic resonance imaging of moving objects." Thesis, King's College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406105.
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Lee, Jae-Min. "Characterization of spatial and temporal brain activation patterns in functional magnetic resonance imaging data." [Gainesville, Fla.] : University of Florida, 2005. http://purl.fcla.edu/fcla/etd/UFE0013024.
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Kulkarni, Praveen P. "Functional MRI data analysis techniques and strategies to map the olfactory system of a rat brain." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-011906-095333/.
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Gill, Andrew Brian. "The analysis of dynamic contrast-enhanced magnetic resonance imaging data : treatment effects, sampling rates and repeatability." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648826.
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Saber, Nikoo Rezazadeh. "CFD modelling of blood flow in the human left ventricle based on magnetic resonance imaging data." Thesis, Imperial College London, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390806.
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Meeson, Alan Charles. "Development and application of a toolbox for multivariate pattern analysis of functional magnetic resonance imaging data." Thesis, University of Birmingham, 2016. http://etheses.bham.ac.uk//id/eprint/6468/.
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The combination of functional magnetic resonance imaging (fMRI) and multivariate pattern analysis (MVPA) is a powerful method for investigating brain function, with multiple MVPA methods being applied to the task including Logistic Regression, Support Vector Machines, Neural Networks, and Gaussian Naive Bayes classifiers. Careful review of application of these methods revealed a common process used in most studies; the majority of variations occurring in the implementation choices in key sections such as feature selection or classification algorithms being employed. Thus, it is possible to develop modularised tools for performing MVPA of fMRI data which can be applied in a variety of ways through selection of appropriate components. Development of such a toolbox for use by the University of Birmingham Cognitive Neuroimaging Laboratory is described. The modular design allows for flexible application and provides a basis for development of novel methods, which is explored through implementation of a novel cross-validation method and development of a method for investigating the effects of learning on tuning of neural populations. The development process has resulted in an efficient, robust and reliable toolbox, capable of performing a pre-implemented set of standard multi-variate pattern analyses and provides a basis for further development of novel methods.
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Küstner, Thomas [Verfasser]. "Motion Correction of Positron Emission Tomography Data by a Magnetic Resonance Imaging derived Model / Thomas Küstner." München : Verlag Dr. Hut, 2018. http://d-nb.info/1153253984/34.
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Khatamian, Yasha. "Investigating the limits of temporal clustering analysis for detecting epileptic activity in functional magnetic resonance imaging data." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=96861.
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Precise localization of epileptic activity is a necessity for those patients who may benefit from resective surgery. One common localization technique, EEG functional MRI (EEG-fMRI), localizes activity in an fMRI recording by finding blood oxygen level dependent (BOLD) signal correlates to epileptic events detected in a simultaneously recorded EEG. 2D temporal clustering analysis (2D-TCA) is a relatively new fMRI-based epileptic activity localization technique that breaks BOLD activity into components based on timing, finding epileptic activity without simultaneously recorded EEG. This study evaluated the ability of 2D-TCA to detect both simulated epileptic activity and activity detected in patients using EEG-fMRI. Although it was found that 2D-TCA could effectively detect epileptic activity with certain characteristics, it also detected activity not associated with epilepsy. As such, it was determined that 2D-TCA can only be used to validate epileptic activity localization by other means or to create hypotheses as to where activity may occur.Une localisation précise de l'activité épileptique est une nécessité pour les patients qui pourraient bénéficier d'une opération résective. L'EEG-IRM fonctionnelle (EEG-IRMf) est une nouvelle technique de localisation qui localise l'activité épileptique dans un enregistrement IRMf en trouvant un signal « blood oxygen level dependent » (BOLD) qui correspond à des événements épileptiques détectés simultanément par un enregistrement EEG. L'analyse temporelle groupée 2D (ATG-2D) est une technique de localisation de l'activité épileptique relativement nouvelle, qui est basée sur des données IRMf. Pour trouver l'activité épileptique, elle décompose l'activité BOLD en différentes composantes selon le moment où elles surviennent sans recourir à un enregistrement EEG simultané. Cette étude évalue la capacité de la technique ATG-2D à détecter une activité épileptique simulée ainsi que sa capacité à détecter une activité épileptique précédemment détectée chez des patients avec EEG-IRMf. Même nous avons montré que la technique ATG-2D pouvait détecter de façon efficace une activité épileptique ayant certaines caractéristiques, il a aussi été trouvé qu'elle détectait de l'activité non épileptique. Il a été déterminé que la technique ATG-2D pouvait seulement être utilisée pour valider une activité épileptique localisée par d'autres moyens ou pour formuler des hypothèses concernant l'endroit où l'activité pourrait survenir.
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Meara, Simon Jonathan Pierpoint. "Voxelwise deformation morphology of magnetic resonance imaging data of the brain based on linear scale-space features." Thesis, King's College London (University of London), 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.406083.
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Salami, Alireza. "Decoding the complex brain : multivariate and multimodal analyses of neuroimaging data." Doctoral thesis, Umeå universitet, Institutionen för integrativ medicinsk biologi (IMB), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51842.
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Functional brain images are extraordinarily rich data sets that reveal distributed brain networks engaged in a wide variety of cognitive operations. It is a substantial challenge both to create models of cognition that mimic behavior and underlying cognitive processes and to choose a suitable analytic method to identify underlying brain networks. Most of the contemporary techniques used in analyses of functional neuroimaging data are based on univariate approaches in which single image elements (i.e. voxels) are considered to be computationally independent measures. Beyond univariate methods (e.g. statistical parametric mapping), multivariate approaches, which identify a network across all regions of the brain rather than a tessellation of regions, are potentially well suited for analyses of brain imaging data. A multivariate method (e.g. partial least squares) is a computational strategy that determines time-varying distributed patterns of the brain (as a function of a cognitive task). Compared to its univariate counterparts, a multivariate approach provides greater levels of sensitivity and reflects cooperative interactions among brain regions. Thus, by considering information across more than one measuring point, additional information on brain function can be revealed. Similarly, by considering information across more than one measuring technique, the nature of underlying cognitive processes become well-understood. Cognitive processes have been investigated in conjunction with multiple neuroimaging modalities (e.g. fMRI, sMRI, EEG, DTI), whereas the typical method has been to analyze each modality separately. Accordingly, little work has been carried out to examine the relation between different modalities. Indeed, due to the interconnected nature of brain processing, it is plausible that changes in one modality locally or distally modulate changes in another modality. This thesis focuses on multivariate and multimodal methods of image analysis applied to various cognitive questions. These methods are used in order to extract features that are inaccessible using univariate / unimodal analytic approaches. To this end, I implemented multivariate partial least squares analysis in study I and II in order to identify neural commonalities and differences between the available and accessible information in memory (study I), and also between episodic encoding and episodic retrieval (study II). Study I provided evidence of a qualitative differences between availability and accessibility signals in memory by linking memory access to modality-independent brain regions, and availability in memory to elevated activity in modality-specific brain regions. Study II provided evidence in support of general and specific memory operations during encoding and retrieval by linking general processes to the joint demands on attentional, executive, and strategic processing, and a process-specific network to core episodic memory function. In study II, III, and IV, I explored whether the age-related changes/differences in one modality were driven by age-related changes/differences in another modality. To this end, study II investigated whether age-related functional differences in hippocampus during an episodic memory task could be accounted for by age-related structural differences. I found that age-related local structural deterioration could partially but not entirely account for age-related diminished hippocampal activation. In study III, I sought to explore whether age-related changes in the prefrontal and occipital cortex during a semantic memory task were driven by local and/or distal gray matter loss. I found that age-related diminished prefrontal activation was driven, at least in part, by local gray matter atrophy, whereas the age-related decline in occipital cortex was accounted for by distal gray matter atrophy. Finally, in study IV, I investigated whether white matter (WM) microstructural differences mediated age-related decline in different cognitive domains. The findings implicated WM as one source of age-related decline on tasks measuring processing speed, but they did not support the view that age-related differences in episodic memory, visuospatial ability, or fluency were strongly driven by age-related differences in white-matter pathways. Taken together, the architecture of different aspects of episodic memory (e.g. encoding vs. retrieval; availability vs. accessibility) was characterized using a multivariate partial least squares. This finding highlights usefulness of multivariate techniques in guiding cognitive theories of episodic memory. Additionally, competing theories of cognitive aging were investigated by multimodal integration of age-related changes in brain structure, function, and behavior. The structure-function relationships were specific to brain regions and cognitive domains. Finally, we urged that contemporary theories on cognitive aging need to be extended to longitudinal measures to be further validated.
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Borkowetz, Angelika, Ivan Platzek, Marieta Toma, Theresa Renner, Roman Herout, Martin Baunacke, Michael Laniado, et al. "Evaluation of Prostate Imaging Reporting and Data System Classification in the Prediction of Tumor Aggressiveness in Targeted Magnetic Resonance Imaging/Ultrasound-Fusion Biopsy." Karger, 2017. https://tud.qucosa.de/id/qucosa%3A70625.
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Objectives: The study aimed to evaluate the prediction of Prostate Imaging Reporting and Data System (PI-RADS) with respect to the prostate cancer (PCa) detection rate and tumor aggressiveness in magnetic resonance imaging (MRI)/ultrasound-fusion-biopsy (fusPbx) and in systematic biopsy (sysPbx). Materials and Methods: Six hundred and twenty five patients undergoing multiparametric MRI were investigated. MRI findings were classified using PI-RADS v1 or v2. All patients underwent fusPbx combined with sysPbx (comPbx). The lesion with the highest PI-RADS was defined as maximum PI-RADS (maxPI-RADS). Gleason Score ≥ 7 (3 + 4) was defined as significant PCa. Results: The overall PCa detection rate was 51% ( n = 321; 39% significant PCa). The detection rate was 43% in fusPbx ( n = 267; 34% significant PCa) and 36% in sysPbx ( n = 223; 27% significant PCa). Nine percentage of significant PCa were detected by sysPbx alone. A total of 1,162 lesions were investigated. The detection rate of significant PCa in lesions with PI-RADS 2, 3, 4, and 5 were 9% (18/206), 12% (56/450), 27% (98/358), and 61% (90/148) respectively. maxPI-RADS ≥ 4 was the strongest predictor for the detection of significant PCa in comPbx (OR 2.77; 95% CI 1.81–4.24; p < 0.005). Conclusions: maxPI-RADS is the strongest predictor for the detection of significant PCa in comPbx. Due to a high detection rate of additional significant PCa in sysPbx, fusPbx should still be combined with sysPbx.
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Neumann, Markus. "Automatic multimodal real-time tracking for image plane alignment in interventional Magnetic Resonance Imaging." Phd thesis, Université de Strasbourg, 2014. http://tel.archives-ouvertes.fr/tel-01038023.
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Interventional magnetic resonance imaging (MRI) aims at performing minimally invasive percutaneous interventions, such as tumor ablations and biopsies, under MRI guidance. During such interventions, the acquired MR image planes are typically aligned to the surgical instrument (needle) axis and to surrounding anatomical structures of interest in order to efficiently monitor the advancement in real-time of the instrument inside the patient's body. Object tracking inside the MRI is expected to facilitate and accelerate MR-guided interventions by allowing to automatically align the image planes to the surgical instrument. In this PhD thesis, an image-based workflow is proposed and refined for automatic image plane alignment. An automatic tracking workflow was developed, performing detection and tracking of a passive marker directly in clinical real-time images. This tracking workflow is designed for fully automated image plane alignment, with minimization of tracking-dedicated time. Its main drawback is its inherent dependence on the slow clinical MRI update rate. First, the addition of motion estimation and prediction with a Kalman filter was investigated and improved the workflow tracking performance. Second, a complementary optical sensor was used for multi-sensor tracking in order to decouple the tracking update rate from the MR image acquisition rate. Performance of the workflow was evaluated with both computer simulations and experiments using an MR compatible testbed. Results show a high robustness of the multi-sensor tracking approach for dynamic image plane alignment, due to the combination of the individual strengths of each sensor.
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Prucka, William R. "Wavelet-based regression and classification for longitudinal diffusion tensor imaging data." Thesis, Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2008p/prucka.pdf.
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Lindemeyer, Johannes [Verfasser], Nadim Joni Akademischer Betreuer] Shah, and Achim [Akademischer Betreuer] [Stahl. "Optimisation of Phase Data Processing for Susceptibility Reconstruction in Magnetic Resonance Imaging / Johannes Lindemeyer ; Nadim Joni Shah, Achim Stahl." Aachen : Universitätsbibliothek der RWTH Aachen, 2015. http://d-nb.info/1128316560/34.
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Lindemeyer, Johannes Verfasser], Nadim Joni [Akademischer Betreuer] Shah, and Achim [Akademischer Betreuer] [Stahl. "Optimisation of Phase Data Processing for Susceptibility Reconstruction in Magnetic Resonance Imaging / Johannes Lindemeyer ; Nadim Joni Shah, Achim Stahl." Aachen : Universitätsbibliothek der RWTH Aachen, 2015. http://d-nb.info/1128316560/34.
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Burrell, Lauren S. "Feature analysis of functional mri data for mapping epileptic networks." Diss., Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/26528.
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This research focused on the development of a methodology for analyzing functional magnetic resonance imaging (fMRI) data collected from patients with epilepsy in order to map epileptic networks. Epilepsy, a chronic neurological disorder characterized by recurrent, unprovoked seizures, affects up to 1% of the world's population. Antiepileptic drug therapies either do not successfully control seizures or have unacceptable side effects in over 30% of patients. Approximately one-third of patients whose seizures cannot be controlled by medication are candidates for surgical removal of the affected area of the brain, potentially rendering them seizure free. Accurate localization of the epileptogenic focus, i.e., the area of seizure onset, is critical for the best surgical outcome. The main objective of the research was to develop a set of fMRI data features that could be used to distinguish between normal brain tissue and the epileptic focus. To determine the best combination of features from various domains for mapping the focus, genetic programming and several feature selection methods were employed. These composite features and feature sets were subsequently used to train a classifier capable of discriminating between the two classes of voxels. The classifier was then applied to a separate testing set in order to generate maps showing brain voxels labeled as either normal or epileptogenic based on the best feature or set of features. It should be noted that although this work focuses on the application of fMRI analysis to epilepsy data, similar techniques could be used when studying brain activations due to other sources. In addition to investigating in vivo data collected from temporal lobe epilepsy patients with uncertain epileptic foci, phantom (simulated) data were created and processed to provide quantitative measures of the efficacy of the techniques.
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Abufadel, Amer Y. "4D Segmentation of Cardiac MRI Data Using Active Surfaces with Spatiotemporal Shape Priors." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/14005.
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This dissertation presents a fully automatic segmentation algorithm for cardiac MR data. Some of the currently published methods are
automatic, but they only work well in 2D and sometimes in 3D and do not perform well near the extremities (apex and base) of the heart.
Additionally, they require substantial user input to make them feasible for use in a clinical environment. This dissertation introduces
novel approaches to improve the accuracy, robustness, and consistency of existing methods.
Segmentation accuracy can be improved by knowing as much about the data as possible. Accordingly, we compute a single 4D active surface
that performs segmentation in space and time simultaneously. The segmentation routine can now take advantage of information from
neighboring pixels that can be adjacent either spatially or temporally.
Robustness is improved further by using confidence labels on shape priors. Shape priors are deduced from manual
segmentation of training data. This data may contain imperfections that may impede proper manual segmentation. Confidence
labels indicate the level of fidelity of the manual segmentation to the actual data. The contribution of regions with low
confidence levels can be attenuated or excluded from the final result.
The specific advantages of using the 4D segmentation along with shape priors and regions of confidence are highlighted throughout the
thesis dissertation. Performance of the new method is measured by comparing the results to traditional 3D segmentation and to manual
segmentation performed by a trained clinician.
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Klemens, Fabian [Verfasser], and G. [Akademischer Betreuer] Thäter. "Combining computational fluid dynamics and magnetic resonance imaging data using lattice Boltzmann based topology optimisation / Fabian Klemens ; Betreuer: G. Thäter." Karlsruhe : KIT-Bibliothek, 2020. http://d-nb.info/1221186922/34.
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Knight, Caroline L. "Antenatal characterisation and postnatal validation of fetal nutritional status using novel fetal imaging methods, neonatal body composition data, and anthropometry." Thesis, University of Oxford, 2014. https://ora.ox.ac.uk/objects/uuid:11107be6-35ea-4d6d-9161-ddee4cde1b3e.
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Background: Fetal growth restricted (FGR) infants have increased perinatal morbidity and mortality risks. Standard fetal biometry may identify some FGR babies; amniotic fluid measurement and Doppler assessment of blood vessels provide additional, functional assessments, but are often normal in babies with late-onset FGR who are difficult to diagnose. A marker reflecting nutritional status should help to identify FGR fetuses, enabling effective intervention: increased monitoring or delivery and neonatal management. Arm and/or thigh measurements have previously shown potential in 2D and 3D. Limb fat volume has never been measured and could provide an in utero marker of fetal nutritional status. Aims: 1. To develop an ultrasound scanning protocol to acquire 2D images and 3D volumes of fetal arms and thighs. 2. To develop method(s), suitable for use in clinical practice, to measure fat in these images and volumes. 3. To assess the reproducibility of these methods. 4. To assess the validity of these methods by comparing them with MRI images of fetal limb fat. 5. To use these methods in a healthy population to develop reference ranges. 6. To correlate these methods with validated neonatal measurements, to assess whether the antenatal methods reflect neonatal body composition. 7. To apply these methods to longitudinal prospective ultrasound images from multiple countries to assess SGA fetuses. Method: Ultrasound imaging protocols were developed to obtain accurate images and volumes of fetal arms and thighs. Segmentation tools were designed with biomedical engineers to measure fat, lean and limb compartments, and applied to 2D images and 3D volumes (n=500), with calculation of reference centiles in optimally healthy women (INTERGROWTH-21st study) and MRI validation of the ultrasound measurements. Additional methods were assessed: a two-ellipse method, and a three-thickness Fetal Fat Index (FFI). Reproducibility was assessed using Bland-Altman plots and ICCs. Fetal measurements were correlated with neonatal body composition data and anthropometry. Third trimester fetal thigh fat volumes were compared in sub-cohorts of AGA and SGA fetuses. Results: Reference centiles were calculated for novel fractional arm and leg volumes (fat and lean), from 16 to 41 weeks. 2D reference ranges were also calculated. The FFI technique - quick, simple, 2D - correlated well with fat area and fat volume. DXA analyses showed a strong correlation between neonatal limb and whole body fat. Correlation analyses showed that infants with above- and below-average arm circumferences have significantly different amounts of arm fat as early as 30-35 weeks. The strength of correlation between antenatal limb fat and neonatal PEA POD whole body fat increased with increasing gestational age. Scans at 30-34 weeks showed a significant difference in fractional thigh fat between those who would be born SGA compared with AGA. Conclusion: This thesis explores, in detail, the measurement of fetal arm and thigh fat using 2D and 3D ultrasound, and demonstrates that it is correlated to neonatal body composition thus allowing 'fetal body composition' to be established as a research tool; the ultimate aim is to be able to distinguish growth-restricted fetuses from those of normal nutritional status. Novel measurements have been developed, acquisition protocols described, reproducibility assessed, and reference centiles calculated in an optimally healthy population: 2D Fetal Fat Index, 3D fractional limb fat and lean volumes, and limb fat (2D and 3D) as a percentage.
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Douros, Ioannis. "Towards a 3 dimensional dynamic generic speaker model to study geometry simplifications of the vocal tract using magnetic resonance imaging data." Electronic Thesis or Diss., Université de Lorraine, 2020. http://www.theses.fr/2020LORR0115.
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Dans cette thèse, nous avons utilisé les données de l’IRM du conduit vocal pour étudier la production de la parole. La première partie consiste en l’étude de l’impact que le vélum, l’épiglotte et la position de la tête a sur la phonation de cinq voyelles françaises. Des simulations acoustiques ont été utilisées pour comparer les formants des cas étudiés avec la référence afin de mesurer leur impact. Pour cette partie du travail, nous avons utilisé des IRM statiques en 3D. Comme la parole est généralement une phénomène dynamique une question s’est posée, à savoir s’il serait possible de traiter les données 3D afin d’incorporer des informations temporelles de la parole continue. Par conséquent, la deuxième partie présente quelques algorithmes que l’on peut utiliser pour améliorer les données de production de la parole. Plusieurs transformations d’images ont été combinées afin de générer des estimations des formes du conduit vocal qui sont plus informatives que les originales. À ce stade, nous avons envisagé, outre l’amélioration des données de production de la parole, de créer un modèle de référence générique qui pourrait fournir des informations améliorées non pas pour un sujet spécifique, mais globalement pour la parole. C’est pourquoi nous avons consacré la troisième partie l’étude d’un algorithme permettant de créer un atlas spatio-temporel de l’appareil vocal qui peut être utilisé comme référence ou standard pour l’étude de la parole car il est indépendant du locuteur. Enfin, la dernière partie de la thèse, fait référence à une sélection de questions ouvertes du domaine qui restent encore sans réponse, quelques pistes intéressantes que l’on peut développer à partir de cette thèse et quelques approches potentielles qui pourraient être envisager afin de répondre à ces questionsIn this thesis we used MRI (Magnetic Resonance Imaging) data of the vocal tract to study speech production. The first part consist of the study of the impact that the velum, the epiglottis and the head position has on the phonation of five french vowels. Acoustic simulations were used to compare the formants of the studied cases with the reference in order to measure their impact. For this part of the work, we used 3D static MR (Magnetic Resonance) images. As speech is usually a dynamic phenomenon, a question arose, whether it would be possible to process the 3D data in order to incorporate dynamic information of continuous speech. Therefore the second part presents some algorithms that one can use in order to enhance speech production data. Several image transformations were combined in order to generate estimations of vocal tract shapes which are more informative than the original ones. At this point, we envisaged apart from enhancing speech production data, to create a generic speaker model that could provide enhanced information not for a specific subject, but globally for speech. As a result, we devoted the third part in the investigation of an algorithm that one can use to create a spatiotemporal atlas of the vocal tract which can be used as a reference or standard speaker for speech studies as it is speaker independent. Finally, the last part of the thesis, refers to a selection of open questions of the field that are still left unanswered, some interesting directions that one can expand this thesis and some potential approaches that could help someone move forward towards these directions
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Lee, Thomas Seward. "Software-based gradient nonlinearity distortion correction." CSUSB ScholarWorks, 2006. https://scholarworks.lib.csusb.edu/etd-project/3180.
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The primary purpose of the thesis is to discuss the use of Magnetic Resonance Imaging (MRI) in functional proton radiosurgery. The methods presented in this thesis were specifically designed to correct gradient nonlinearity distortion, the single greatest hurdle that limits the deployment of MRI-based functional proton radiosurgery systems. The new system central in the thesis fully utilized MRI to provide localization of anatomical targets with submillimeter accuracy. The thesis provides analysis and solutions to the problems related to gradient nonlinearity distortion. The characteristics of proton radiosurgery are introduced, in addition to a discussion of its advantages over other current methods of radiation oncology. A historical background for proton radiosurgery is also presented, along with a description of its implementation at Loma Linda University Medical Center (LLUMC), where a new system for functional proton radiosurgery has been proposed and is currently under development.
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Sarlls, Joelle Elita. "High-resolution Diffusion-weighted Magnetic Resonance Imaging: Development and Application of Novel Radial Fast Spin-echo Acquisitions." Diss., Tucson, Arizona : University of Arizona, 2006. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1704%5F1%5Fm.pdf&type=application/pdf.
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