Thematische Bibliographien / Artefakty fMRI

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Auswahl der wissenschaftlichen Literatur zum Thema „Artefakty fMRI“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 17. Februar 2022

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Zeitschriftenartikel zum Thema "Artefakty fMRI"

Chowdhury, Muhammad E. H., Karen J. Mullinger, Paul Glover und Richard Bowtell. „Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI“. NeuroImage 84 (Januar 2014): 307–19. http://dx.doi.org/10.1016/j.neuroimage.2013.08.039.

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Spencer, Glyn S., James A. Smith, Muhammad E. H. Chowdhury, Richard Bowtell und Karen J. Mullinger. „Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction“. NeuroImage 173 (Juni 2018): 188–98. http://dx.doi.org/10.1016/j.neuroimage.2018.02.034.

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Leclercq, Yves, Jessica Schrouff, Quentin Noirhomme, Pierre Maquet und Christophe Phillips. „fMRI Artefact Rejection and Sleep Scoring Toolbox“. Computational Intelligence and Neuroscience 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/598206.

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We started writing the “fMRI artefact rejection and sleep scoring toolbox”, or “FAST”, to process our sleep EEG-fMRI data, that is, the simultaneous recording of electroencephalographic and functional magnetic resonance imaging data acquired while a subject is asleep. FAST tackles three crucial issues typical of this kind of data: (1) data manipulation (viewing, comparing, chunking, etc.) of long continuous M/EEG recordings, (2) rejection of the fMRI-induced artefact in the EEG signal, and (3) manual sleep-scoring of the M/EEG recording. Currently, the toolbox can efficiently deal with these issues via a GUI, SPM8 batching system or hand-written script. The tools developed are, of course, also useful for other EEG applications, for example, involving simultaneous EEG-fMRI acquisition, continuous EEG eye-balling, and manipulation. Even though the toolbox was originally devised for EEG data, it will also gracefully handle MEG data without any problem. “FAST” is developed in Matlab as an add-on toolbox for SPM8 and, therefore, internally uses its SPM8-meeg data format. “FAST” is available for free, under the GNU-GPL.

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Ferreira, José L., Yan Wu, René M. H. Besseling, Rolf Lamerichs und Ronald M. Aarts. „Gradient Artefact Correction and Evaluation of the EEG Recorded Simultaneously with fMRI Data Using Optimised Moving-Average“. Journal of Medical Engineering 2016 (28.06.2016): 1–17. http://dx.doi.org/10.1155/2016/9614323.

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Over the past years, coregistered EEG-fMRI has emerged as a powerful tool for neurocognitive research and correlated studies, mainly because of the possibility of integrating the high temporal resolution of the EEG with the high spatial resolution of fMRI. However, additional work remains to be done in order to improve the quality of the EEG signal recorded simultaneously with fMRI data, in particular regarding the occurrence of the gradient artefact. We devised and presented in this paper a novel approach for gradient artefact correction based upon optimised moving-average filtering (OMA). OMA makes use of the iterative application of a moving-average filter, which allows estimation and cancellation of the gradient artefact by integration. Additionally, OMA is capable of performing the attenuation of the periodic artefact activity without accurate information about MRI triggers. By using our proposed approach, it is possible to achieve a better balance than the slice-average subtraction as performed by the established AAS method, regarding EEG signal preservation together with effective suppression of the gradient artefact. Since the stochastic nature of the EEG signal complicates the assessment of EEG preservation after application of the gradient artefact correction, we also propose a simple and effective method to account for it.

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Yan, Winston X., Karen J. Mullinger, Matt J. Brookes und Richard Bowtell. „Understanding gradient artefacts in simultaneous EEG/fMRI“. NeuroImage 46, Nr. 2 (Juni 2009): 459–71. http://dx.doi.org/10.1016/j.neuroimage.2009.01.029.

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Beckmann, C. F., J. A. Noble und S. M. Smith. „Artefact detection in FMRI data using independent component analysis“. NeuroImage 11, Nr. 5 (Mai 2000): S614. http://dx.doi.org/10.1016/s1053-8119(00)91544-1.

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Kasper, L., S. Marti, SJ Vannesjö, C. Hutton, R. Dolan, N. Weiskopf, KP Prüssmann und KE Stephan. „Cardiac artefact correction for human brainstem fMRI at 7T“. NeuroImage 47 (Juli 2009): S100. http://dx.doi.org/10.1016/s1053-8119(09)70854-7.

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Chowdhury, Muhammad E. H., Karen J. Mullinger, Paul Glover und Richard Bowtell. „Corrigendum to “Reference layer artefact subtraction (RLAS): A novel method of minimizing EEG artefacts during simultaneous fMRI” [Neuroimage 84 (2014) 307–319]“. NeuroImage 98 (September 2014): 547. http://dx.doi.org/10.1016/j.neuroimage.2014.04.034.

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Bungert, Andreas, Christopher D. Chambers, Mark Phillips und C. John Evans. „Reducing image artefacts in concurrent TMS/fMRI by passive shimming“. NeuroImage 59, Nr. 3 (Februar 2012): 2167–74. http://dx.doi.org/10.1016/j.neuroimage.2011.10.013.

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Paasonen, Jaakko, Hanne Laakso, Tiina Pirttimäki, Petteri Stenroos, Raimo A. Salo, Ekaterina Zhurakovskaya, Lauri J. Lehto et al. „Multi-band SWIFT enables quiet and artefact-free EEG-fMRI and awake fMRI studies in rat“. NeuroImage 206 (Februar 2020): 116338. http://dx.doi.org/10.1016/j.neuroimage.2019.116338.

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Dissertationen zum Thema "Artefakty fMRI"

Nováková, Marie. „Mapování pohybových artefaktů ve fMRI“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220039.

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This thesis summarizes a theory of magnetic resonance and the method of functional magnetic resonance. It is focused on the influence of motion artifacts and image preprocessing methods, especially realign. It deals with the possibility of using movement parameters obtained in the process of alignment of functional scans to create maps that show the expression of motion artifacts. In this thesis, three different methods were designed, implemented a tested. These methods lead to the creation of probability, power and statistical group maps showing areas typically affected by movement artifacts.

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Pospíšil, Jan. „Potlačení šumu a artefaktů ve fMRI datech s využitím analýzy nezávislých komponent a multi-echo dat“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442586.

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The main task of this work is to design an algorithm for suppressing unwanted noise and artifacts in fMRI data using the analysis of independent components and multi-echo data. The theoretical part deals with the basic principles of magnetic resonance, including construction and image data processing. The practical part presents a pilot design of a method inspired by a professional publication in the Matlab software environment, where this design is subsequently tested on real fMRI data provided by the Laboratory of Multimodal and Functional Imaging, CEITEC MU.

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Šejnoha, Radim. „Nástroj pro analýzu pohybu subjektů při měření funkční magnetickou rezonancí“. Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-242165.

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This diploma thesis deals with an analysis of subject’s movement during measurements with funcional magnetic resonance imaging (fMRI). It focuses on methods of a movement artifacts detection and their removal in fMRI images. Thesis deals with metrics which are used for the movement rate of measured subjects evaluation. Metrics and a correction of movement are implemented into the programme in MATLAB. Comparison of subjects suffering from Parkinson’s disease with a group of healthy control was carried out. Tresholds of individual metrics were suggested and a criterion for the removal of subjects with high movement rate was determined.

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Spencer, G. S. „EEG-fMRI : novel methods for gradient artefact correction“. Thesis, University of Nottingham, 2015. http://eprints.nottingham.ac.uk/29370/.

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The general aim of the work detailed in this thesis is to improve the quality of electroencepholography (EEG) recordings acquired simultaneously with functional magnetic resonance imaging (fMRI) data. Simultaneous EEG-fMRI recordings offer significant advantages over the isolated use of each modality for measuring brain function. The high temporal resolution associated with EEG complements the high spatial resolution provided by fMRI. However, combining the two modalities can have significant effects on the overall data quality. The gradient artefact (GA), which is induced on the EEG cables by the time varying magnetic fields associated with fMRI sequences, can be particularly problematic to correct for in experiments containing any subject movement. In this thesis, two novel, movement-invariant methods are introduced for correcting the GA. The first method is named the gradient model fit (GMF) and relies upon the assumption that the GA can be modelled as a linear combination of basis components, where the relative weighting of each component varies dependent upon subject position. By modelling these underlying components, it is possible to characterise and remove the GA, which is particularly beneficial in the presence of subject movement. The second method named the difference model subtraction (DMS) relies on the assumption that the GA varies linearly for small changes in subject position. By modelling the change in GA for a basis set of likely head movements, it was shown to be possible to combine DMS with standard GA correction methods to improve the attenuation of the GA for data acquired during subject movement. Both methods showed a significant improvement over the existing GA correction techniques, particularly for experiments containing subject movement. These methods are therefore relevant to any experimenter interested in working with subject groups such as children or patients where movement is likely to occur.

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Chowdhury, Muhammad Enamul Hoque. „Simultaneous EEG-fMRI : novel methods for EEG artefacts reduction at source“. Thesis, University of Nottingham, 2014. http://eprints.nottingham.ac.uk/14297/.

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This thesis describes the development and application of novel techniques to reduce the EEG artefacts at source during the simultaneous acquisition of EEG and fMRI data. The work described in this thesis was carried out by the author in the Sir Peter Mansfield Magnetic Resonance Centre, School of Physics & Astronomy at the University of Nottingham, between October 2010 and January 2013. Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after correction, which can easily swamp signals from brain activity. Therefore any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, and facilitating improved detection of brain activity. This thesis firstly explores a new method for reducing the gradient artefact (GA), which is induced in EEG data recorded during concurrent MRI, by investigating the effects of the cable configuration on the characteristics of the GA. This work showed that the GA amplitude and its sensitivity to movement of the cabling is reduced by minimising wire loop areas in the cabling between the EEG cap and amplifier. Another novel approach for reducing the magnitude and variability of the artefacts is the use of an EEG cap that incorporates electrodes embedded in a reference layer, which has a similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads are theoretically similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Therefore taking the difference of the voltages in the reference and scalp channels should reduce the artefacts, without affecting sensitivity to neuronal signals. The theoretical efficacy of artefact correction that can be achieved by using this new reference layer artefact subtraction (RLAS) method was investigated. This was done through separate electromagnetic simulations of the artefacts induced in a hemispherical reference layer and a spherical volume conductor in a time-varying magnetic field and the results showed that similar artefacts are induced on the surface of both conductors. Simulations are also performed to find the optimal design for an RLAS system, by varying the geometry of the system. A simple experimental realisation of the RLAS system was implemented to investigate the degree of artefact attenuation that can be achieved via RLAS. Through a series of experiments on phantoms and human subjects, it is shown here that RLAS significantly reduces the GA, pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms the standard artefact correction method, average artefact subtraction (AAS), in the removal of the GA and PA when motion is present, while the combination of RLAS and AAS always produces higher artefact attenuation than AAS alone. Additionally, this work demonstrates that RLAS greatly attenuates the unpredictable and highly variable MA that are very hard to remove using post-processing methods.

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Del, Castello Mariangela. „Analysis of electroencephalography signals collected in a magnetic resonance environment: characterisation of the ballistocardiographic artefact“. Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13214/.

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L’acquisizione simultanea di segnali elettroencefalografici (EEG) e immagini di risonanza magnetica funzionale (fMRI) permette di investigare attivazioni cerebrali in modo non invasivo. La presenza del campo magnetico altera però in modo non trascurabile la qualità dei segnali EEG acquisiti. In particolare due artefatti sono stati individuati: l’artefatto da gradiente e l’artefatto da ballistocardiogramma (BCG). L’artefatto da BCG è legato all’attività cardiaca del soggetto, ed è caratterizzato da elevata variabilità tra un’occorrenza e l’altra in termini di ampiezza, forma d’onda e durata dell’artefatto. Differenti algoritmi sono stati implementati al fine di rimuoverlo, ma la rimozione completa rimane ancora un difficile obiettivo da raggiungere a causa della sua complessa natura. L’argomento della tesi riguarda l’analisi di segnali EEG acquisiti in ambiente di risonanza magnetica e la caratterizzazione dell’artefatto BCG. L’obiettivo è individuare ulteriori caratteristiche dell’artefatto che possano condurre al miglioramento dei precedenti metodi, o all’implementazione di nuovi. Con questa tesi abbiamo mostrato quali sono i motivi che causano la presenza di residui artefattuali nei segnali EEG processati con i metodi presenti in letteratura. Attraverso analisi statistica abbiamo riscontrato che occorrenze dell’artefatto BCG sono caratterizzate da un ritardo variabile rispetto al picco R sull’ECG, che nella nostra analisi rappresenta l’evento di riferimento nell’attività cardiaca. Abbiamo inoltre trovato che il ritardo R-BCG varia con la frequenza cardiaca. Le successive valutazioni riguardano i maggiori contributi all’artefatto BCG. Attraverso l’analisi alle componenti principali, sono stati individuati due contributi legati al fluire del sangue dal cuore verso il cervello e alla sua pulsatilità nei vasi principali dello scalpo.

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Maziero, Danilo. „Localizing and studying epileptogenic sources in patients with focal epilepsy in pre-surgical planning“. Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/59/59135/tde-27072016-151155/.

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The simultaneous acquisitions of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) have been applied to improve the surgery planning of patients with drug resistant epilepsy. However, the classical approach of analyzing the EEG-fMRI data is inefficient in patients whom only few or non interictal epileptiforms discharges (IEDs) are detected during the simultaneous acquisition. Another issue of EEG-fMRI acquisition is related to its high sensitivity to motion, which decreases the quality of both data, even worse in non-cooperative patients. In this work we propose and discuss the application of two methods of analyzing fMRI data of patients with focal epilepsy: Independent component analysis (ICA) and two-dimensional temporal clustering (2dTCA). Each method was applied in a distinct group of patients and the results were compared to those obtained by the classic EEG-fMRI analysis. We have also proposed a method to improve the quality of EEG data using the head position measurements obtained, by a prospective motion correction (PMC) system, during the EEG-fMRI acquisitions. In the ICA study, we have used the electrical source images for selecting independent components (ICs) in EEG data of 13 patients with different spiking frequency. The method detected epilepsy-related BOLD activity in all the patients. Comparatively, the classic EEG-fMRI could be applied in 11 patients and epilepsy-related BOLD activities were found in seven of them. In the 2dTCA study, we have evaluated 20 patients and found epilepsy-related maps in 14 of them. Thirteen of the twenty patients have IEDs detected during the simultaneous acquisition; the classic EEG-fMRI provided maps related to the epileptogenic region in six of them. Finally we have verified in three health subjects that the proposed method for correcting motion-induced artefacts in the EEG data is effective for high amplitude and velocities (~1cm and 55mm/s). We concluded that the ICA and 2dTCA methods increase the sensitivity of using fMRI for mapping the epileptogenic region, mainly in patients presenting few or no IEDs in the EEG data simultaneously acquired to the fMRI. The PMC use during the fMRI acquisition does not degrade the quality of the EEG data acquired simultaneously. In fact, the motion information can be used for improving its quality by correcting motion-induced artefacts.
As aquisições simultâneas de dados de eletroencefalografia (EEG) e imagens funcionais por ressonância magnética (fMRI) vêm sendo utilizadas com intuito de melhorar o planejamento cirúrgico de pacientes com epilepsia refratária. Entretanto, o processamento classicamente usado nestes dados combinados não é possível em pacientes sem descargas epileptiformes interictais (IEDs) e possui baixa sensibilidade para aqueles em que poucas IEDs são detectadas durante a aquisição simultânea. Além disto, a técnica é sensível ao movimento dos pacientes durante as aquisições, o que reduz a qualidade dos dados, principalmente em pacientes não cooperantes. Neste trabalho é proposto e discutido o uso de dois métodos de processamento, baseados nas técnicas de análise de componentes independentes (ICA) e análise temporal de clusters em duas dimensões (2dtca), para se mapear regiões epileptogênicas. Cada método foi analisado em um conjunto diferente de pacientes e os resultados foram comparados com os obtidos pelo EEG-fMRI clássico. Finalmente, propomos um método que utiliza às medidas de posicionamento da cabeça, obtidas durante a aquisição das fMRI, para aumentar a qualidade dos dados de EEG adquiridos simultaneamente. No estudo usando ICA combinado com imagens de fontes elétricas analisamos os dados de 13 pacientes com diferentes frequências de descargas e observamos que este método encontrou ao menos uma componente independente relacionada à epilepsia em cada paciente. Comparativamente usando o processamento convencional foi possível avaliar 11 dos 13 pacientes, e em apenas sete deles os mapas resultantes foram considerados concordantes com a região epileptogênica (RE). No estudo utilizando 2dTCA avaliamos 20 pacientes e encontramos mapas relacionados com a RE em 14 deles. Neste conjunto de pacientes, 13 apresentaram IEDs durante as aquisições; neles o método clássico de processamento teve resultados concordantes com a RE em seis deles. Finalmente verificamos em três sujeitos saudáveis que o método aqui proposto para corrigir os artefatos induzidos no EEG devido ao movimento é efetivo para altas amplitudes e velocidades (~1cm e 55mm/s). Concluímos que os métodos ICA e 2dTCA aumentam a sensibilidade do uso de fMRI para mapear RE, principalmente em pacientes com baixa ou nenhuma detecção de IEDs durante às aquisições. Também concluímos que o uso da correção prospectiva de movimento em aquisições de fMRI não reduz a qualidade do dado de EEG adquirido simultaneamente e que às informações de movimento mensuradas podem melhorar a qualidade deste dado em situações de repouso e movimento do sujeito durante o experimento.

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Buchteile zum Thema "Artefakty fMRI"

Ritter, Petra, Robert Becker, Frank Freyer und Arno Villringer. „EEG Quality:The Image Acquisition Artefact“. In EEG - fMRI, 153–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87919-0_9.

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Debener, Stefan, Cornelia Kranczioch und Ingmar Gutberlet. „EEG Quality: Origin and Reduction of the EEG Cardiac-Related Artefact“. In EEG - fMRI, 135–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-87919-0_8.

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Konferenzberichte zum Thema "Artefakty fMRI"

„Removal of Gradient Artefacts during Transient Head Movements for Continuous EEG-fMRI“. In International Conference on Bio-inspired Systems and Signal Processing. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004802002130220.

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Ferreira, Jose L., Ronald M. Aarts und Pierre J. M. Cluitmans. „Optimized moving-average filtering for gradient artefact correction during simultaneous EEG-fMRI“. In 5th IEEE Biosignals and Biorobotics conference (BRC 2014). IEEE, 2014. http://dx.doi.org/10.1109/brc.2014.6880955.

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„GRADIENT ARTEFACT CORRECTION IN THE EEG SIGNAL RECORDED WITHIN THE fMRI SCANNER“. In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003788001100117.

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