Relevant bibliographies by topics / Brain Magnetic resonance imaging Statistical methods

Academic literature on the topic 'Brain Magnetic resonance imaging Statistical methods'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Brain Magnetic resonance imaging Statistical methods.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Brain Magnetic resonance imaging Statistical methods"

1

Woo, D.-C., C.-B. Choi, J.-W. Nam, K.-N. Ryu, G.-H. Jahng, S.-H. Lee, D.-W. Lee, et al. "Quantitative analysis of hydrocephalic ventricular alterations in Yorkshire terriers using magnetic resonance imaging." Veterinární Medicína 55, No. 3 (April 15, 2010): 125–32. http://dx.doi.org/10.17221/127/2009-vetmed.

Full text
Abstract:
The purpose of this work was to evaluate hydrocephalic ventricular changes using three quantitative analysis methods. The height, area and volume of the ventricles and brain were measured in 20 Yorkshire terriers (10 normal and 10 hydrocephalic dogs) using low-field MR imaging (at 0.2 Tesla). All measurements were averaged and the relative ventricle size was defined as a percentage (percent size of the ventricle/size of the brain). The difference between normal and hydrocephalic dogs was statistically significant for the average of each ventricle as well as for the percentage value. Five hydrocephalic symptoms were identified: circling, head tilting, seizures, ataxia, and strabismus. With respect to height, area and volume of the brain/ventricle, the difference between normal and hydrocephalic dogs was not significant. The ventricle/brain with height (1D) was related to the area (2D) and volume (3D). The correlations with area and volume were as good as the ventricle/brain height ratio in the case of hydrocephalic dogs. Therefore, one-, two- and three-dimensional quantitative methods may be complementary. We expect that the stage of hydrocephalic symptoms can be classified if statistical significance for ventricular size among symptoms is determined with the analysis of a large number of hydrocephalic cases.
APA, Harvard, Vancouver, ISO, and other styles
2

Bhalodiya, Jayendra M., Sarah N. Lim Choi Keung, and Theodoros N. Arvanitis. "Magnetic resonance image-based brain tumour segmentation methods: A systematic review." DIGITAL HEALTH 8 (January 2022): 205520762210741. http://dx.doi.org/10.1177/20552076221074122.

Full text
Abstract:
Background Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.
APA, Harvard, Vancouver, ISO, and other styles
3

Smitha, KA, K. Akhil Raja, KM Arun, PG Rajesh, Bejoy Thomas, TR Kapilamoorthy, and Chandrasekharan Kesavadas. "Resting state fMRI: A review on methods in resting state connectivity analysis and resting state networks." Neuroradiology Journal 30, no. 4 (March 29, 2017): 305–17. http://dx.doi.org/10.1177/1971400917697342.

Full text
Abstract:
The inquisitiveness about what happens in the brain has been there since the beginning of humankind. Functional magnetic resonance imaging is a prominent tool which helps in the non-invasive examination, localisation as well as lateralisation of brain functions such as language, memory, etc. In recent years, there is an apparent shift in the focus of neuroscience research to studies dealing with a brain at ‘resting state’. Here the spotlight is on the intrinsic activity within the brain, in the absence of any sensory or cognitive stimulus. The analyses of functional brain connectivity in the state of rest have revealed different resting state networks, which depict specific functions and varied spatial topology. However, different statistical methods have been introduced to study resting state functional magnetic resonance imaging connectivity, yet producing consistent results. In this article, we introduce the concept of resting state functional magnetic resonance imaging in detail, then discuss three most widely used methods for analysis, describe a few of the resting state networks featuring the brain regions, associated cognitive functions and clinical applications of resting state functional magnetic resonance imaging. This review aims to highlight the utility and importance of studying resting state functional magnetic resonance imaging connectivity, underlining its complementary nature to the task-based functional magnetic resonance imaging.
APA, Harvard, Vancouver, ISO, and other styles
4

Przyborowska, P., Z. Adamiak, P. Holak, Y. Zhalniarovich, and WS Maksymowicz. "Diagnosis of cerebral ventriculomegaly in felines using 0.25 Tesla and 3 Tesla magnetic resonance imaging." Veterinární Medicína 63, No. 1 (January 22, 2018): 28–35. http://dx.doi.org/10.17221/59/2017-vetmed.

Full text
Abstract:
Twenty European shorthair cats with neurological disorders, aged 1–3 years and with body weights of 2.6–4.05 kg, were studied in low-field and high-field magnetic resonance imaging systems. Aims of the study were to evaluate the dilation of lateral ventricles in the examined population of cats with the use of quantitative analysis methods and to identify any differences in the results of low- and high-field magnetic resonance imaging. The average brain height was determined to 27.3 mm, and the average volume of the brain was 10 699.7 mm<sup>3</sup>. Moderately enlarged ventricles were observed in 16 symptomatic cats. Moderate unilateral enlargement was observed in one cat. Mild ventricular asymmetry was described in four animals. The average difference in ventricular height between measurements obtained in low- and high-field magnetic resonance imaging was 0.37 ± 0.16% and for ventricular volume it was 0.62 ± 0.29%. The magnetic resonance imaging scan did not reveal statistically significant differences in brain height or volume between healthy and cats with ventriculomegaly. The differences in the results of low- and high-field magnetic resonance imaging were not statistically significant. Described findings could facilitate the interpretation of magnetic resonance images in cats with ventriculomegaly or hydrocephalus.
APA, Harvard, Vancouver, ISO, and other styles
5

Meziane, Abdelfettah, Saïd MAHMOUDI, and Mohammed Amine CHIKH. "Brain Structures Segmentation by using Statistical Models." Medical Technologies Journal 1, no. 3 (September 28, 2017): 59. http://dx.doi.org/10.26415/2572-004x-vol1iss3p59-59.

Full text
Abstract:
Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The large variation of brain structures requires the development of efficient and specific methods, often by using Magnetic Resonance Imaging (MRI) modality. The goal of our work is to implement an automatic brain’s structures segmentation method that uses the active shape models (ASM) and active appearance models (AAM) techniques. Another goal of this work is to compare the performances of these segmentation approaches, and also to evaluate their use in a computer aided diagnosis tools and to compare their performances.
APA, Harvard, Vancouver, ISO, and other styles
6

Johnson, L. Clark, Todd L. Richards, Kristen H. Archbold, and Carol A. Landis. "Functional Magnetic Resonance Imaging in Nursing Research." Biological Research For Nursing 8, no. 1 (July 2006): 43–54. http://dx.doi.org/10.1177/1099800406289341.

Full text
Abstract:
Functional magnetic resonance imaging (fMRI) is a powerful noninvasive neuroimaging technique nurse scientists can use to investigate the structure and cognitive capacities of the brain. A strong magnetic field and intermittent high-frequency pulses cause protons in body tissues to release energy, which can be recorded and processed into images that are sensitive to specific tissue characteristics. Although temporal and spatial resolution constraints define an upper limit to the precision of magnetic resonance (MR) scanners, the primary index of neuronal activity, hemodynamic response, can be efficiently estimated. Characteristics of the experimental environment, the hypothesis of interest, and the physiology of the cognitive process under investigation provide guidance for the design and limit available options. The processing of functional data to remove unwanted variability is briefly described as are the techniques used to estimate statistical effects and control for the rate of false positives in the results. A detailed applied example of nursing research is included to demonstrate the practical application of the theory, methods, and techniques being discussed. A glossary of key terms is also provided.
APA, Harvard, Vancouver, ISO, and other styles
7

Chen, Shengyong, and Xiaoli Li. "Functional Magnetic Resonance Imaging for Imaging Neural Activity in the Human Brain: The Annual Progress." Computational and Mathematical Methods in Medicine 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/613465.

Full text
Abstract:
Functional magnetic resonance imaging (fMRI) is recently developed and applied to measure the hemodynamic response related to neural activity. The fMRI can not only noninvasively record brain signals without risks of ionising radiation inherent in other scanning methods, such as CT or PET scans, but also record signal from all regions of the brain, unlike EEG/MEG which are biased towards the cortical surface. This paper introduces the fundamental principles and summarizes the research progress of the last year for imaging neural activity in the human brain. Aims of functional analysis of neural activity from fMRI include biological findings, functional connectivity, vision and hearing research, emotional research, neurosurgical planning, pain management, and many others. Besides formulations and basic processing methods, models and strategies of processing technology are introduced, including general linear model, nonlinear model, generative model, spatial pattern analysis, statistical analysis, correlation analysis, and multimodal combination. This paper provides readers the most recent representative contributions in the area.
APA, Harvard, Vancouver, ISO, and other styles
8

Samadi Ghoushchi, Hamed, and Yaghoub Pourasad. "Clustering of Brain Tumors in Brain MRI Images based on Extraction of Textural and Statistical Features." International Journal of Online and Biomedical Engineering (iJOE) 16, no. 12 (October 19, 2020): 116. http://dx.doi.org/10.3991/ijoe.v16i12.16929.

Full text
Abstract:
<p>The purpose of this article is to investigate techniques for classifying tumor grade from magnetic resonance imaging (MRI). This requires early diagnosis of the brain tumor and its grade. Magnetic resonance imaging may show a clear tumor in the brain, but doctors need to measure the tumor in order to treat more or to advance treatment. For this purpose, digital imaging techniques along with machine learning can help to quickly identify tumors and also treatments and types of surgery. These combined techniques in understanding medical images for researchers are an important tool to increase the accuracy of diagnosis. In this paper, classification methods for MRI images of tumors of the human brain are performed to review the astrocytoma-containing glands. Methods used to classify brain tumors, including preprocessing, screening, tissue extraction, and statistical features of the tumor using two types of T<sub>1</sub>W and Flair brain MRI images and also the method of dimensionality reduction of extracted features and how to train them in classification are also explained. Determine the tumor area using three classification of Fuzzy Logic <em>C</em><em>-</em><em>Means</em><em> </em>Clustering (FCM), Probabilistic Neural Networks (PNN) and Support Vector Machines (SVM). In this paper, simulated and real MRI images are used. The results obtained from the proposed methods in this paper are compared with the reference results and the results show that the proposed approach can increase the reliability of brain tumor diagnosis.</p>
APA, Harvard, Vancouver, ISO, and other styles
9

Konar, Amaresha Shridhar, Akash Deelip Shah, Ramesh Paudyal, Maggie Fung, Suchandrima Banerjee, Abhay Dave, Vaios Hatzoglou, and Amita Shukla-Dave. "Quantitative Synthetic Magnetic Resonance Imaging for Brain Metastases: A Feasibility Study." Cancers 14, no. 11 (May 27, 2022): 2651. http://dx.doi.org/10.3390/cancers14112651.

Full text
Abstract:
The present preliminary study aims to characterize brain metastases (BM) using T1 and T2 maps generated from newer, rapid, synthetic MRI (MAGnetic resonance image Compilation; MAGiC) in a clinical setting. We acquired synthetic MRI data from 11 BM patients on a 3T scanner. A multiple-dynamic multiple-echo (MDME) sequence was used for data acquisition and synthetic image reconstruction, including post-processing. MDME is a multi-contrast sequence that enables absolute quantification of physical tissue properties, including T1 and T2, independent of the scanner settings. In total, 82 regions of interest (ROIs) were analyzed, which were obtained from both normal-appearing brain tissue and BM lesions. The mean values obtained from the 48 normal-appearing brain tissue regions and 34 ROIs of BM lesions (T1 and T2) were analyzed using standard statistical methods. The mean T1 and T2 values were 1143 ms and 78 ms, respectively, for normal-appearing gray matter, 701 ms and 64 ms for white matter, and 4206 ms and 390 ms for cerebrospinal fluid. For untreated BMs, the mean T1 and T2 values were 1868 ms and 100 ms, respectively, and 2211 ms and 114 ms for the treated group. The quantitative T1 and T2 values generated from synthetic MRI can characterize BM and normal-appearing brain tissues.
APA, Harvard, Vancouver, ISO, and other styles
10

Chu, Wen-Lin, Min-Wei Huang, Bo-Lin Jian, and Kuo-Sheng Cheng. "Brain Structural Magnetic Resonance Imaging for Joint Independent Component Analysis in Schizophrenic Patients." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 5 (June 19, 2019): 471–78. http://dx.doi.org/10.2174/1573405613666171122163759.

Full text
Abstract:
Background: In past magnetic resonance imaging studies, normal participants and schizophrenia patients have usually been compared using imaging processing modes with only one parameter. A more extensive evaluation of significant differences between gray and white matter in Schizophrenic patents was necessary. Methods: Voxel based morphometry was used to separate brain images into gray matter and white matter. Then, the images were mapped to Montreal Neurological Institute space, and DARTEL analytic template was applied for image calibration with statistical parametric mapping. Finally, joint independent component analysis was employed to analyze the gray and white matter of brain images from Schizophrenic patients and normal controls. In this study, joint independent component analysis was used to discriminate clinical differences in magnetic resonance imaging signals between Schizophrenic patients and normal controls. Results: Region of interest analyses has repeatedly shown gray matter reduction in the superior temporal gyrus of Schizophrenic patients. Conclusion: These results strongly support previous studies regarding brain volume in schizophrenic patients. The connection networks in frontal and temporal lobes evidently did not differ between normal participants and schizophrenia patients.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Brain Magnetic resonance imaging Statistical methods"

1

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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Roura, Pérez Eloy. "Automated methods on magnetic resonance brain imaging in multiple sclerosis." Doctoral thesis, Universitat de Girona, 2016. http://hdl.handle.net/10803/394030.

Full text
Abstract:
In this thesis, we have focused on the image pre-processing in order to enhance the image information. The main aspects of this enhancement rely on removing any image noise and correcting any intensity bias induced by the scanner. Besides, we also contributed with a new technique based on a multispectral, adaptive, region growing algorithm in order to segment the brain from the rest of the head. We include, as a pre-processing step, the image registration process, in which we proposed a novel pipeline by using information from multiple modalities to improve the results of this process. Furthermore, we have also studied the current techniques for the detection and segmentation of WML, proposing a new method based on a previous proposal. Therefore, we presented a tool able to automatically detect and segment WML of Multiple sclerosis and Lupus patients.
En aquesta tesi ens centrem, per una part, en el pre-processat de la imatge per tal d'eliminar el soroll i corregir les inhomogeneïtats en les intensitats, ambdós errors introduïts per l'escàner. A més hem contribuït també amb una nova tècnica basada en un algoritme de “región growing” per tal de segmentar el cervell de dins de tota la imatge del cap. Incloem com a pre-processat el registre d'imatges, on hem proposat una “pipeline" mitjançant la informació de múltiples modalitats per tal de millorar els resultats d'aquest procés. Per altra banda, hem estudiat també les tècniques actuals de detecció i segmentació de lesions en la matèria blanca, proposant un mètode nou basat en anteriors propostes. Així doncs, presentem una eina automàtica capaç de detectar i segmentar lesions en la matèria blanca de pacients d'Esclerosi Múltiple i Lupus.
APA, Harvard, Vancouver, ISO, and other styles
3

Streitbürger, Daniel-Paolo. "Investigating Brain Structure Using Voxel-Based Methods with Magnetic Resonance Imaging." Doctoral thesis, Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-132638.

Full text
Abstract:
The number of people suffering from neurodegenerative diseases, such as Alzheimer`s disease, increased dramatically over the past centuries and is expected to increase even further within the next years. Based on predictions of the World Health Organization and Alzheimer`s Disease International, 115 million people will suffer from dementia by the year 2050. An additionally increase in other age related neurodegenerative diseases is also forecasted. Quite naturally, neurodegenerative diseases became a focus of attention of governments and health insurances, trying to control the uprising financial burden. Early detection and treatment of neurodegenerative diseases could be an important component in containing this problem. In particular, researchers focused on automatic methods to analyze patients’ imaging data. One way to detect structural changes in magnetic resonance images (MRI) is the voxel-based method approach. It was specifically implemented for various imaging modalities, e.g. T1-weighted images or diffusion tensor imaging (DTI). Voxel-based morphometry (VBM), a method specifically designed to analyze T1-weighted images, has become very popular over the last decade. Investigations using VBM revealed numerous structural brain changes related to, e.g. neurodegeneration, learning induced structural changes or aging. Although voxel-based methods are designed to be robust and reliable structural change detection methods, it is known that they can be influenced by physical and physiological factors. Dehydration, for example, can affect the volume of brain structures and possibly induce a confound in morphometric studies. Therefore, three-dimensional T1-weighted images were acquired of six young and healthy subjects during different states of hydration. Measurements during normal hydration, hyperhydration, and dehydration made it possible to assess consequential volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using VBM, FreeSurfer and SIENA. A significant decrease of GM and WM volume, associated with dehydration, was found in various brain regions. The most prominent effects were found in temporal and parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, an expansion around 6% of the ventricular system affecting both lateral ventricles, i.e. the third and fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of Alzheimer’s disease during disease progression and in its prestage mild cognitive impairment. Based on these findings, a potential confound in GM and WM or CSF studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed. These results underline the sensitivity of VBM and might also concern other voxel-based methods, such as Tract-Based Spatial Statistics (TBSS). TBSS was specifically designed for WM analyses and its sensitivity might be helpful for revealing the spatial relation of structural WM changes and related blood serum biomarkers. Two common brain related biomarkers are the glial protein S100B, a plasticity inducing neuro- and gliotrophin, and neuron-specific enolase (NSE), a marker for neuronal damage. However, the spatial specificity of these biomarkers for brain region has not been investigated in vivo until now. Therefore, we acquired two MRI parameters – T1- weighted and DTI - sensitive to changes in GM and WM, and obtained serum S100B and NSE levels of 41 healthy subjects. Additionally, the gene expression of S100B on the whole brain level in a male cohort of three subjects from the Allen Brain Database was analyzed. Furthermore, a female post mortal brain was investigated using double immunofluorescence labeling with oligodendrocyte markers. It could be shown that S100B is specifically related to white matter structures, namely the corpus callosum, anterior forceps and superior longitudinal fasciculus in female subjects. This effect was observed in fractional anisotropy and radial diffusivity – the latest an indicator of myelin changes. Histological data confirmed a co-localization of S100B with oligodendrocyte markers in the human corpus callosum. S100B was most abundantly expressed in the corpus callosum according to the whole genome Allen Human Brain Atlas. In addition, NSE was related to gray matter structures, namely the amygdala. This effect was detected across sexes. The data demonstrates a very high S100B expression in white matter tracts, in particular in human corpus callosum. This was the first in vivo study validating the specificity of the glial marker S100B for the human brain, and supporting the assumption that radial diffusivity represents a myelin marker. The results open a new perspective for future studies investigating major neuropsychiatric disorders. All above mentioned studies are mainly dependent on the sensitivity and accuracy of soft and hardware parameters. In particular, technical developments have improved acquisition accuracy in the field of MRI. Interestingly, very little is known about the confounding effects of variations due to hardware parameters and their possible impact on reliability and sensitivity of VBM. Recent studies have shown that different acquisition parameters may influence VBM results. Therefore age-related GM changes were investigated with VBM in 36 healthy volunteers grouped into 12 young, 12 middle-aged and 12 elderly subject. Six T1-weighted datasets were acquired per subject with a 12-channel matrix coil, as well as a 32-channel array, MP-RAGE and MP2RAGE, and with isotropic resolutions of 0.8 and 1 mm. DARTEL-VBM was applied on all images and GM, WM and CSF segments were statistically analyzed.. Paired t-tests and statistical interaction tests revealed significant effects of acquisition parameters on the estimated gray-matter-density (GMD) in various cortical and subcortical brain regions. MP2RAGE seemed slightly less prone to false positive results when comparing data acquired with different RF coils and yielded superior segmentation of deep GM structures. With the 12-channel coil, MP-RAGE was superior in detecting age-related changes, especially in cortical structures. Most differences between both sequences became insignificant with the 32-channel coil, indicating that the MP2RAGE images benefited more from the improved signal-to-noise ratio and improved parallel-imaging reconstruction). A possible explanation might be an overestimation of the GM compartment on the MP-RAGE images. In view of substantial effects obtained for all parameters, careful standardization of the acquisition protocol is advocated. While the current investigation focused on aging effects, similar results are expected for other VBM studies, like on plasticity or neurodegenerative diseases. This work has shown that voxel-based methods are sensitive to subtle structural brain changes, independent of imaging modality and scanning parameters. In particular, the studies investigated and discussed the analysis of T1- and diffusion weighted images with VBM and TBSS in the context of dehydration, blood serum sensitive biomarkers and aging were discussed. The major goal of these studies was the investigation of the sensitivity of voxel-based methods. In conclusion, sensitivity and accuracy of voxelbased methods is already high, but it can be increased significantly, using optimal hardand software parameters. It is of note, though, that these optimizations and the concomitant increase of detection sensitivity could also introduce additional confounding factors in the imaging data and interfere with the latter preprocessing and statistical computations. To avoid an interference e.g. originating from physiological parameters, a very careful selection and monitoring of biological parameters of each volunteer throughout the whole study is recommended. A potential impact of scanning parameters can be minimized by strict adherence to the imaging protocol for each study subjectwithin a study. A general increase in detection sensitivity due to optimized parameters selection in hard- and/or can not be concluded by the above mentioned studies. Although the present work addressed some of those issues, the topic of optimal selection of parameters for morphometric studies is still very complex and controversial and has to be individually decided. Further investigations are needed to define more general scanning and preprocessing standards to increase detection sensitivity without the concomitant amplification of confounding factors.
APA, Harvard, Vancouver, ISO, and other styles
4

Grieve, Stuart Michael. "Development of fast magnetic resonance imaging methods for investigation of the brain." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365824.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Woo, Bo-kei, and 胡寶琦. "A new hierarchical Bayesian approach to low-field magnetic resonance imaging." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31226917.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Buchanan, Colin Richard. "Structural brain networks from diffusion MRI : methods and application." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/14183.

Full text
Abstract:
Structural brain networks can be constructed at a macroscopic scale using diffusion magnetic resonance imaging (dMRI) and whole-brain tractography. Under this approach, grey matter regions, such as Brodmann areas, form the nodes of a network and tractography is used to construct a set of white matter fibre tracts which form the connections. Graph-theoretic measures may then be used to characterise patterns of connectivity. In this study, we measured the test-retest properties of such networks by varying several factors affecting network construction using ten healthy volunteers who underwent a dMRI protocol at 1.5 T on two separate occasions. High resolution T1-weighted brains were parcellated into regions-of-interest and network connections were identified using dMRI and two alternative tractography algorithms, two alternative seeding strategies, constraints on anatomical plausibility and three alternative network weightings. Test-retest performance was found to improve when: 1) seeding from white matter, rather than grey; and 2) using probabilistic tractography, rather than deterministic. In terms of network weighting, a measure of streamline density produced better test-retest performance than tract-averaged diffusion anisotropy, although it remains unclear which is most representative of the underlying axonal connections. These findings were then used to inform network construction for two further cohorts: a casecontrol analysis of 30 patients with amyotrophic lateral sclerosis (ALS) compared with 30 age-matched healthy controls; and a cross-sectional analysis of 80 healthy volunteers aged 25– 64 years. In both cases, networks were constructed using a weighting reflecting tract-averaged fractional anisotropy (FA). A mass-univariate statistical technique called network-based statistics, identified an impaired motor-frontal-subcortical subnetwork (10 nodes and 12 bidirectional connections), consistent with upper motor neuron pathology, in the ALS group compared with the controls. Reduced FA for three of the impaired network connections, which involved fibres of the cortico-spinal tract, were significantly correlated with the rate of disease progression. Cross-sectional analysis of the 80 healthy volunteers was intended to provide supporting evidence for the widely reported age-related decline in white matter integrity. However, no meaningful relationships were found between increasing age and impaired connectivity based on global, lobar and nodal network properties – findings which were confirmed with a conventional voxel-based analysis of the dMRI data. In conclusion, whilst current acquisition protocols and methods can produce networks capable of characterising the genuine between-subject differences in connectivity, it is challenging to measure subtle white matter changes, for example, due to normal ageing. We conclude that future work should be undertaken to address these concerns.
APA, Harvard, Vancouver, ISO, and other styles
7

Stelzer, Johannes. "Nonparametric statistical inference for functional brain information mapping." Doctoral thesis, Universitätsbibliothek Leipzig, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:15-qucosa-143884.

Full text
Abstract:
An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate analysis frameworks. Two most prominent MVPA methods for information mapping are searchlight decoding and classifier weight mapping. The new MVPA brain mapping methods, however, have also posed new challenges for analysis and statistical inference on the group level. In this thesis, I discuss why the usual procedure of performing t-tests on MVPA derived information maps across subjects in order to produce a group statistic is inappropriate. I propose a fully nonparametric solution to this problem, which achieves higher sensitivity than the most commonly used t-based procedure. The proposed method is based on resampling methods and preserves the spatial dependencies in the MVPA-derived information maps. This enables to incorporate a cluster size control for the multiple testing problem. Using a volumetric searchlight decoding procedure and classifier weight maps, I demonstrate the validity and sensitivity of the new approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, the new results showed a higher sensitivity and spatial specificity. The second goal of this thesis is the comparison of the two widely used information mapping approaches -- the searchlight technique and classifier weight mapping. Both methods take into account the spatially distributed patterns of activation in order to predict stimulus conditions, however the searchlight method solely operates on the local scale. The searchlight decoding technique has furthermore been found to be prone to spatial inaccuracies. For instance, the spatial extent of informative areas is generally exaggerated, and their spatial configuration is distorted. In this thesis, I compare searchlight decoding with linear classifier weight mapping, both using the formerly proposed non-parametric statistical framework using a simulation and ultra-high-field 7T experimental data. It was found that the searchlight method led to spatial inaccuracies that are especially noticeable in high-resolution fMRI data. In contrast, the weight mapping method was more spatially precise, revealing both informative anatomical structures as well as the direction by which voxels contribute to the classification. By maximizing the spatial accuracy of ultra-high-field fMRI results, such global multivariate methods provide a substantial improvement for characterizing structure-function relationships.
APA, Harvard, Vancouver, ISO, and other styles
8

Myllylä, T. (Teemu). "Multimodal biomedical measurement methods to study brain functions simultaneously with functional magnetic resonance imaging." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205076.

Full text
Abstract:
Abstract Multimodal measurements are increasingly being employed in the study of human physiology. Brain studies in particular can draw advantage of simultaneous measurements using different modalities to analyse correlations, mechanisms and relationships of physiological signals and their dynamics in relation to brain functions. Moreover, multimodal measurements help to identify components of physiological dynamics generated specifically by the brain. This thesis summarizes the study, design and development of non-invasive medical instruments that can be utilized in conjunction with magnetic resonance imaging (MRI). A key challenge in the development of measurement methods is posed by the extraordinary requirements that the MRI environment poses - all materials need to be MR-compatible and the selected instruments and devices must not be affected by the strong magnetic field generated by the MRI scanner nor the MRI by the instruments placed within its scanning volume. The presented methods allow simultaneous continuous measurement of heart rate (HR) and metabolism from the brain cortex as well as pulse wave velocity (PWV) and blood pressure measurements in synchrony with electroencephalography (EEG) and MRI. Furthermore, the thesis explored the reliability and accuracy of the responses gathered by the developed instruments and, using new experimental methods, estimated the propagation of near-infrared light in the human brain. The goal of the novel multimodal measurement environment is to provide more extensive tools for medical researchers, neurologists in particular, to acquire accurate information on the function of the brain and the human body. Measurements have been performed on more than 70 persons using the presented multimodal setup to study such factors as the correlation between blood oxygen level-dependent (BOLD) data and low-frequency oscillations (LFOs) during the resting state
Tiivistelmä Multimodaalisia kuvantamismenetelmiä käytetään enenevässä määrin ihmisen fysiologian ja elintoimintojen tutkimisessa. Erityisesti aivotutkimuksessa samanaikaisesti useammalla modaliteetilla mittaaminen mahdollistaa erilaisten fysiologisten mekanismien ja niiden korrelaatioiden tutkimisen kehon ja aivotoimintojen välillä. Lisäksi multimodaaliset mittaukset auttavat yksilöimään fysiologiset komponentit toisistaan ja identifioimaan aivojen tuottamia fysiologisia signaaleja. Tämä väitöskirja kokoaa tutkimustyön sekä laite- ja instrumentointisuunnittelun ja sen kehittämistyön ei-invasiivisesti toteutettujen lääketieteen mittausmenetelmien käyttämiseksi magneettikuvauksen aikana. Erityishaasteena työssä on ollut magneettikuvausympäristö, joka asettaa erityisvaatimuksia mm. mittalaitteissa käytettäville materiaaleille sekä laitteiden häiriönsiedolle magneettikuvauslaitteen aiheuttaman voimakkaan magneettikentän takia. Kehitettävät mittausmenetelmät eivät myöskään saa aiheuttaa häiriöitä magneettikuvauslaitteen tuottamalle kuvainformaatiolle. Väitöskirjassa esitettävät mittausmenetelmät tekevät mahdolliseksi mitata magneettikuvausympäristössä ihmisen sydämen sykettä, veren virtauksen kulkunopeutta ja verenpaineen vaihteluja sekä aivokuoren metaboliaa - kaikki synkronissa aivosähkökäyrän mittaamisen ja magneettikuvantamisen kanssa. Lisäksi väitöskirjassa tutkitaan kehitettyjen mittausmenetelmien antamaa mittaustarkkuutta sekä arvioidaan lähi-infrapunavalon etenemistä ihmisen aivoissa uudenlaisella menetelmällä. Kehitetyllä multimodaalisella mittausympäristöllä on tavoitteena antaa lääketieteen alan tutkijoille, erityisesti neurologeille, käyttöön mittausmenetelmiä, joiden avulla voidaan tutkia ihmisen aivojen ja kehon välisiä toimintoja aiempaa kattavammin. Laitekokonaisuudella on tutkittu jo yli 70:tä henkilöä. Näissä mittauksissa on tutkittu mm. veren happitasojen hitaita vaihteluja ihmisen aivojen ollessa lepotilassa, ns. resting state -tilassa
APA, Harvard, Vancouver, ISO, and other styles
9

Clayden, Jonathan D. "Comparative analysis of connection and disconnection in the human brain using diffusion MRI : new methods and applications." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/2383.

Full text
Abstract:
Diffusion magnetic resonance imaging (dmri) is a technique that can be used to examine the diffusion characteristics of water in the living brain. A recently developed application of this technique is tractography, in which information from brain images obtained using dmri is used to reconstruct the pathways which connect regions of the brain together. Proxy measures for the integrity, or coherence, of these pathways have also been defined using dmri-derived information. The disconnection hypothesis suggests that specific neurological impairments can arise from damage to these pathways as a consequence of the resulting interruption of information flow between relevant areas of cortex. The development of dmri and tractography have generated a considerable amount of renewed interest in the disconnectionist thesis, since they promise a means for testing the hypothesis in vivo in any number of pathological scenarios. However, in order to investigate the effects of pathology on particular pathways, it is necessary to be able to reliably locate them in three-dimensional dmri images. The aim of the work described in this thesis is to improve upon the robustness of existing methods for segmenting specific white matter tracts from image data, using tractography, and to demonstrate the utility of the novel methods for the comparative analysis of white matter integrity in groups of subjects. The thesis begins with an overview of probability theory, which will be a recurring theme throughout what follows, and its application to machine learning. After reviewing the principles of magnetic resonance in general, and dmri and tractography in particular, we then describe existing methods for segmenting particular tracts from group data, and introduce a novel approach. Our innovation is to use a reference tract to define the topological characteristics of the tract of interest, and then search a group of candidate tracts in the target brain volume for the best match to this reference. In order to assess how well two tracts match we define a heuristic but quantitative tract similarity measure. In later chapters we demonstrate that this method is capable of successfully segmenting tracts of interest in both young and old, healthy and unhealthy brains; and then describe a formalised version of the approach which uses machine learning methods to match tracts from different subjects. In this case the similarity between tracts is represented as a matching probability under an explicit model of topological variability between equivalent tracts in different brains. Finally, we examine the possibility of comparing the integrity of groups of white matter structures at a level more fine-grained than a whole tract.
APA, Harvard, Vancouver, ISO, and other styles
10

Taljan, Kyle Andrew Ignatius. "Investigations of Anatomical Connectivity in the Internal Capsule of Macaques with Diffusion Magnetic Resonance Imaging." Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1311093061.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Brain Magnetic resonance imaging Statistical methods"

1

Poldrack, Russell A. Handbook of functional MRI data analysis. Cambridge: Cambridge University Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

The statistical analysis of functional MRI data. New York: Springer, 2008.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Faro, Scott H. BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

BOLD fMRI: A guide to functional imaging for neuroscientists. New York: Springer, 2010.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

1971-, Song Allen W., and McCarthy Gregory 1952-, eds. Functional magnetic resonance imaging. 2nd ed. Sunderland, Mass: Sinauer Associates, 2009.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Magnetic resonance neuroimaging: Methods and protocols. New York: Humana Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Magnetic resonance imaging of the brain. Philadelphia: Lippincott, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Luna, A. Diffusion MRI outside the brain: A case-based review and clinical applications. Berlin: Springer, 2012.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Brain imaging: An introduction. London: Wright, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

W, Toga Arthur, and Mazziotta John C, eds. Brain mapping: The methods. 2nd ed. Amsterdam: Academic Press, 2002.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Brain Magnetic resonance imaging Statistical methods"

1

Soufi, Ghazaleh Jamalipour, Nastaran Fallahpour, Kaveh Jamalipour Soufi, and Siavash Iravani. "Magnetic Resonance Spectroscopic Analysis in Brain Tumors." In Medical Imaging Methods, 43–58. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9121-7_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Dela Haije, Tom, and Aasa Feragen. "Conceptual Parallels Between Stochastic Geometry and Diffusion-Weighted MRI." In Mathematics and Visualization, 193–202. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-56215-1_9.

Full text
Abstract:
AbstractDiffusion-weighted magnetic resonance imaging (MRI) is sensitive to ensemble-averaged molecular displacements, which provide valuable information on e.g. structural anisotropy in brain tissue. However, a concrete interpretation of diffusion-weighted MRI data in terms of physiological or structural parameters turns out to be extremely challenging. One of the main reasons for this is the multi-scale nature of the diffusion-weighted signal, as it is sensitive to the microscopic motion of particles averaged over macroscopic volumes. In order to analyze the geometrical patterns that occur in (diffusion-weighted measurements of) biological tissue and many other structures, we may invoke tools from the field of stochastic geometry. Stochastic geometry describes statistical methods and models that apply to random geometrical patterns of which we may only know the distribution. Despite its many uses in geology, astronomy, telecommunications, etc., its application in diffusion-weighted MRI has so far remained limited. In this work we review some fundamental results in the field of diffusion-weighted MRI from a stochastic geometrical perspective, and discuss briefly for which other questions stochastic geometry may prove useful. The observations presented in this paper are partly inspired by the Workshop on Diffusion MRI and Stochastic Geometry held at Sandbjerg Estate (Denmark) in 2019, which aimed to foster communication and collaboration between the two fields of research.
APA, Harvard, Vancouver, ISO, and other styles
3

Shen, Qiang, Lora Tally Watts, Wei Li, and Timothy Q. Duong. "Magnetic Resonance Imaging in Experimental Traumatic Brain Injury." In Methods in Molecular Biology, 645–58. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3816-2_35.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Wang, Shui-Hua, Yu-Dong Zhang, Zhengchao Dong, and Preetha Phillips. "Canonical Feature Extraction Methods for Structural Magnetic Resonance Imaging." In Pathological Brain Detection, 45–70. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4026-9_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Melhem, Elias R., and Riyadh N. Alokaili. "Intra-axial Brain Tumors: Diagnostic Magnetic Resonance Imaging." In Methods of Cancer Diagnosis, Therapy, and Prognosis, 263–78. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8665-5_21.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Bulakbasi, Nail, and Murat Kocaoglu. "Metastatic Solitary Malignant Brain Tumor: Magnetic Resonance Imaging." In Methods of Cancer Diagnosis, Therapy, and Prognosis, 305–23. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8665-5_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Yamasaki, Fumiyuki, Kazuhiko Sugiyama, and Kaoru Kurisu. "Brain Tumors: Apparent Diffusion Coefficient at Magnetic Resonance Imaging." In Methods of Cancer Diagnosis, Therapy, and Prognosis, 279–96. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8665-5_22.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Aggarwal, Manisha, Jiangyang Zhang, and Susumu Mori. "Magnetic Resonance Imaging-Based Mouse Brain Atlas and Its Applications." In Methods in Molecular Biology, 251–70. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-61737-992-5_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

McAteer, Martina A., Constantin von Zur Muhlen, Daniel C. Anthony, Nicola R. Sibson, and Robin P. Choudhury. "Magnetic Resonance Imaging of Brain Inflammation Using Microparticles of Iron Oxide." In Methods in Molecular Biology, 103–15. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-901-7_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Hunt, Matthew A., and Edward A. Neuwelt. "Magnetic Resonance Imaging of Brain Tumors Using Iron Oxide Nanoparticles." In Methods of Cancer Diagnosis, Therapy, and Prognosis, 297–304. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-8665-5_23.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Brain Magnetic resonance imaging Statistical methods"

1

Lei, Zhen, Qinghai Wang, Weifeng Wang, and Deven Hu. "A New Statistical Method for Detecting Significant Activation in Functional Magnetic Resonance Brain Imaging." In 2009 WRI World Congress on Computer Science and Information Engineering. IEEE, 2009. http://dx.doi.org/10.1109/csie.2009.578.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Chen, Bin, John Moreland, and Jingyu Zhang. "Human Brain Functional MRI and DTI Visualization With Virtual Reality." In ASME 2011 World Conference on Innovative Virtual Reality. ASMEDC, 2011. http://dx.doi.org/10.1115/winvr2011-5565.

Full text
Abstract:
Magnetic resonance diffusion tensor imaging (DTI) and functional MRI (fMRI) are two active research areas in neuroimaging. DTI is sensitive to the anisotropic diffusion of water exerted by its macromolecular environment and has been shown useful in characterizing structures of ordered tissues such as the brain white matter, myocardium, and cartilage. The diffusion tensor provides two new types of information of water diffusion: the magnitude and the spatial orientation of water diffusivity inside the tissue. This information has been used for white matter fiber tracking to review physical neuronal pathways inside the brain. Functional MRI measures brain activations using the hemodynamic response. The statistically derived activation map corresponds to human brain functional activities caused by neuronal activities. The combination of these two methods provides a new way to understand human brain from the anatomical neuronal fiber connectivity to functional activities between different brain regions. In this study, virtual reality (VR) based MR DTI and fMRI visualization with high resolution anatomical image segmentation and registration, ROI definition and neuronal white matter fiber tractography visualization and fMRI activation map integration is proposed. Rationale and methods for producing and distributing stereoscopic videos are also discussed.
APA, Harvard, Vancouver, ISO, and other styles
3

Sacho, Isabella Batistela Inhesta, João Vitor Gerdulli Tamanini, Brunno Machado Campos, Danilo Santos Silva, Ana Carolina Coan, and Wagner Mauad Avelar. "Brain functional network alterations in patients with asymptomatic carotid disease." In XIII Congresso Paulista de Neurologia. Zeppelini Editorial e Comunicação, 2021. http://dx.doi.org/10.5327/1516-3180.449.

Full text
Abstract:
Background: The best therapeutic approach to asymptomatic carotid stenosis (ACS) is still subject to discussion. Previous studies have agreed on the findings that ACS patients present with cerebral atrophy and cognitive decline compared to healthy controls. The present study aims to identify possible alterations in the brain functional network of such patients. Objectives: Study alternations in the connectivity of the Default Mode Network (DNM) in patients with ACS of at least 70%, compared to controls. Design and Setting: A cross-sectional case-control study was carried out at the Neuroimaging Laboratory at Hospital das Clínicas, Universidade Estadual de Campinas (UNICAMP) - Campinas, São Paulo (Brazil). Methods: Two groups of 15 individuals matched by sex and age, the first composed ACS patients with stenosis and the second of healthy volunteers, were submitted to 3 Tesla magnetic resonance imaging. The images were analyzed using Statistical Parametric Mapping 12 and UF2C User Friendly Functional Connectivity Toolbox software. All patients signed the Informed Consent Form. Results: ACS patients were 13 men and 2 women with an average age of 72 years. Regarding DMN connectivity, the control presented higher activity, particularly in the medial segment of the superior frontal gyrus. Conclusion: ACS patients with stenosis higher than 70% displayed prejudiced cerebral connectivity compared to healthy controls.
APA, Harvard, Vancouver, ISO, and other styles
4

Kornev, Denis, Roozbeh Sadeghian, Stanley Nwoji, Qinghua He, Amir Gandjbbakhche, and Siamak Aram. "Machine Learning-Based Gaming Behavior Prediction Platform." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001826.

Full text
Abstract:
Brain disorders caused by Gaming Addiction drastically increased due to the rise of Internet users and Internet Gaming auditory. Driven by such a tendency, in 2018, World Health Organization (WHO) and the American Medical Association (AMA) addressed this problem as a “gaming disorder” and added it to official manuals. Scientific society equipped by statistical analysis methods such as t-test, ANOVA, and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG), has achieved significant success in brain mapping, examining dynamics and patterns in different conditions and stages. Nevertheless, more powerful, self-learning intelligent algorithms are suitable not only to evaluate the correlation between gaming addiction patterns but also to predict behavior and prognosis brain response depending on the addiction severity. The current paper aims to enrich the knowledge base of the correlation between gaming activity, decision-making, and brain activation, using Machine Learning (ML) algorithms and advanced neuroimaging techniques. The proposed gaming behavior patterns prediction platform was built inside the experiment environment composed of a Functional Near-Infrared Spectrometer (fNIRS) and the computer version of Iowa Gambling Task (IGT). Thirty healthy participants were hired to perform 100 cards selection while equipped with fNIRS. Thus, accelerated by IGT gaming decision-making process was synchronized with changes of oxy-hemoglobin (HbO) levels in the human brain, averaged, and investigated in the left and the right brain hemispheres as well as different psychosomatic conditions, conditionally divided by blocks with 20 card trials in each: absolute unknown and uncertainty in the first block, “pre-hunch” and “hunch” in the second and third blocks, and conceptuality and risky in the fourth and fifth blocks. The features space was constructed around the HbO signal, split by training and tested in two proportions 70/30 and 80/20, and drove patterns prediction ML-based platform consisted of five mechanics, such as Multiple Regression, Classification and Regression Trees (CART), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest. The algorithm prediction power was validated by the 5- and 10-fold cross-validation method and compared by Root Mean Squared Error (RMSE) and coefficient of determination (R Squared) metrics. Indicators of “the best” fit model, lowest RMSE, and highest R Squared were determined for each block and both brain hemispheres and used to make a conclusion about prediction accuracy: SVM algorithm with RBF kernel, Random Forest, and ANN demonstrated the best accuracy in most cases. Lastly, “best fit” classifiers were applied to the testing dataset and finalized the experiment. Hence, the distribution of gaming score was predicted by five blocks and both brain hemispheres that reflect the decision-making process patterns during gaming. The investigation showed increasing ML algorithm prediction power from IGT block one to five, reflecting an increasing learning effect as a behavioral pattern. Furthermore, performed inside constructed platform simulation could benefit in diagnosing gaming disorders, their patterns, mechanisms, and abnormalities.
APA, Harvard, Vancouver, ISO, and other styles
5

Brinkmann, Benjamin H., Armando Manduca, and Richard A. Robb. "Quantitative analysis of statistical methods of grayscale inhomogeneity correction in magnetic resonance images." In Medical Imaging 1996, edited by Murray H. Loew and Kenneth M. Hanson. SPIE, 1996. http://dx.doi.org/10.1117/12.237957.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Jondhale, Utkarsha I., and Rachna s. Potpelwar. "Multispectral brain Magnetic Resonance Imaging denoising using non-local means with statistical nearest neighbor." In 2019 International Conference on Communication and Electronics Systems (ICCES). IEEE, 2019. http://dx.doi.org/10.1109/icces45898.2019.9002219.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Perkins, James B., Ian R. Greenshields, Francis DiMario, and Gale Ramsby. "Unsupervised classification of multiecho magnetic resonance images of the pediatric brain with implicit spatial and statistical hypotheses validation." In Medical Imaging 1993, edited by Murray H. Loew. SPIE, 1993. http://dx.doi.org/10.1117/12.154523.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Andrioli, Carlos José, and Carlos Eduardo Thomaz. "Discriminant analysis of background noise in extremity magnetic resonance images." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/wvc.2021.18891.

Full text
Abstract:
Since the creation of the first magnetic resonance imaging (MRI) equipment in 1974, experts have been studying the continuous improvement of image quality. This work aims to study the types of background noise in images from extremity MRI system of high-field, mainly caused by Faraday Cage problems. Phantom images of 1T equipment were investigated for this study. For the acquisition of these images, a protocol called DQA (Daily Quality Assurance) was used. For this work, 45 MRI images were acquired, which were pre-classified by an expert, and analyzed by SNR, an index that quantifies the ratio between signal and image noise, and by the multivariate statistical methods PCA + MLDA. PCA served as a statistical filter, which considerably decreased the amount of input information for MLDA. When all main components were used, MLDA showed an accuracy of 93.33% and results that allowed to discriminate background noise from these images in complementarity with SNR.
APA, Harvard, Vancouver, ISO, and other styles
9

Viviers, D., E. E. W. Van Houten, M. D. J. McGarry, J. B. Weaver, and K. D. Paulsen. "Initial In-Vivo Results Considering Rayleigh Damping in Magnetic Resonance Elastography." In ASME 2009 International Mechanical Engineering Congress and Exposition. ASMEDC, 2009. http://dx.doi.org/10.1115/imece2009-12709.

Full text
Abstract:
Dispersive material properties provide valuable metrics for characterizing the nature of soft tissue lesions. Magnetic Resonance Elastography (MRE) targets non-invasive breast cancer diagnosis and is capable of imaging the damping properties of soft tissue. 3D time-harmonic displacement data obtained via MRI is used to drive a reconstruction algorithm capable of deducing the distribution of mechanical properties in the tissue. To make the most of this diagnostic capability, characterization of the damping behavior of tissue is made more sophisticated by the use of a Rayleigh damping model. To date, time-harmonic motion attenuation in tissue as found in dynamic MRE has been characterized by a single parameter model that takes the form of an imaginary component of a complex valued shear modulus. A more generalized damping formulation for the time-harmonic case, known commonly as Rayleigh or proportional damping, includes an additional parameter that takes the form of an imaginary component of a complex valued density. The effects of these two different damping mechanisms can be shown to be independent across homogeneous distributions and mischaracterization of the damping structure can be shown to lead to artifacts in the reconstructed attenuation profile. We have implemented a Rayleigh damping reconstruction method for MRE and measured the dispersive properties of actual patient data sets with impressive results. Reconstructions show a close match with varying tissue structure. The reconstructed values for real shear modulus and overall damping levels are in reasonable agreement with values established in the literature or measured by mechanical testing, and in the case of malignant lesions, show good correspondence with contrast enhanced MRI. There is significant medical potential for an algorithm that can accurately reconstruct soft tissue material properties through non invasive MRI scans. Imaging methods that help identify invasive regions through reconstruction of dispersive soft tissue properties could be applied to pathologies in the brain, lung, liver and kidney as well.
APA, Harvard, Vancouver, ISO, and other styles
10

Easley, Deanna C., Prahlad G. Menon, Pamela A. Moalli, and Steven D. Abramowitch. "Inter-Observer Variability of Vaginal Wall Segmentation From MRI: A Statistical Shape Analysis Approach." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-53499.

Full text
Abstract:
Pelvic floor disorders such as Pelvic Organ Prolapse (POP) negatively impact the health and quality of life of millions of women worldwide. POP is characterized by the descent of the pelvic organs into the vagina due to compromised connective tissue support, resulting in discomfort and urinary/fecal incontinence. Magnetic Resonance Imaging (MRI) has been used to aid in the quantification of these anatomical changes, however the inter- and intra-observer repeatability necessary to make reliable conclusions about changes in anatomical positioning is questioned using current methods. The aim of this study was to quantify the degree of variability produced from inter-observer manual tracings of the vagina from MRI scans using a statistical shape matching approach.
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Brain Magnetic resonance imaging Statistical methods"

1

Rosen, Matthew. New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada568601.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Rosen, Matthew S. New Methods of Low-Field Magnetic Resonance Imaging for Application to Traumatic Brain Injury. Fort Belvoir, VA: Defense Technical Information Center, February 2013. http://dx.doi.org/10.21236/ada585828.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Brain Magnetic resonance imaging Statistical methods' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography