Relevant bibliographies by topics / MRI

Academic literature on the topic 'MRI'

Author: Grafiati
Published: 4 June 2021
Last updated: 31 August 2024

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Journal articles on the topic "MRI"

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Vogel, Patrick, Steffen Lother, Martin A. Ruckert, Walter H. Kullmann, Peter M. Jakob, Florian Fidler, and Volker C. Behr. "MRI Meets MPI: A Bimodal MPI-MRI Tomograph." IEEE Transactions on Medical Imaging 33, no. 10 (October 2014): 1954–59. http://dx.doi.org/10.1109/tmi.2014.2327515.

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Mizerski, Krzysztof A., and Wladimir Lyra. "On the connection between the magneto-elliptic and magneto-rotational instabilities." Journal of Fluid Mechanics 698 (March 30, 2012): 358–73. http://dx.doi.org/10.1017/jfm.2012.95.

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AbstractIt has recently been suggested that the magneto-rotational instability (MRI) is a limiting case of the magneto-elliptic instability (MEI). This limit is obtained for horizontal modes in the presence of rotation and an external vertical magnetic field, when the aspect ratio of the elliptic streamlines tends to infinite. In this paper we unveil the link between these previously unconnected mechanisms, explaining both the MEI and the MRI as different manifestations of the same magneto-elliptic-rotational instability (MERI). The growth rates are found and the influence of the magnetic and rotational effects is explained, in particular the effect of the magnetic field on the range of negative Rossby numbers at which the horizontal instability is excited. Furthermore, we show how the horizontal rotational MEI in the rotating shear flow limit is linked to the MRI by the use of the local shearing box model, typically used in the study of accretion discs. In such a limit the growth rates of the two instability types coincide for any power-law-type background angular velocity radial profile with negative exponent corresponding to the value of the Rossby number of the rotating shear flow. The MRI requirement for instability is that the background angular velocity profile is a decreasing function of the distance from the centre of the disc, which corresponds to the horizontal rotational MEI requirement of negative Rossby numbers. Finally a physical interpretation of the horizontal instability, based on a balance between the strain, the Lorentz force and the Coriolis force, is given.
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Rao, Gaofeng, Hui Gao, Xiaoyang Wang, Jinchao Zhang, Miaoqing Ye, and Liyuan Rao. "MRI measurements of brain hippocampus volume in relation to mild cognitive impairment and Alzheimer disease: A systematic review and meta-analysis." Medicine 102, no. 36 (September 8, 2023): e34997. http://dx.doi.org/10.1097/md.0000000000034997.

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Background: This is the first meta-analysis conducted to compare the hippocampal volume measured by magnetic resonance imaging (MRI) in healthy normal subjects, mild cognitive impairment (MCI) and Alzheimer disease (AD), and to analyze the relationship between hippocampal volume changes and MCI and AD. Methods: English literatures published from January 2004 to December 2006 were extracted from PubMed, Embase, Wanfang Medical, and China National Knowledge Infrastructure databases. Statistical analysis was carried out with Stata/SE 16.0 software. Results: The smaller the volume of the hippocampus measured by MRI, the more severe the cognitive impairment or AD. Different MRI post-measurement correction methods have different measurement results: Left hippocampal volume measured by MRI Raw volume method is negatively correlated with MCI and AD (OR [odds ratio] = 0.58, 95%CI [confidence interval]: 0.42, 0.75) right hippocampal volume measured was not associated with MCI OR AD (OR = 0.87, 95%CI: 0.56, 1.18); left hippocampal volume measured by MRI total intracranial volume (TIV) Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampal volume was not associated with MCI OR AD (OR = 0.81, 95%CI: 0.49, 1.12); left hippocampal volume measured by MRI TIV Correction was not associated with MCI and AD (OR = 0.90, 95%CI: 0.62, 1.19), measured right hippocampus volume was negatively associated with MCI and AD (OR = 0.49, 95%CI: 0.35, 0.62). Conclusion: The shrinkage of hippocampus volume is closely related to MCI and AD. MRI measurement of hippocampus volume is not only an auxiliary diagnostic tool for MCI and AD, but also a good prognosis assessment tool.
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Piccardo, Arnoldo, Francesco Paparo, Riccardo Picazzo, Mehrdad Naseri, Paolo Ricci, Andrea Marziano, Lorenzo Bacigalupo, et al. "Value of Fused18F-Choline-PET/MRI to Evaluate Prostate Cancer Relapse in Patients Showing Biochemical Recurrence after EBRT: Preliminary Results." BioMed Research International 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/103718.

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Purpose. We compared the accuracy of18F-Choline-PET/MRI with that of multiparametric MRI (mMRI),18F-Choline-PET/CT,18F-Fluoride-PET/CT, and contrast-enhanced CT (CeCT) in detecting relapse in patients with suspected relapse of prostate cancer (PC) after external beam radiotherapy (EBRT). We assessed the association between standard uptake value (SUV) and apparent diffusion coefficient (ADC).Methods. We evaluated 21 patients with biochemical relapse after EBRT. Patients underwent18F-Choline-PET/contrast-enhanced (Ce)CT,18F-Fluoride-PET/CT, and mMRI. Imaging coregistration of PET and mMRI was performed.Results.18F-Choline-PET/MRI was positive in 18/21 patients, with a detection rate (DR) of 86%. DRs of18F-Choline-PET/CT, CeCT, and mMRI were 76%, 43%, and 81%, respectively. In terms of DR the only significant difference was between18F-Choline-PET/MRI and CeCT. On lesion-based analysis, the accuracy of18F-Choline-PET/MRI,18F-Choline-PET/CT, CeCT, and mMRI was 99%, 95%, 70%, and 85%, respectively. Accuracy, sensitivity, and NPV of18F-Choline-PET/MRI were significantly higher than those of both mMRI and CeCT. On whole-body assessment of bone metastases, the sensitivity of18F-Choline-PET/CT and18F-Fluoride-PET/CT was significantly higher than that of CeCT. Regarding local and lymph node relapse, we found a significant inverse correlation between ADC and SUV-max.Conclusion.18F-Choline-PET/MRI is a promising technique in detecting PC relapse.
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Park, Bogyeom, Yuwon Kim, Jinseok Park, Hojin Choi, Seong-Eun Kim, Hokyoung Ryu, and Kyoungwon Seo. "Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study." Journal of Medical Internet Research 26 (April 17, 2024): e54538. http://dx.doi.org/10.2196/54538.

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Background Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. Objective We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. Methods The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. Results The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). Conclusions The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.
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Cao, Ping, Jie Gao, and Zuping Zhang. "Multi-View Based Multi-Model Learning for MCI Diagnosis." Brain Sciences 10, no. 3 (March 20, 2020): 181. http://dx.doi.org/10.3390/brainsci10030181.

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Mild cognitive impairment (MCI) is the early stage of Alzheimer’s disease (AD). Automatic diagnosis of MCI by magnetic resonance imaging (MRI) images has been the focus of research in recent years. Furthermore, deep learning models based on 2D view and 3D view have been widely used in the diagnosis of MCI. The deep learning architecture can capture anatomical changes in the brain from MRI scans to extract the underlying features of brain disease. In this paper, we propose a multi-view based multi-model (MVMM) learning framework, which effectively combines the local information of 2D images with the global information of 3D images. First, we select some 2D slices from MRI images and extract the features representing 2D local information. Then, we combine them with the features representing 3D global information learned from 3D images to train the MVMM learning framework. We evaluate our model on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our proposed model can effectively recognize MCI through MRI images (accuracy of 87.50% for MCI/HC and accuracy of 83.18% for MCI/AD).
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Li, Ying, Yixian Fang, Jiankun Wang, Huaxiang Zhang, and Bin Hu. "Biomarker Extraction Based on Subspace Learning for the Prediction of Mild Cognitive Impairment Conversion." BioMed Research International 2021 (September 2, 2021): 1–12. http://dx.doi.org/10.1155/2021/5531940.

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Accurate recognition of progressive mild cognitive impairment (MCI) is helpful to reduce the risk of developing Alzheimer’s disease (AD). However, it is still challenging to extract effective biomarkers from multivariate brain structural magnetic resonance imaging (MRI) features to accurately differentiate the progressive MCI from stable MCI. We develop novel biomarkers by combining subspace learning methods with the information of AD as well as normal control (NC) subjects for the prediction of MCI conversion using multivariate structural MRI data. Specifically, we first learn two projection matrices to map multivariate structural MRI data into a common label subspace for AD and NC subjects, where the original data structure and the one-to-one correspondence between multiple variables are kept as much as possible. Afterwards, the multivariate structural MRI features of MCI subjects are mapped into a common subspace according to the projection matrices. We then perform the self-weighted operation and weighted fusion on the features in common subspace to extract the novel biomarkers for MCI subjects. The proposed biomarkers are tested on Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Experimental results indicate that our proposed biomarkers outperform the competing biomarkers on the discrimination between progressive MCI and stable MCI. And the improvement from the proposed biomarkers is not limited to a particular classifier. Moreover, the results also confirm that the information of AD and NC subjects is conducive to predicting conversion from MCI to AD. In conclusion, we find a good representation of brain features from high-dimensional MRI data, which exhibits promising performance for predicting conversion from MCI to AD.
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Wegner, Franz, Kerstin Lüdtke-Buzug, Sjef Cremers, Thomas Friedrich, Malte M. Sieren, Julian Haegele, Martin A. Koch, et al. "Bimodal Interventional Instrument Markers for Magnetic Particle Imaging and Magnetic Resonance Imaging—A Proof-of-Concept Study." Nanomaterials 12, no. 10 (May 21, 2022): 1758. http://dx.doi.org/10.3390/nano12101758.

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The purpose of this work was to develop instrument markers that are visible in both magnetic particle imaging (MPI) and magnetic resonance imaging (MRI). The instrument markers were based on two different magnetic nanoparticle types (synthesized in-house KLB and commercial Bayoxide E8706). Coatings containing one of both particle types were fabricated and measured with a magnetic particle spectrometer (MPS) to estimate their MPI performance. Coatings based on both particle types were then applied on a segment of a nonmetallic guidewire. Imaging experiments were conducted using a commercial, preclinical MPI scanner and a preclinical 1 tesla MRI system. MPI image reconstruction was performed based on system matrices measured with dried KLB and Bayoxide E8706 coatings. The bimodal markers were clearly visible in both methods. They caused circular signal voids in MRI and areas of high signal intensity in MPI. Both the signal voids as well as the areas of high signal intensity were larger than the real marker size. Images that were reconstructed with a Bayoxide E8706 system matrix did not show sufficient MPI signal. Instrument markers with bimodal visibility are essential for the perspective of monitoring cardiovascular interventions with MPI/MRI hybrid systems.
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Meyer, John S., Minh Quach, John Thornby, Munir Chowdhury, and Juebin Huang. "MRI identifies MCI subtypes: vascular versus neurodegenerative." Journal of the Neurological Sciences 229-230 (March 2005): 121–29. http://dx.doi.org/10.1016/j.jns.2004.11.012.

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Holm, Bill. "MRI." Annals of Internal Medicine 140, no. 7 (April 6, 2004): 576. http://dx.doi.org/10.7326/0003-4819-140-7-200404060-00021.

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Dissertations / Theses on the topic "MRI"

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Peterson, Erika. "Synthetic MRI for visualization of quantitative MRI." Thesis, Linköpings universitet, Avdelningen för radiologiska vetenskaper, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-102651.

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Magnetic resonance imaging (MRI) is an imaging technique that is used in hospitals worldwide. The images are acquired through the use of an MRI scanner and the clinical information is provided through the image contrast, which is based on the magnetic properties in biological tissue. By altering the scanner settings, images with different contrast properties can be obtained. Conventional MRI is a qualitative imaging technique and no absolute measurements are performed. At Center for Medical Imaging and Visualization (CMIV) researchers are developing a new MRI technique named synthetic MRI (SyMRI). SyMRI is based on quantitative measurements of data and absolute values of the magnetic properties of the biological tissue can be obtained. The purpose of this master thesis has been to take the development of SyMRI a step further by developing and implementing a visualization studio for SyMRI imaging of the human brain. The software, SyMRI Brain Studio, is intended to be used in clinical routine. Input from radiologists was used to evaluate the imaging technique and the software. Additionally, the requirements of the radiologists were converted into technical specifications for the imaging technique and SyMRI Brain Studio. Additionally, validation of the potential in terms of replacing conventional MRI with SyMRI Brain Studio was performed. The work resulted in visualization software that provides a solid formation for the future development of SyMRI Brain Studio into a clinical tool that can be used for validation and research purposes. A list of suggestions for the future developments is also presented. Future clinical evaluation, technical improvements and research are required in order to estimate the potential of SyMRI and to introduce the technique as a generally used clinical tool.
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Avdeev, S. "Functional MRI." Thesis, Sumy State University, 2014. http://essuir.sumdu.edu.ua/handle/123456789/45414.

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Medical technology has advanced in recent years. Now one can dissect the body in the finest photos and create three-dimensional models of organs and tissues with the help of scanning technology to find in malfunction and to diagnose diseases. Nevertheless, a relatively new type of scanning technology called functional magnetic resonance imaging (FMRI) raises this technology one step further. FMRI can not only help to diagnose diseases of the brain, but allows doctors to get into our mental processes to determine the way we think and feel. Functional MRI can detect even if we tell the truth.
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Lustig, Michael. "Sparse MRI /." May be available electronically:, 2008. http://proquest.umi.com/login?COPT=REJTPTU1MTUmSU5UPTAmVkVSPTI=&clientId=12498.

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King, P. J. "Kinetic studies on the MRI/MRII equilibrium of bovine rhodopsin." Thesis, University of Bristol, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.356301.

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Filos, Jason. "Parallel Transmission MRI." Thesis, Imperial College London, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516789.

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Kaka, Shwan. "Advanced functional MRI." Thesis, University of Sheffield, 2015. http://etheses.whiterose.ac.uk/9962/.

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Functional Magnetic Resonance Imaging (fMRI) has been widely used to study the responses of the somatosensory cortex, motor cortex and associated neuronal activity in the human cerebral cortex. fMRI is a non-invasive and indirect method for mapping brain activity through measurement of the hemodynamic responses associated with electrical neuronal activity and the neural activity leads directly to changes in blood flow, blood volume and the cerebral metabolic rate of oxygen consumption. Non-invasive neuroimaging technologies such as functional MRI have both advantages, such as good spatial resolution, and disadvantages, such as poor temporal resolution. Some of the disadvantages have been alleviated by incorporating other techniques such as optical spectroscopy or electroencephalography (EEG) which are also non-invasive. All these techniques are sensitive to the vascular response of neuronal activity but in addition we are now investigating the existence of a weak direct electromagnetic effect with advanced fMRI. This neuronal current effect which gives rise to main magnetic field modulation should provide additional information for studying nerve characteristics. In this thesis, methods for fMRI mapping of responses from phantoms, the median nerve, the visual system, the motor sensory cortex and the thalamus are optimised and subsequently quantified. The experimental results strongly support the main hypothesis of the thesis and suggest that the generated magnetic field due to ionic current can be detected by present generation MRI using specific experimental designs and stimulation paradigms. Overall our results show that ionic currents in subjects can generate percentage signal changes in MRI up to 0.1± 0.01% corresponding to mean magnetic axonal fields of 0.7± 0.1nT with a Signal to Noise Ratio (SNR) of 3:1. The responses of the median nerve, motor sensory cortex and thalamus were detected using transcutaneous electrical nerve stimulation (TENS) and the visual cortex using strobe light stimulation in the range of frequencies 2.1 Hz to 4.1 Hz. All these measurements were acquired at 1.5T. Fast fMRI experiments using TENS and finger tapping were also acquired simultaneously. In addition, real and imaginary finger tapping experiments were performed in the motor sensory cortex at 3T. Our results imply that axonal fields that are generated due to action potentials can generate effects on MRI sensitive enough to directly detect neuronal activity using advanced fMRI, although sensitivity is still not fully adequate for clinical use.
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Kořínek, Radim. "Rychlé MRI metody." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218408.

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This thesis deals with comparison of rapid and conventional methods used in MRI (Magnetic Resonance Imaging). There is a description of imaging methods such as EPI (Echo Planar Imaging), Ultra-fast GRE, FSE (Fast spin echo) as well as a snapshot-FLASH and FISP (Fast Imaging with Steady Precession). Experimental part of this thesis deals with the rapid FSE (Fast Spin Echo) method. Especially is explained and assembled an algorithm for proper compilation of data from the FSE method. This algorithm allows us to evaluate the images from the FSE method. This method is examined in detail (in terms of impact parameters) and compared with traditional conventional methods. Finally, the individual images are evaluated, and the best parameters for FSE method are identified.
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Dixon, Emma Charlotte. "Applications of MRI magnetic susceptibility mapping in PET-MRI brain studies." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10053515/.

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Magnetic susceptibility mapping (SM) uses magnetic resonance imaging (MRI) phase images to produce maps of the magnetic susceptibility (χ) of tissues. This work focuses on the applications of SM-based imaging to PET-MRI, the hybrid imaging modality which combines positron emission tomography (PET) with MRI. First, the potential of using SM to aid PET attenuation correction (AC) is explored. AC for PET-MRI is challenging as PET-MRI provides no information regarding the electron density of tissues. Recently proposed SM methods for calculating the χ in regions of no MRI signal are used to segment air, bone and soft tissue in order to create AC maps. In the head, SM methods are found to produce inferior air/bone segmentations to high-performing AC methods, but result in more accurate AC than ultrashort-echo (UTE)-based air/bone segmentations, and may be able to provide additional information in subjects with atypical anatomy. Secondly, a SM pipeline for inclusion in a PET-MRI study into biomarkers for Alzheimer’s disease (AD) is developed. In the Insight46 study 500 healthy subjects from the 1946 MRC National Survey of Health and Development are undergoing a comprehensive PET-MRI protocol at two time-points. SM processing methods are compared and optimised, and a method for processing images with oblique imaging planes is developed. The effect of using different tools for automated segmentation of regions of interest (ROIs) on reported regional χ values is analysed. The ROIs resulting from different tools are found to result in large differences in χ values. FIRST is chosen as the most appropriate ROI segmentation tool for this study based on anatomical accuracy as assessed by a neuroradiologist. Initial analysis of χ values from 100 subjects using data from the first time-point is carried out. No significant association with regional χ values is found for amyloid status, PET radiotracer uptake, or APOE genotype.
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Gerardin, Emilie. "Morphometry of the human hippocampus from MRI and conventional MRI high field." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00856589.

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The hippocampus is a gray matter structure in the temporal lobe that plays a key role in memory processes and in many diseases (Alzheimer's disease, epilepsy, depression ...).The development of morphometric models is essential for the study of the functional anatomy and structure alterations associated with different pathologies. The objective of this thesis is to develop and validate methods for morphometry of the hippocampus in two contexts: the study of the external shape of the hippocampus from conventional MRI (1.5T or 3T) with millimeter resolution, and the study of its internal structure from 7T MRI with high spatial resolution. These two settings correspond to the two main parts of the thesis.In the first part, we propose a method for the automatic classification of patients from shape descriptors. This method is based on a spherical harmonic decomposition which is combined with a support vector machine classifier (SVM). The method is evaluated in the context of automatic classification of patients with Alzheimer's disease (AD) patients, mild cognitive impairment (MCI) patients and healthy elderly subjects. It is also compared to other approaches and a more comprehensive validation is available in a population of 509 subjects from the ADNI database. Finally, we present another application of morphometry to study structural alterations associated with the syndrome of Gilles de la Tourette.The second part of the thesis is devoted to the morphometry of the internal structure of the hippocampus from MRI at 7 Tesla. Indeed, the internal structure of the hippocampus is rich and complex but inaccessible to conventional MRI. We first propose an atlas of the internal structure of the hippocampus from postmortem data acquired at 9.4T. Then, we propose to model the Ammon's horn and the subiculum as a skeleton and a local measure thickness. To do this, we introduce a variational method using original Hilbert spaces reproducing kernels. The method is validated on the postmortem atlas and evaluated on in vivo data from healthy subjects and patients with epilepsy acquired at 7T.
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Zrinzo, L. "MRI guided and MRI verified deep brain stimulation : accuracy, safety and efficacy." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1325642/.

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This thesis investigates a systematic approach to the use of MRI-guided and MRI-verified deep brain stimulation (DBS) in clinical practice. The concept of individual targeting of visualised brain structures without microelectrode recording (MER) was examined with respect to accuracy, safety and efficacy. Accurate MRI localisation of the pedunculopontine nucleus, an investigational new DBS target for parkinsonian gait disorders, is described and proof-of-principle confirmed in a cadaver study using MR-microscopy and histological examination. The impact of surgical trajectory on stereotactic accuracy in routine clinical practice was examined at two centres using MER: trajectories involving the ventricle suffered from significantly greater targeting errors compared to those that did not (p<0.001) and multiple brain-passes were more likely to be required to reach the intended target (p<0.01). Subcortical brain shift between pre and postoperative stereotactic images was minimal after MRI-verified procedures without MER (136 procedures); the observed shift did not adversely affect targeting accuracy or clinical outcome. A simple calibration process improved mean targeting errors by 0.6 mm (p<0.001) to 0.9 ± 0.5 mm from the intended target point. A large patient series was compared to a systematic literature review to determine factors associated with haemorrhage; an image-guided and image-verified approach carried a significantly lower risk of haemorrhage and associated permanent deficit than other surgical methodologies (p=0.001). Another study confirmed that, when observing certain precautions, cranial MR images could be obtained with an extremely low risk in patients with implanted DBS hardware. Outcome data from patients undergoing MRI-guided and MRI-verified surgery for Parkinson’s disease and primary generalised dystonia compared favourably to reports from the literature. Mode of anaesthesia did not impact on surgical outcome. In conclusion, this thesis demonstrates that a meticulous approach to MRI-guided and MRIverified DBS is safe and accurate, with clinical outcomes comparable to other techniques.
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Books on the topic "MRI"

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Brown, Mark A., and Richard C. Semelka. MRI. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2003. http://dx.doi.org/10.1002/0471467936.

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Rajan, Sunder S. MRI. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-1632-2.

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Prayer, Daniela. Fetal MRI. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2011.

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Prayer, Daniela, ed. Fetal MRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-540-73271-6.

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Danias, Peter G. Cardiovascular MRI. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-59745-511-4.

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Moonen, Chrit, and Peter A. Bandettini, eds. Functional MRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58716-0.

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Debatin, Jörg F., and Graeme C. McKinnon. Ultrafast MRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-80384-0.

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Hegazi, Tarek M., and Jim S. Wu. Musculoskeletal MRI. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26777-3.

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Carrio, Ignasi, and Pablo Ros, eds. PET/MRI. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40692-8.

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Pavone, Paolo, and Plinio Rossi, eds. Functional MRI. Milano: Springer Milan, 1996. http://dx.doi.org/10.1007/978-88-470-2194-5.

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Book chapters on the topic "MRI"

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Bigler, Erin D., Mark Allen, and Gary K. Stimac. "MRI and Functional MRI." In Neuroimaging in Forensic Psychiatry, 27–40. Chichester, UK: John Wiley & Sons, Ltd, 2012. http://dx.doi.org/10.1002/9781119968900.ch2.

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Singh, Harjit, and Janet A. Neutze. "MRI." In Radiology Fundamentals, 29–30. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0944-1_6.

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Iannarelli, Angelo, Stefano Badia, and Marco Rengo. "MRI." In Diagnostic Imaging for Thoracic Surgery, 75–89. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-89893-3_4.

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Khanfer, Riyad, John Ryan, Howard Aizenstein, Seema Mutti, David Busse, Ilona S. Yim, J. Rick Turner, et al. "MRI." In Encyclopedia of Behavioral Medicine, 1266. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1688.

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Singh, Harjit, Janet A. Neutze, and Jonathan R. Enterline. "MRI." In Radiology Fundamentals, 31–32. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10362-4_6.

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Jenkins, J. P. R. "MRI." In Imaging of the Foot & Ankle, 61–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-59363-5_4.

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Teh, William, and Konstantia Diana Stavrou. "MRI." In Oncoplastic Breast Surgery, 26–33. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781315115146-6.

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Schroeter, Aileen, Markus Rudin, Eliana Gianolio, Alessandra Viale, Daniela Delli Castelli, Silvio Aime, Jan-Bernd Hövener, et al. "MRI." In Small Animal Imaging, 227–324. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42202-2_13.

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Kissane, Jennifer, Janet A. Neutze, and Harjit Singh. "MRI." In Radiology Fundamentals, 33–35. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-22173-7_7.

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Hudson, Darren, and Christine Heales. "MRI." In Person-Centred Care in Radiology, 209–23. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003310143-20.

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Conference papers on the topic "MRI"

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Bilic, Anthony, and Chen Chen. "BC- MRI -SEG: A Breast Cancer MRI Tumor Segmentation Benchmark." In 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), 674–78. IEEE, 2024. http://dx.doi.org/10.1109/ichi61247.2024.00107.

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Ahmed, Shahzad, Feng Jinchao, Malik Abdul Manan, Muhammad Yaqub, Kebin Jia, and Zhonghua Sun. "ADLER-MRI: Adaptive Deep Learning for Enhanced MRI Reconstruction with Noise-Resilient Models." In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1–5. IEEE, 2024. http://dx.doi.org/10.1109/isbi56570.2024.10635153.

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Uray, Peter, DI Thomas Kienzl, and DI Ulf Marsche. "MRI." In ACM SIGGRAPH 2006 Emerging technologies. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1179133.1179158.

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Chen, Yasheng, Meher Juttukonda, Yue Z. Lee, Yi Su, Felipe Espinoza, Weili Lin, Dinggang Shen, David Lulash, and Hongyu An. "MRI based attenuation correction for PET/MRI via MRF segmentation and sparse regression estimated CT." In 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI 2014). IEEE, 2014. http://dx.doi.org/10.1109/isbi.2014.6868131.

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Sun, Hongmei, and Tianfu Wang. "LCG-MRF-Based Segmentation of MRI Brain Images." In 2008 International Conference on Computer Science and Information Technology (ICCSIT). IEEE, 2008. http://dx.doi.org/10.1109/iccsit.2008.102.

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"MRI SEGMENTATION USING MULTIFRACTAL ANALYSIS AND MRF MODELS." In Computer Vision Methods in Medicine. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002046901010106.

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Lincender-Cvijetić, Lidija. "FETAL MRI." In Međunarodni naučni simpozij FETALNA MEDICINA: OD LEONARDA DA VINCIJA DO DANAS. Akademija nauka i umjetnosti Bosne i Hercegovine, 2015. http://dx.doi.org/10.5644/pi2015-159.06.

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Iwasawa, Shoichiro, Kenji Mase, and Shigeo Morishima. "Mocap+MRI=?" In ACM SIGGRAPH 2004 Posters. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1186415.1186552.

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Zhao, Pu, Hong Pan, and Siyu Xia. "MRI-Trans-GAN: 3D MRI Cross-Modality Translation." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550256.

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Zhao, Mengyang, Xu Cao, Mingwei Zhou, Jinchao Feng, Luxi Xia, Brian W. Pogue, Keith D. Paulsen, and Shudong Jiang. "MRI-guided near-infrared spectroscopic tomography (MRg-NIRST): system development for wearable, simultaneous NIRS and MRI imaging." In Multimodal Biomedical Imaging XVII, edited by Fred S. Azar, Xavier Intes, and Qianqian Fang. SPIE, 2022. http://dx.doi.org/10.1117/12.2607983.

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Reports on the topic "MRI"

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Espy, Michelle A. Portable MRI. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1045388.

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Kasivisvanathan, Veeru. Transperineal MRI-Targeted Prostatic Biopsy. Touch Surgery Simulations, April 2015. http://dx.doi.org/10.18556/touchsurgery/2015.s0064.

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Chen, Weijie. Computerized Interpretation of Dynamic Breast MRI. Fort Belvoir, VA: Defense Technical Information Center, May 2004. http://dx.doi.org/10.21236/ada426322.

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Brody, David L. Advanced MRI in Acute Military TBI. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada613778.

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Brody, David L., and Christine L. MacDonald. Advanced MRI in Blast-Related TBI. Fort Belvoir, VA: Defense Technical Information Center, September 2009. http://dx.doi.org/10.21236/ada509239.

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Brody, David L. Advanced MRI in Acute Military TBI. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada574647.

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Brody, David L. Advanced MRI in Blast-related TBI. Fort Belvoir, VA: Defense Technical Information Center, July 2012. http://dx.doi.org/10.21236/ada581398.

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Rutt, Brian. Development of Quantitative Microvascular Breast MRI. Fort Belvoir, VA: Defense Technical Information Center, July 2000. http://dx.doi.org/10.21236/ada393160.

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Brody, David L. Advanced MRI in Acute Military TBI. Fort Belvoir, VA: Defense Technical Information Center, September 2013. http://dx.doi.org/10.21236/ada590498.

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Weiner, Michael W. 4 Tesla MRI for Neurodegenerative Diseases. Fort Belvoir, VA: Defense Technical Information Center, October 2005. http://dx.doi.org/10.21236/ada462064.

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