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Journal articles on the topic 'MRI'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 August 2024

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1

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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2

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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6

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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7

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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8

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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9

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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10

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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11

Reinig, James W., and Lucien M. Levy. "MRI." Journal of Computer Assisted Tomography 17, no. 4 (July 1993): 671. http://dx.doi.org/10.1097/00004728-199307000-00035.

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12

Das, Mahashweta, Sihem Amer-Yahia, Gautam Das, and Cong Yu. "MRI." Proceedings of the VLDB Endowment 4, no. 11 (August 2011): 1063–74. http://dx.doi.org/10.14778/3402707.3402742.

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13

Hendrick, R. Edward, Paul D. Russ, Jack H. Simon, and James F. Schlund. "MRI." Topics in Magnetic Resonance Imaging 6, no. 2 (1994): 147. http://dx.doi.org/10.1097/00002142-199400620-00008.

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14

HIBBELN, JOHN F., STEPHANIE M. SHORS, and SHARON E. BYRD. "MRI." Clinical Obstetrics and Gynecology 55, no. 1 (March 2012): 352–66. http://dx.doi.org/10.1097/grf.0b013e3182487d04.

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15

&NA;. "MRI." Plastic and Reconstructive Surgery 86, no. 1 (July 1990): 183. http://dx.doi.org/10.1097/00006534-199007000-00105.

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16

Silvestri, Dianne. "MRI." Families, Systems, & Health 34, no. 1 (2016): 68–69. http://dx.doi.org/10.1037/fsh0000180.

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17

Bydder, GM, JV Hajnal, and IR Young. "Mri." Clinical Imaging 22, no. 5 (September 1998): 383. http://dx.doi.org/10.1016/s0899-7071(98)00056-4.

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18

Kuhl, C. "MRI." Journal de Radiologie 90, no. 10 (October 2009): 1279. http://dx.doi.org/10.1016/s0221-0363(09)75101-7.

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19

Hall, Laurie. "MRI." Filtration & Separation 41, no. 3 (April 2004): 24–25. http://dx.doi.org/10.1016/s0015-1882(04)00145-4.

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20

Ohmura, Fumitoshi. "MRI." Back Letter 7, no. 6 (June 1992): 6. http://dx.doi.org/10.1097/00130561-199206000-00006.

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&NA;. "MRI." Back Letter 7, no. 8 (1992): 6. http://dx.doi.org/10.1097/00130561-199207080-00007.

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22

Nagendran, Jayan, and Evangelos Michelakis. "MRI." Chest 132, no. 1 (July 2007): 2–5. http://dx.doi.org/10.1378/chest.07-0563.

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23

Reisman, Anna B. "MRI." Annals of Internal Medicine 133, no. 4 (August 15, 2000): 262. http://dx.doi.org/10.7326/0003-4819-133-4-200008150-00008.

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&NA;. "MRI." Plastic and Reconstructive Surgery 90, no. 3 (September 1992): 548. http://dx.doi.org/10.1097/00006534-199209000-00095.

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25

Garg, Mandeep Kumar, Pankaj Gupta, Ritesh Agarwal, Kushaljit Singh Sodhi, and Niranjan Khandelwal. "MRI." Chest 147, no. 2 (February 2015): e58-e59. http://dx.doi.org/10.1378/chest.14-2347.

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Olanow, C. W. "MRI." Movement Disorders 7, S1 (1992): 21. http://dx.doi.org/10.1002/mds.870070522.

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Skillman, Judith. "MRI." JAMA 315, no. 13 (April 5, 2016): 1407. http://dx.doi.org/10.1001/jama.2016.2443.

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28

Zhu, Jianguo, Faming Zhang, Yun Luan, Peng Cao, Fei Liu, Wenwen He, and Dehang Wang. "Can Dynamic Contrast-Enhanced MRI (DCE-MRI) and Diffusion-Weighted MRI (DW-MRI) Evaluate Inflammation Disease." Medicine 95, no. 14 (April 2016): e3239. http://dx.doi.org/10.1097/md.0000000000003239.

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29

Ngo, Thi Thanh Tu, Manh Cuong Pham, Thuy Trang Dam, and Hong Hai Nguyen. "Value rapid magnetic resonance imaging protocol for detecting femoral head avascular necrosis in high risk patient." Vietnamese Journal of Radiology and Nuclear Medicine, no. 44 (January 8, 2022): 11–17. http://dx.doi.org/10.55046/vjrnm.44.3.2021.

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Purpose: Evaluate the agreement between limited MRI, which using T1W sequence or STIR sequence in coronal direction, with standard MRI in diagnosis early femoral head necrosis(FHN) occurring in high riskpatients.Subjects and methods: descriptive cross-sectional study was performed on 58 patients, who warediagnosed of femoral head osteonecrosis at stage 2 or higher according to the Arlet Ficat classification. The patients were performed hip joints MRI at the Radiology Center, Bach Mai Hospital from June 2020 to August 2021.Results: The agreement in FHN staggingbetween limited MRI using T1Wsequence, or limited MRI using STIR sequence with the standard MRI was 0.98 and 0.86, respectively. The agreement in measurement extent of osteonecrosis areabetween limited MRI using T1Wsequence, or limited MRI using STIR sequence with the standard MRI was 0.98 and 0.85.Conclusion: There was excellent agreement between the full and limited MR examinations both forstagging and determining the extent of osteonecrosis area. The time and potential cost reduction achieved when taking limited MRImay lead to more widespread using in patient care.

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Vasconcellos, Luiz Felipe, João Santos Pereira, Marcelo Adachi, Denise Greca, Manuela Cruz, Ana Lara Malak, Helenice Charchat-Fichman, and Mariana Spitz. "Correlation of MRI Visual Scales with Neuropsychological Profile in Mild Cognitive Impairment of Parkinson’s Disease." Parkinson's Disease 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/7380102.

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Few studies have evaluated magnetic resonance imaging (MRI) visual scales in Parkinson’s disease-Mild Cognitive Impairment (PD-MCI). We selected 79 PD patients and 92 controls (CO) to perform neurologic and neuropsychological evaluation. Brain MRI was performed to evaluate the following scales: Global Cortical Atrophy (GCA), Fazekas, and medial temporal atrophy (MTA). The analysis revealed that both PD groups (amnestic and nonamnestic) showed worse performance on several tests when compared to CO. Memory, executive function, and attention impairment were more severe in amnestic PD-MCI group. Overall analysis of frequency of MRI visual scales by MCI subtype did not reveal any statistically significant result. Statistically significant inverse correlation was observed between GCA scale and Mini-Mental Status Examination (MMSE), Montreal Cognitive Assessment (MoCA), semantic verbal fluency, Stroop test, figure memory test, trail making test (TMT) B, and Rey Auditory Verbal Learning Test (RAVLT). The MTA scale correlated with Stroop test and Fazekas scale with figure memory test, digit span, and Stroop test according to the subgroup evaluated. Visual scales by MRI in MCI should be evaluated by cognitive domain and might be more useful in more severely impaired MCI or dementia patients.

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Muhle, C., J. Brossmann, U. H. Melchert, C. Schröder, R. de Boer, R. P. Spielmann, and M. Meller. "Functional MRI of the patellofemoral joint: comparison of ultrafast MRI, motion-triggered cine MRI and static MRI." European Radiology 5, no. 4 (August 1995): 371–78. http://dx.doi.org/10.1007/bf00184946.

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32

J, Paredes, Prince T, Simpson A, and Daniel M. "A-06 MRI and Neuropsychological Change During Conversion from Normal/MCI to Alzheimer’s Disease." Archives of Clinical Neuropsychology 35, no. 6 (August 28, 2020): 796. http://dx.doi.org/10.1093/arclin/acaa068.06.

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Abstract Objective Analyze neurocognitive and structural brain changes associated with conversion from normal cognition/MCI to Alzheimer’s disease (AD). Method Thirty-two participants from the National Alzheimer’s Coordinating Center included 22 women; mean (SD): age = 77.06 (8.1); education = 14.59 (3.5). All had either normal cognition or MCI at first visit when MRI was obtained and were diagnosed with AD at follow-up MRI; mean time between MRI’s = 4.1 years. Imaging of Dementia & Aging lab performed calculations for MRI structural change using Linux-based software. Participants took neuropsychological tests within three months of each MRI visit. Results MRI structural degeneration occurred in: left (d = .46) and right (d = .47) entorhinal cortical thickness; left (d = .82) and right (d = .95) hippocampal volume; left (d = .74) and right (d = .43) middle temporal gray matter volume; left parahippocampal cortical thickness (d = .55); total white matter volume (d = .55); total brain volume (d = .78); and total CSF volume (d = 1.14). Significant neuropsychological decline included Animal fluency (d = 1.02), Vegetable fluency (d = .69), Digit Symbol (d = .53), Trails B (d = .42), and Digit Span Backward (d = .56). There was not a significant change in Logical Memory. Conclusions Participants who converted from normal cognition/MCI to AD showed MRI degeneration in medial temporal structures as well as generalized atrophy and white matter loss. These structural changes accompanied a significant decline in semantic verbal fluency, working memory, and processing speed. There was not a significant change in verbal memory.

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33

Kuroda, Kagayaki. "Interventional MRI." Journal of Japan Society of Computer Aided Surgery 6, no. 2 (2004): 75–78. http://dx.doi.org/10.5759/jscas1999.6.75.

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34

Tan, Jerry Hai Kok, Julia Schumacher, John-Paul Taylor, and Alan Thomas. "558 - Multimodal EEG-MRI in the diagnosis of mild cognitive impairment with lewybodies." International Psychogeriatrics 33, S1 (October 2021): 97–98. http://dx.doi.org/10.1017/s1041610221002532.

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Background:Differentiating mild cognitive impairment with Lewy bodies (MCI-LB) from mild cognitive impairment due to Alzheimer’s disease (MCI-AD) is challenging due to an overlap of symptoms. Quantitative EEG analyses have shown varying levels of diagnostic accuracy, while visual assessment of EEG may be a promising diagnostic method. Additionally, a multimodal EEG-MRI approach may have greater diagnostic utility than individual modalities alone.Research Objective:To evaluate the utility of (1) a structured visual EEG assessment and (2) a machine learning multimodal EEG-MRI approach to differentiate MCI-LB from MCI-AD.Method:300 seconds of eyes-closed, resting-state EEG from 37 MCI-LB and 36 MCI-AD patients were analysed. EEGs were visually assessed for the presence of diffuse, focal, and epileptiform abnormalities, overall grade of abnormalities and focal rhythmic delta activity (FIRDA). Random forest classifiers to discriminate MCI-LB from MCI-AD were trained on combinations of visual EEG, quantitative EEG and structural MRI features. Quantitative EEG features (dominant frequency, dominant frequency variability, theta/alpha ratio and measures of spectral power in the delta, theta, prealpha, alpha and beta bands) and structural MRI features (hippocampal and insular volumes) were obtained from previous analyses of our dataset.Results:Most patients had abnormal EEGs on visual assessment (MCI-LB = 91.9%, MCI-AD = 77.8%). Overall grade (Χ2 (73, 2) = 4.416, p = 0.110), diffuse abnormalities Χ2(73,1) = 3.790, p = 0.052, focal abnormalities Χ2 (73,1) = 3.113, p = 0.077 and FIRDA Χ2(73,1) = 0.862, p = 0.353 did not differ between groups. All multimodal classifiers had similar diagnostic accuracy (area underthe curve, AUC = 0.681 - 0.686) to a classifier that used quantitative EEG features only (AUC =0.668). The feature ‘beta power’ had the highest predictive power in all classifiers.Conclusion:Visual EEG assessment was unable to discriminate between MCI-LB and MCI-AD. However, future work with a more sensitive visual assessment score may yield more promising results.A multimodal EEG-MRI approach does not enhance the diagnostic value of quantitative EEG alone in diagnosing MCI-LB.(326 words)

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35

Grau-Ruiz, Daniel, Juan P. Rigla, Eduardo Pallás, José M. Algarín, José Borreguero, Rubén Bosch, Guillermo López-Comazzi, et al. "Magneto-stimulation limits in medical imaging applications with rapid field dynamics." Physics in Medicine & Biology 67, no. 4 (February 18, 2022): 045016. http://dx.doi.org/10.1088/1361-6560/ac515c.

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Abstract Objective. The goal of this work is to extend previous peripheral nerve stimulation (PNS) studies to scenarios relevant to magnetic particle imaging (MPI) and low-field magnetic resonance imaging (MRI), where field dynamics can evolve at kilo-hertz frequencies. Approach. We have constructed an apparatus for PNS threshold determination on a subject’s limb, capable of narrow and broad-band magnetic stimulation with pulse characteristic times down to 40 μs. Main result. From a first set of measurements on 51 volunteers, we conclude that the PNS dependence on pulse frequency/rise-time is compatible with traditional stimulation models where nervous responses are characterized by a rheobase and a chronaxie. Additionally, we have extended pulse length studies to these fast timescales and confirm thresholds increase significantly as trains transition from tens to a few pulses. We also look at the influence of field spatial distribution on PNS effects, and find that thresholds are higher in an approximately linearly inhomogeneous field (relevant to MRI) than in a rather homogeneous distribution (as in MPI). Significance. PNS constrains the clinical performance of MRI and MPI systems. Extensive magneto-stimulation studies have been carried out recently in the field of MPI, where typical operation frequencies range from single to tens of kilo-hertz. However, PNS literature is scarce for MRI in this fast regime, relevant to small (low inductance) dedicated MRI setups, and where the resonant character of MPI coils prevents studies of broad-band stimulation pulses. This work advances in this direction.

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Chepure, Ashish Hanmantrao, Alka A. Subramanyam, and Apurva Karmveer Ungratwar. "Can basic magnetic resonance imaging along with neuropsychological assessment be used as a cost-effective means for the detection of early dementia in the Indian sub-continent?" Journal of Geriatric Mental Health 10, no. 2 (2023): 60–68. http://dx.doi.org/10.4103/jgmh.jgmh_4_23.

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ABSTRACT Introduction: The mild cognitive impairment (MCI) stage occurs sporadically between healthy aging and the onset of Alzheimer’s disease (AD). MCI shows significant defacement in magnetic resonance imaging (MRI) of the brain along with neuropsychological and behavioral parameters. Aim: Assessment of neuropsychological, behavioral, and structural MRI changes in MCI and their relation with each other. Methodology: Seventy-eight participants in the MCI group and healthy controls (HC) were assessed using Addenbrooke’s Cognitive Examination (ACE), Mini-Mental Status Examination (MMSE), Clinical Dementia Rating Scale (CDR); and behavioral assessment by using Behavioral Pathology In AD (BEHAVE-AD). MRI brain volumetric analysis was performed using the software MYRIAN. Statistical analysis was performed using the Mann–Whitney U test, unpaired t-test (P = <0.05), and Spearman’s rank correlation. Results: MCI group showed significant impairments in ACE, MMSE, and CDR and significantly higher behavioral symptoms on BEHAVE-AD. Episodic memory had a significant positive correlation with normalized right hippocampal volume and total intracranial volume (TICV). Remote memory had a significant negative correlation with normalized left hippocampal volumes. Global CDR score had a moderately negative correlation with normalized right and left hippocampal volumes. Affective disturbances were negatively correlated with TICV. Conclusions: Comparable analysis of correctly scaled neuropsychological assessments may provide unbiased proxies for MRI-based measures of dementia risk.

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Sadowski, Elizabeth A., Katherine E. Maturen, Andrea Rockall, Caroline Reinhold, Helen Addley, Priyanka Jha, Nishat Bharwani, and Isabelle Thomassin-Naggara. "Ovary: MRI characterisation and O-RADS MRI." British Journal of Radiology 94, no. 1125 (September 1, 2021): 20210157. http://dx.doi.org/10.1259/bjr.20210157.

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Ultrasound has a high specificity for the diagnosis of a benign lesion in cases of classic appearing simple cyst, hemorrhagic cyst, endometrioma and dermoid. However, ultrasound can sometimes be limited for definitive characterisation and risk stratification of other types of lesions, including those with echogenic content that may appear solid, with or without blood flow. Frequently, MRI can be used to further characterise these types of lesions, due to its ability to distinguish solid tissue from non-tissue solid components such as fat, blood, or debris. Incorporating the MR imaging into the evaluation of adnexal lesions can improve diagnostic certainty and guide clinical management potentially avoiding inappropriate surgery for benign lesions and expediting appropriate treatment for malignant lesions, particularly in the females with sonographically indeterminate adnexal lesions.

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Weiss, Stan L. "MRI of the Body.Raven MRI Teaching File." Radiology 190, no. 3 (March 1994): 770. http://dx.doi.org/10.1148/radiology.190.3.770.

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Hiraga, Akira, Hiroaki Taki, Masato Hori, Syoji Naruse, Hiroshi Nishimura, and Tadatsune Ueshima. "414 Functional MRI using 0.2tesla MRI system." Japanese Journal of Radiological Technology 51, no. 10 (1995): 1484. http://dx.doi.org/10.6009/jjrt.kj00001353185.

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Stonier, G., and P. Hardee. "MRI safety: MRI and fixed orthodontic appliances." British Dental Journal 225, no. 8 (October 2018): 684. http://dx.doi.org/10.1038/sj.bdj.2018.935.

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Siegmann, K. "MRI and interventional MRI in breast cancer." Journal de Radiologie 89, no. 10 (October 2008): 1387. http://dx.doi.org/10.1016/s0221-0363(08)76192-4.

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Calamante, Fernando. "Perfusion MRI Using Dynamic-Susceptibility Contrast MRI." Topics in Magnetic Resonance Imaging 21, no. 2 (April 2010): 75–85. http://dx.doi.org/10.1097/rmr.0b013e31821e53f5.

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Mayerhoefer, Marius E., Stephen J. Archibald, Christina Messiou, Anton Staudenherz, Dominik Berzaczy, and Heiko Schöder. "MRI and PET/MRI in hematologic malignancies." Journal of Magnetic Resonance Imaging 51, no. 5 (July 2019): 1325–35. http://dx.doi.org/10.1002/jmri.26848.

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Eliassen, Carl Fredrik, Ivar Reinvang, Per Selnes, Tormod Fladby, and Erik Hessen. "Convergent Results from Neuropsychology and from Neuroimaging in Patients with Mild Cognitive Impairment." Dementia and Geriatric Cognitive Disorders 43, no. 3-4 (2017): 144–54. http://dx.doi.org/10.1159/000455832.

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Background/Aims: To investigate the correspondence between neuropsychological single measures and variation in fludeoxyglucose positron emission tomography (FDG PET) glucose metabolism and magnetic resonance imaging (MRI) cortical thickness in mild cognitive impairment (MCI) patients. Methods: Forty-two elderly controls and 73 MCI subjects underwent FDG PET and MRI scanning. Backward regression analyses with PET and MRI regions were used as dependent variables, while Rey Auditory Verbal Memory Test (RAVLT) recall, Trail Making Test B (TMT B), and a composite test score (RAVLT learning and immediate recall, TMT A, COWAT, and letter-number sequencing) were used as predictor variables. Results: The composite score predicted variation in cortical metabolism; supplementary analyses showed that TMT B was significantly correlated with PET metabolism as well. RAVLT and TMT B were significant predictors of variation in MRI cortical thickness. Conclusion: Our results indicate that RAVLT and TMT B are sensitive to variation in Alzheimer disease neuroimaging markers.

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Soman, Salil, Gregor Kasprian, Veronika Schopf, Vanessa Berger-Kulemann, Ursula Nemec, Christian Mitter, and Daniela Prayer. "Advanced fetal MRI: Diffusion tensor imaging, spectroscopy, dynamic MRI, resting-state functional MRI." Journal of Pediatric Neuroradiology 01, no. 03 (July 28, 2015): 225–51. http://dx.doi.org/10.3233/pnr-2012-031.

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Park, Ingyu, Sang-Kyu Lee, Hui-Chul Choi, Moo-Eob Ahn, Ohk-Hyun Ryu, Daehun Jang, Unjoo Lee, and Yeo Jin Kim. "Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images." Brain Sciences 14, no. 5 (May 9, 2024): 480. http://dx.doi.org/10.3390/brainsci14050480.

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In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

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Milek, David, Scott R. Echternacht, Dalton LaBarge, Jonnby LaGuardia, Howard N. Langstein, and Jonathan I. Leckenby. "PC16. PERIPHERAL NERVE REGENERATION IN MRI/MPI MICE." Plastic and Reconstructive Surgery - Global Open 10, no. 4S (April 1, 2022): 42–43. http://dx.doi.org/10.1097/01.gox.0000828316.98401.d0.

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Wang, Li-xue, Yi-zhe Wang, Chen-guang Han, Lei Zhao, Li He, and Jie Li. "Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis." Arquivos de Neuro-Psiquiatria 82, no. 08 (August 2024): 001–10. http://dx.doi.org/10.1055/s-0044-1788657.

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Abstract Background The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights. Objective A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models. Methods A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI. Results A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone. Conclusion Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

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Madusanka, Nuwan, Heung-Kook Choi, Jae-Hong So, Boo-Kyeong Choi, and Hyeon Gyun Park. "One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging." Current Medical Imaging Formerly Current Medical Imaging Reviews 15, no. 7 (August 26, 2019): 699–709. http://dx.doi.org/10.2174/1573405615666190327102052.

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Background: In this study, we investigated the effect of hippocampal subfield atrophy on the development of Alzheimer’s disease (AD) by analyzing baseline magnetic resonance images (MRI) and images collected over a one-year follow-up period. Previous studies have suggested that morphological changes to the hippocampus are involved in both normal ageing and the development of AD. The volume of the hippocampus is an authentic imaging biomarker for AD. However, the diverse relationship of anatomical and complex functional connectivity between different subfields implies that neurodegenerative disease could lead to differences between the atrophy rates of subfields. Therefore, morphometric measurements at subfield-level could provide stronger biomarkers. Methods: Hippocampal subfield atrophies are measured using MRI scans, taken at multiple time points, and shape-based normalization to a Montreal neurological institute (MNI) ICBM 152 nonlinear atlas. Ninety subjects were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and divided equally into Healthy Controls (HC), AD, and mild cognitive impairment (MCI) groups. These subjects underwent serial MRI studies at three time-points: baseline, 6 months and 12 months. Results: We analyzed the subfield-level hippocampal morphometric effects of normal ageing and AD based on radial distance mapping and volume measurements. We identified a general trend and observed the largest hippocampal subfield atrophies in the AD group. Atrophy of the bilateral CA1, CA2- CA4 and subiculum subfields was higher in the case of AD than in MCI and HC. We observed the highest rate of reduction in the total volume of the hippocampus, especially in the CA1 and subiculum regions, in the case of MCI. Conclusion: Our findings show that hippocampal subfield atrophy varies among the three study groups.

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Brickman, Adam M., Giuseppe Tosto, Jose Gutierrez, Howard Andrews, Yian Gu, Atul Narkhede, Batool Rizvi, et al. "An MRI measure of degenerative and cerebrovascular pathology in Alzheimer disease." Neurology 91, no. 15 (September 14, 2018): e1402-e1412. http://dx.doi.org/10.1212/wnl.0000000000006310.

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ObjectiveTo develop, replicate, and validate an MRI-based quantitative measure of both cerebrovascular and neurodegeneration in Alzheimer disease (AD) for clinical and potentially research purposes.MethodsWe used data from a cross-sectional and longitudinal community-based study of Medicare-eligible residents in northern Manhattan followed every 18–24 months (n = 1,175, mean age 78 years). White matter hyperintensities, infarcts, hippocampal volumes, and cortical thicknesses were quantified from MRI and combined to generate an MRI measure associated with episodic memory. The combined MRI measure was replicated and validated using autopsy data, clinical diagnoses, and CSF biomarkers and amyloid PET from the Alzheimer's Disease Neuroimaging Initiative.ResultsThe quantitative MRI measure was developed in a group of community participants (n = 690) and replicated in a similar second group (n = 485). Compared with healthy controls, the quantitative MRI measure was lower in patients with mild cognitive impairment and lower still in those with clinically diagnosed AD. The quantitative MRI measure correlated with neurofibrillary tangles, neuronal loss, atrophy, and infarcts at postmortem in an autopsy subset and was also associated with PET amyloid imaging and CSF levels of total tau, phosphorylated tau, and β-amyloid 42. The MRI measure predicted conversion to MCI and clinical AD among healthy controls.ConclusionWe developed, replicated, and validated an MRI measure of cerebrovascular and neurodegenerative pathologies that are associated with clinical and neuropathologic diagnosis of AD and related to established biomarkers.

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