Journal articles on the topic 'Brain Magnetic resonance imaging Statistical methods'
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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<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>
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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.
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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.
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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.
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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.
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Momen, Ali Akbar, Gholamreza Jelodar, and Hamid Dehdashti. "Brain Magnetic Resonance Imaging Findings in Developmentally Delayed Children." International Journal of Pediatrics 2011 (2011): 1–4. http://dx.doi.org/10.1155/2011/386984.
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Background. Developmental disorders are failure or inability to acquire various age-specific skills at expected maturational age, which affects about 5–10% of preschool children. One of the most important methods for evaluation of developmentally delayed children is neuroimaging, especially, brain magnetic resonance imaging (MRI) that provides useful information regarding brain tissue structures and anomalies.Method and Material. In this study, hospital records of 580 developmentally delayed children (aged 2 months to 15 years) who admitted in pediatric ward of Golestan Hospital from 1997 to 2009 were selected. Information such as age, MRI findings were collected in the questionnaire and statistically analyzed.Results. Total, 580 children including 333 males (57.4%) and 247 females (42.6%) were studied. Abnormal brain MRI was observed in 340 (58.6%) cases (204 Males, 136 females). The finding includes nonspecific in 38 (6.6%), congenital and developmental anomalies of brain in 39 (6.7%), recognizable syndromes in 3 (0.5%), neurovascular diseases or trauma in 218 (37.6%), and metabolic or neurodegenerative diseases in 42 (7.2%) cases.Conclusion. Because 60% of all study groups showed abnormal brain MRI, using this method could be effective in diagnosis, management, and almost prognosis determination processes.
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Koefman, Alex J., Melissa Licari, Michael Bynevelt, and Christopher R. P. Lind. "Functional magnetic resonance imaging evaluation of lumbosacral radiculopathic pain." Journal of Neurosurgery: Spine 25, no. 4 (October 2016): 517–22. http://dx.doi.org/10.3171/2016.3.spine151230.
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OBJECTIVE An objective biomarker for pain is yet to be established. Functional MRI (fMRI) is a promising neuroimaging technique that may reveal an objective radiological biomarker. The purpose of this study was to evaluate fMRI technology in the setting of lumbosacral radiculopathy and discuss its application in revealing a biomarker for pain in the future. METHODS A prospective, within-participant control study was conducted. Twenty participants with painful lumbosacral radiculopathy from intervertebral disc pathology were recruited. Functional imaging of the brain was performed during a randomly generated series of nonprovocative and provocative straight leg raise maneuvers. RESULTS With a statistical threshold set at p < 0.000001, 3 areas showed significant blood oxygen level–dependent (BOLD) signal change: right superior frontal gyrus (x = 2, y = 13, z = 48, k = 29, Brodmann area 6 [BA6]), left supramarginal cortex (x = −37, y = −44, z = 33, k = 1084, BA40), and left parietal cortex (x = −19, y = −41, z = 63, k = 354, BA5). With a statistical threshold set at p < 0.0002, 2 structures showed significant BOLD signal change: right putamen (x = 29, y = −11, z = 6, k = 72) and bilateral thalami (right: x = 23, y = −11, z = 21, k = 29; x = 8, y = −11, z = 9, k = 274; and left: x = −28, y = −32, z = 6, k = 21). CONCLUSIONS The results in this study compare with those in previous studies and suggest that fMRI technology can provide an objective assessment of the pain experience.
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PETERSON, BRADLEY S. "Conceptual, methodological, and statistical challenges in brain imaging studies of developmentally based psychopathologies." Development and Psychopathology 15, no. 3 (August 1, 2003): 811–32. http://dx.doi.org/10.1017/s0954579403000385.
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Brain imaging studies in developmentally based psychopathologies most often use magnetic resonance imaging (MRI) to study regional volumes, task-related activity, neurometabolite concentrations, or the paths of fiber tracts within the brain. Methodological challenges for the use of MRI in studying these disorders include understanding the ultrastructural correlates of brain structure and function that are below the limits of resolution of this imaging modality and developing better methods for approximating the anatomical boundaries of the cytoarchitectonic units that are defined by those ultrastructural characteristics. Conceptual challenges include distinguishing findings that represent pathophysiologically central causes from compensatory and epiphenomenal effects, a difficulty that stems directly from the inherently correlational nature of imaging data. The promise of functional imaging studies must capitalize on the specificity of the cognitive and behavioral probes that are used to illuminate core features of the pathophysiology of developmental disorders, while recognizing the assumptions and limitations of the subtraction paradigms that are used to isolate the brain functions of interest. Statistical challenges include incorporating adequate statistical models for scaling effects within the brain, as well as modeling important demographic correlates that contribute to the substantial interindividual variability inherent in most imaging data. Statistical analyses need to consider the substantial intercorrelation of measures across the brain and the importance of correcting for multiple statistical comparisons, as well as the need for improved methods for brain warping and for assessing effective connectivity in functional imaging studies.
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H., Smitha, Meena Devi V. N., Sreekanth K. S., and Vinoo Jacob. "Evaluation of MRI Orthogonal Planes in Differential Diagnosis of Non-Tumour Brain Lesions." Journal of Evolution of Medical and Dental Sciences 10, no. 40 (October 4, 2021): 3543–47. http://dx.doi.org/10.14260/jemds/2021/718.
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BACKGROUND Magnetic resonance imaging (MRI) provides structural characterization of brain lesions, by measuring volume of axial, sagittal and coronal planes through two dimensional slices. The purpose of this study was to characterize and identify the orthogonal imaging planes to detect non tumour lesions of brain through MRI. METHODS This study included 81 patients, both males and females, suspected of brain lesions and underwent MRI for diagnosis. The variations in the volume of the anatomical structures were measured and compared the planes as axial and sagittal, axial and coronal and coronal and sagittal for non-tumour brain lesions. RESULTS The present study revealed the differences in the measurement of volume in nontumour lesions (N = 81) in axial, sagittal and coronal planes. It was found that the volume of axial planes (9.2) is more dominant than the sagittal (9.1) and coronal planes (8.8) in non-tumour lesions. Statistical analysis was done by Statistical Package for Social Sciences (SPSS version 16 software). Two way/Friedman test were used for comparing the three groups. CONCLUSIONS This study concluded that, in most of the brain lesions irrespective of the type of tumours, axial planes helps more in the detection of tumour volume as compared to sagittal and coronal planes for precise diagnosis of brain lesions. KEY WORDS Axial Plane; Coronal Plane; Magnetic Resonance Imaging; Non-Tumour Brain Lesions; Sagittal Plane.
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Klineova, Sylvia, Rebecca Farber, Catarina Saiote, Colleen Farrell, Bradley N. Delman, Lawrence N. Tanenbaum, Joshua Friedman, Matilde Inglese, Fred D. Lublin, and Stephen Krieger. "Relationship between timed 25-foot walk and diffusion tensor imaging in multiple sclerosis." Multiple Sclerosis Journal - Experimental, Translational and Clinical 2 (January 1, 2016): 205521731665536. http://dx.doi.org/10.1177/2055217316655365.
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Objective/Background The majority of multiple sclerosis patients experience impaired walking ability, which impacts quality of life. Timed 25-foot walk is commonly used to gauge gait impairment but results can be broadly variable. Objective biological markers that correlate closely with patients’ disability are needed. Diffusion tensor imaging, quantifying fiber tract integrity, might provide such information. In this project we analyzed relationships between timed 25-foot walk, conventional and diffusion tensor imaging magnetic resonance imaging markers. Design/Methods A cohort of gait impaired multiple sclerosis patients underwent brain and cervical spinal cord magnetic resonance imaging. Diffusion tensor imaging mean diffusivity and fractional anisotropy were measured on the brain corticospinal tracts and spinal restricted field of vision at C2/3. We analyzed relationships between baseline timed 25-foot walk, conventional and diffusion tensor imaging magnetic resonance imaging markers. Results Multivariate linear regression analysis showed a statistically significant association between several magnetic resonance imaging and diffusion tensor imaging metrics and timed 25-foot walk: brain mean diffusivity corticospinal tracts (p = 0.004), brain corticospinal tracts axial and radial diffusivity (P = 0.004 and 0.02), grey matter volume (p = 0.05), white matter volume (p = 0.03) and normalized brain volume (P = 0.01). The linear regression model containing mean diffusivity corticospinal tracts and controlled for gait assistance was the best fit model (p = 0.004). Conclusions Our results suggest an association between diffusion tensor imaging metrics and gait impairment, evidenced by brain mean diffusivity corticospinal tracts and timed 25-foot walk.
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Jackson, Graeme D., Michael Makdissi, Mangor Pedersen, Donna M. Parker, Evan K. Curwood, Shawna Farquharson, Alan Connelly, David F. Abbott, and Paul McCrory. "Functional brain effects of acute concussion in Australian rules football players." Journal of Concussion 3 (January 2019): 205970021986120. http://dx.doi.org/10.1177/2059700219861200.
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Aim To determine whether acute sport-related concussion is associated with functional brain changes in Australian rules footballers. Methods Twenty acutely concussed professional Australian footballers were studied with 3 T magnetic resonance imaging and compared to 20 age-matched control subjects. We statistically compared whole-brain local functional magnetic resonance imaging connectivity between acutely concussed footballers and controls using voxel-wise permutation testing. Results The acutely concussed football players had significantly decreased local functional magnetic resonance imaging connectivity in the right dorsolateral prefrontal cortex, right inferior parietal lobe, and right anterior insula, compared to controls. No functional brain changes between groups within the default mode network were observed. Discussion Acutely concussed footballers had in common decreased functional connectivity within the right lateralized “cognitive control network” of the brain that is involved in executive functions, and the “salience network” involved in switching between tasks. Dysfunction of these brain regions is a plausible explanation for typical clinical features of concussion.
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Mishra, Hare Krishna, and Manpreet Kaur. "An Approach for Enhancement of MR Images of Brain Tumor." Traitement du Signal 39, no. 4 (August 31, 2022): 1133–44. http://dx.doi.org/10.18280/ts.390405.
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Magnetic Resonance Imaging plays an important role in diagnosing the brain tumor accurately, but it requires the approach to enhance the magnetic resonance images to assist physicians in brain tumor detection and making the treatment plan precisely to reduce the mortality rate. Therefore, in this proposed work, a comprehensive learning-based elephant herding optimization technique has been introduced to select the optimal value of smoothness factor in Bi-Histogram Equalization with Adaptive Sigmoid Function that enhances the visual quality as well as the appearance of the suspicious regions in magnetic resonance images. Further, the enhancement performance has been evaluated by the enhancement quality metrics. The metrics used include mean square error, peak signal to noise ratio, mean absolute error, structural similarity index metric, feature similarity index metric, Riesz transformed based feature similarity index metric, spectral residual-based similarity index metric, and absolute mean brightness error. The outcomes of this proposed work have a remarkable impact on enhancing magnetic resonance images and providing visual assistance for diagnosing brain tumors. The performance of the evaluation metrics is verified with Friedman's mean rank test, which strongly indicates a statistical difference between the proposed method and state-of-the-art methods.
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Mutch, W. Alan C., Michael J. Ellis, Lawrence N. Ryner, M. Ruth Graham, Brenden Dufault, Brian Gregson, Thomas Hall, Martin Bunge, and Marco Essig. "Brain magnetic resonance imaging CO2 stress testing in adolescent postconcussion syndrome." Journal of Neurosurgery 125, no. 3 (September 2016): 648–60. http://dx.doi.org/10.3171/2015.6.jns15972.
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OBJECT A neuroimaging assessment tool to visualize global and regional impairments in cerebral blood flow (CBF) and cerebrovascular responsiveness in individual patients with concussion remains elusive. Here the authors summarize the safety, feasibility, and results of brain CO2 stress testing in adolescents with postconcussion syndrome (PCS) and healthy controls. METHODS This study was approved by the Biomedical Research Ethics Board at the University of Manitoba. Fifteen adolescents with PCS and 17 healthy control subjects underwent anatomical MRI, pseudo-continuous arterial spin labeling MRI, and brain stress testing using controlled CO2 challenge and blood oxygen level–dependent (BOLD) MRI. Post hoc processing was performed using statistical parametric mapping to determine voxel-by-voxel regional resting CBF and cerebrovascular responsiveness of the brain to the CO2 stimulus (increase in BOLD signal) or the inverse (decrease in BOLD signal). Receiver operating characteristic (ROC) curves were generated to compare voxel counts categorized by control (0) or PCS (1). RESULTS Studies were well tolerated without any serious adverse events. Anatomical MRI was normal in all study participants. No differences in CO2 stimuli were seen between the 2 participant groups. No group differences in global mean CBF were detected between PCS patients and healthy controls. Patient-specific differences in mean regional CBF and CO2 BOLD responsiveness were observed in all PCS patients. The ROC curve analysis for brain regions manifesting a voxel response greater than and less than the control atlas (that is, abnormal voxel counts) produced an area under the curve of 0.87 (p < 0.0001) and 0.80 (p = 0.0003), respectively, consistent with a clinically useful predictive model. CONCLUSIONS Adolescent PCS is associated with patient-specific abnormalities in regional mean CBF and BOLD cerebrovascular responsiveness that occur in the setting of normal global resting CBF. Future prospective studies are warranted to examine the utility of brain MRI CO2 stress testing in the longitudinal assessment of acute sports-related concussion and PCS.
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Marchenko, N. V., V. B. Voitenkov, E. Yu Gorelik, M. A. Bedova, A. V. Klimkin, and D. A. Artemov. "Multiparameter magnetic resonance imaging in the diagnosis of herpes encephalitis in children." Diagnostic radiology and radiotherapy 12, no. 4 (January 19, 2022): 23–32. http://dx.doi.org/10.22328/2079-5343-2021-12-4-23-32.
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Introduction. This study is determined with the high prevalence of encephalitis in children, as well as the severe course and the possible disability. Herpes encephalitis occurs in almost half of cases of viral encephalitis in children. It is known that changes on the brain magnetic resonance imaging (MRI) in the acute stage of the disease are detected more often than on brain computed tomography (CT), but the clarification of this brain MRI changes is needed.Objectives of the study. To assess the features of brain multiparametric MRI changes in herpes encephalitis in children.Materials and methods. Two groups of children were examined, the first group included 25 children aged 6,0±4,8 years with laboratory-confirmed acute EH, the second group (control) — 23 children without signs of central nervous system damage at the comparable gender and age. Structural changes were assessed using MRI of the brain in T1-WI, T2-WI, Flair, T1-WI modes post contrast, DWI, DTI and MR spectroscopy.Results. In 40% of cases revealed panencephalitis, 36% — leukoencephalitis, 24% — polioencephalitis. Most often, the process involved the cerebral hemispheres — 72%, the brain stem — 44%, the thalamus — 40% and basal nuclei — 36%. In 52% of cases changes in DWI were found, in 20% of cases the lesions accumulated contrast agent. There was a statistically significant decrease of FA both in the focus and in the intact area in children with EH compared with the control group.Conclusion. The application of multiparametric MRI using DWI, DTI, and MR spectroscopy methods are statistically significant for the detection and assessment of focal brain lesions in children with herpes encephalitis.
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Panina, Yu S., A. N. Narkevich, and D. V. Dmitrenko. "Features of Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy in Patients with Mesial Temporal Lobe Epilepsy." Doctor.Ru 21, no. 4 (2022): 24–29. http://dx.doi.org/10.31550/1727-2378-2022-21-4-24-29.
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Study Objective: To study the features of neuroimaging indicators (magnetic resonance imaging (MRI) of the brain and magnetic resonance spectroscopy of the mediobasal temporal lobes) in patients with mesial temporal epilepsy (TLE). Study Design: A retrospective study. Materials and Methods. Brain MRI was analyzed in 166 patients with mesial TLE, and the results of MR spectroscopy of the mediobasal parts of the temporal lobes in 32 of them were additionally analyzed. There were 69 men (42%), 97 women (58%). The median age was 35 [29; 46] years. The age of onset of TLE is 19 [13; 30.5] years. The median duration of the disease is 11 [9; 20] years. Taking into account the peculiarities of neuroimaging for the analysis of metabolic changes, patients with TLE were divided into three groups: 1st — patients with TLE and hippocampal sclerosis (62 (37.3%) people); 2nd — patients without structural changes, according to MRI of the brain (MR-negative) (40 (24%) people); 3rd — patients with other structural changes of the brain (64 (38.7%) people, excluded from subsequent analysis due to heterogeneity of neuroradiological data). All patients underwent MRI of the brain with a magnetic field power of 1.5 Tesla in T1, T2, FLAIR, DWI and SWI modes. MR spectroscopy of the mediobasal parts of the temporal lobes was carried out with the study of the main metabolites: N-acetylaspartate, choline, creatine, lactate, glutamate-glutamine complex, myonositol. Only qualitative indicators characterizing a decrease or increase in the level of the metabolite, as well as the side of the lesion, were evaluated. Study Results. In patients with TLE and hippocampal sclerosis, focal neurological symptoms (p = 0.019) and interictal regional epileptiform activity (p = 0.002) were statistically significantly more often recorded, they were more likely to receive polytherapy with antiepileptic drugs (p = 0.022) than MR-negative patients. In 93.7% of patients with mesial temporal epilepsy, regardless of the etiology of the disease, one-/two-sided changes in the levels of N-acetylaspartate and other major metabolites were registered, according to MR spectroscopy of the mediobasal temporal lobes. Conclusion. MR spectroscopy of the mediobasal temporal lobes is a sensitive method for diagnosing metabolic disorders in patients with TLE. Keywords: temporal lobe epilepsy, magnetic resonance imaging of the brain, magnetic resonance spectroscopy, hippocampal sclerosis.
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Zhao, Fang, Li-qing Sun, Yi-mei Tian, Liu-mei Xu, Pu-xuan Lu, Xian Tang, Ying-xia Liu, and Hui Wang. "The Diagnostic Value of Brain Magnetic Resonance Imaging in Detecting CNS Diseases Among Advanced AIDS Patients." Infection International 3, no. 4 (December 1, 2014): 173–78. http://dx.doi.org/10.1515/ii-2017-0093.
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Abstract Objective To investigate the diagnostic value of brain magnetic resonance imaging in detecting central nervous system diseases among AIDS patients of different levels of T cells. Methods Total of 164 AIDS patients who did not receive antiviral treatment were divided into 2 groups according to their baseline CD4+ T cell counts. Group A had CD4+ T cell below or equal to 50 cells/μl (n = 81) and group B had CD4+ T cells over 50 cells/μl (n = 83). All patients underwent brain MRI scan. Imaging analysis and the prevalence of the central nervous system disorders were compared between two groups. Results Among them 48 cases were found of abnormal brain MRI, group A was higher than group B (35.8% vs. 22.9%) although without statistical significance (P = 0.065). Altogether 48 cases were diagnosed as AIDS related central nervous system disorders based on clinical symptoms, signs and laboratory findings. The prevalence of CNS disorders was higher in group A than in group B (41.9% vs. 16.8%) with statistical significance (P < 0.01). Conclusions The patients with CD4+ T cell count less than or equal to 50 cells/μl had high prevalence of CNS diseases. Brain MRI plays an important role in the diagnosis and differentiation of CNS diseases in advanced AIDS patients. This study suggests patients with low CD4+ T cell count (≤ 50/μl) should routinely undergo MRI examination.
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Stosic-Opincal, T. L., M. V. Macvanski, S. S. Gavrilovic, M. S. Gavrilov, D. S. Damjanovic, B. D. Vasic, and D. M. Grujicic. "Diffusion and perfusion magnetic resonance imaging in evaluation of primary glial brain tumors." Acta chirurgica Iugoslavica 56, no. 4 (2009): 25–30. http://dx.doi.org/10.2298/aci0904025s.
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Introduction: Diffusion (DWI) and perfusion (PWI) imaging can give important data about physiological characteristics of tissue, which complete morphologic findings from conventional MRI. The aim of this study is to estimate the value of these MRI technics in evaluation of primary glial brain tumors. Materials and methods: The significance of DWI and PWI in differentiation of histologically proven low- and high-grade gliomas was estimated in 48 patient with diagnosed brain gliomas. ADC and rCBV values were compared by application of Mann-Whitney test, and logistic regression analysis was used to determine which of these two parameters contributed the most in increasing the diagnostic accuracy, ia. its sensitivity, specificity and predictive velues. ROC curves were constructed to determine threshold values for differentiation of low- from highgrade lesions. Results: Statistical significance were showed between mean values of rCBV for low-grade (0,82) and high-grade (5,32) gliomas, which was not found for values of ADC parameters. Threshold rCBV value of 1,23 was determinated for discrimination between low- and high-grade gliomas with a sensitivity of 83,2% and a specificity of 77,5%. Conclusion: Conventional MRI combined with PWI increases the accuracy in determination of glioma grade.
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Kurata, Jiro, Keith R. Thulborn, Ferenc E. Gyulai, and Leonard L. Firestone. "Early Decay of Pain-related Cerebral Activation in Functional Magnetic Resonance Imaging." Anesthesiology 96, no. 1 (January 1, 2002): 35–44. http://dx.doi.org/10.1097/00000542-200201000-00012.
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Background Although pain-related activation was localized in multiple brain areas by functional imaging, the temporal profile of its signal has been poorly understood. The authors characterized the temporal evolution of such activation in comparison to that by conventional visual and motor tasks using functional magnetic resonance imaging. Methods Five right-handed volunteers underwent whole brain echo-planar imaging on a 3 T magnetic resonance imaging scanner while they received pain stimulus on the right and left forearm and performed visually guided saccade and finger tapping tasks. Pain stimulus on the right and left forearm consisted of four cycles of 15-s stimulus at 47.2-49.0 degrees C, interleaved with 30-s control at 32 degrees C, delivered by a Peltier-type thermode, and visually guided saccade and finger tapping of three cycles of 30-s active and 30-s rest conditions. Voxel-wise t statistical maps were standardized and averaged across subjects. Blood oxygenation level-dependent signal time courses were analyzed at local maxima of representative activation clusters (t > 3.5). Results Pain stimulus on the right forearm activated the secondary somatosensory (S2), superior temporal, anterior cingulate, insular, prefrontal cortices, premotor area, and lenticular nucleus. Pain stimulus on the left forearm activated similar but fewer areas at less signal intensity. The S2 activation was dominant on the contralateral hemisphere. Pain-related activation was statistically weaker and showed less consistent signal time courses than visually guided saccade- and finger tapping-related activation. Pain-related signals decayed earlier before the end of stimulus, in contrast to well-sustained signal plateaus induced by visually guided saccade and finger tapping. Conclusions The authors speculate that pain-related blood oxygenation level-dependent signals were attenuated by the pain-induced global cerebral blood flow decrease or activation of the descending pain inhibitory systems.
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Janauskaitë, Liuda, Justina Kaèerauskienë, Ugnë Jaðinskaitë, Vytautas Gedrimas, and Rimvydas Stropus. "Thickness of cerebral cortex measured using anatomical mesoscopic imaging and magnetic resonance imaging." Medicina 44, no. 2 (January 27, 2008): 126. http://dx.doi.org/10.3390/medicina44020016.
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Objective. Magnetic resonance imaging method opened up the possibility for in vivo examination of the anatomy of human brain. For this reason it is interesting and relevant to compare the knowledge accumulated over a number of years during the examination of the composition of dead brain to that obtained from magnetic resonance images. The aim of this study was to determine and compare the thickness of cerebral cortex in human of different age and sex, measured in different sites of the hemispheres when applying anatomical mesoscopic imaging and magnetic resonance imaging. Material and methods. The thickness of cerebral cortex was measured in symmetrical Brodmann’s areas of both hemispheres. The anatomical mesoscopic imaging technique was used for the examination of 2×2-cm cortex samples obtained during autopsy and fixed for 4 weeks in 10% paraformaldehyde. In these samples, cortex thickness was measured in sections perpendicular to the convolution, using an operative microscope, in a mesoscopic image at ×16 magnification and with an accuracy of 0.01 mm. Using cerebral magnetic resonance imaging, the thickness of cerebral cortex in live subjects was measured on T1-weighted images of patients examined at the Clinic of Radiology, Kaunas University of Medicine Hospital. The measured cortical field image was magnified to the smallest element of digital image – the pixel – and measured with an accuracy of 0.01 mm. Each of the two techniques was applied for the examination of 20 men and women who were divided into age groups of 20–60 years (n=10) and older than 60 years (n=10). Results and conclusions. Both examination methods yielded a statistically significant difference in the thickness of cerebral cortex between Brodmann’s areas 1, 4, and 19. No significant difference in cortex thickness was found between different age and sex groups; however, the findings showed that the difference in cortex thickness between the different age male groups was 4.6% and female – 1.6%. No significant difference using different techniques was found, but the cortex thickness in the fixed samples was reduced by 0.5 cm on average.
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Maryenko, N. I., and O. Yu Stepanenko. "Fractal dimension of phylogenetically different parts of the human cerebellum (magnetic resonance imaging study)." Reports of Morphology 26, no. 2 (October 12, 2020): 67–73. http://dx.doi.org/10.31393/morphology-journal-2020-26(2)-10.
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In recent years, fractal analysis has been increasingly used as a morphometric method, which allows to assess the complexity of the organization of quasi-fractal biological structures, including the cerebellum. The aim of the study was to determine the value of fractal dimension of phylogenetically different parts of the cerebellum by studying magnetic resonance imaging of the brain using the method of pixel dilation and to identify gender and age characteristics of individual variability of fractal dimension of the cerebellum and its external linear contour. The study was performed on the magnetic resonance images of the brain of 120 relatively healthy patients in age 18-86 years (65 women, 55 men). T2 weighted tomographic images were investigated. Fractal analysis was performed using the method of pixel dilation in the author’s modification. Fractal dimension (FD) values were determined for cerebellar tomographic images segmented with brightness values of 100 (FD100), 90 (FD90) and in the range of 100-90 (FD100-90 or fractal dimension of the outer cerebellar contour) in its upper and lower lobes, which include phylogenetically different zones. The obtained data were processed using generally accepted statistical methods. The average value of FD100 of the upper lobe of the cerebellum was 1.816±0.005, the lower lobe – 1.855±0.005. The average value of FD90 of the upper lobe of the cerebellum was 1.734±009, the lower lobe – 1.768±0.009. The average value of FD100-90 of the upper lobe of the cerebellum was 1.370±0.009, the lower lobe – 1.431±0.008. All three values of the fractal dimension of the lower lobe, which lobules have a lower phylogenetic age, are statistically significantly higher than the corresponding values of the fractal dimension of the upper lobe, have a more pronounced correlation with age than in the upper lobe. The developed research algorithm can be used to assess the condition of the cerebellum as an additional morphometric method during magnetic resonance imaging study of the brain.
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Chamberland, Maxime, Sila Genc, Chantal M. W. Tax, Dmitri Shastin, Kristin Koller, Erika P. Raven, Adam Cunningham, et al. "Detecting microstructural deviations in individuals with deep diffusion MRI tractometry." Nature Computational Science 1, no. 9 (September 2021): 598–606. http://dx.doi.org/10.1038/s43588-021-00126-8.
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AbstractMost diffusion magnetic resonance imaging studies of disease rely on statistical comparisons between large groups of patients and healthy participants to infer altered tissue states in the brain; however, clinical heterogeneity can greatly challenge their discriminative power. There is currently an unmet need to move away from the current approach of group-wise comparisons to methods with the sensitivity to detect altered tissue states at the individual level. This would ultimately enable the early detection and interpretation of microstructural abnormalities in individual patients, an important step towards personalized medicine in translational imaging. To this end, Detect was developed to advance diffusion magnetic resonance imaging tractometry towards single-patient analysis. By operating on the manifold of white-matter pathways and learning normative microstructural features, our framework captures idiosyncrasies in patterns along white-matter pathways. Our approach paves the way from traditional group-based comparisons to true personalized radiology, taking microstructural imaging from the bench to the bedside.
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de Almeida Martins, João P., Chantal M. W. Tax, Filip Szczepankiewicz, Derek K. Jones, Carl-Fredrik Westin, and Daniel Topgaard. "Transferring principles of solid-state and Laplace NMR to the field of in vivo brain MRI." Magnetic Resonance 1, no. 1 (February 28, 2020): 27–43. http://dx.doi.org/10.5194/mr-1-27-2020.
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Abstract. Magnetic resonance imaging (MRI) is the primary method for noninvasive investigations of the human brain in health, disease, and development but yields data that are difficult to interpret whenever the millimeter-scale voxels contain multiple microscopic tissue environments with different chemical and structural properties. We propose a novel MRI framework to quantify the microscopic heterogeneity of the living human brain as spatially resolved five-dimensional relaxation–diffusion distributions by augmenting a conventional diffusion-weighted imaging sequence with signal encoding principles from multidimensional solid-state nuclear magnetic resonance (NMR) spectroscopy, relaxation–diffusion correlation methods from Laplace NMR of porous media, and Monte Carlo data inversion. The high dimensionality of the distribution space allows resolution of multiple microscopic environments within each heterogeneous voxel as well as their individual characterization with novel statistical measures that combine the chemical sensitivity of the relaxation rates with the link between microstructure and the anisotropic diffusivity of tissue water. The proposed framework is demonstrated on a healthy volunteer using both exhaustive and clinically viable acquisition protocols.
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Eklund, Anders, Mats Andersson, and Hans Knutsson. "Fast Random Permutation Tests Enable Objective Evaluation of Methods for Single-Subject fMRI Analysis." International Journal of Biomedical Imaging 2011 (2011): 1–15. http://dx.doi.org/10.1155/2011/627947.
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Parametric statistical methods, such asZ-,t-, andF-values, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of non-parametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in single-subject fMRI analysis. In this work, it is shown how the computational power of cost-efficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutation-based approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level.
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Lang, K., S. Kloska, R. Straeter, C. H. Rickert, G. Goder, G. Kurlemann, A. Brentrup, O. Schober, and M. Weckesser. "Clinical value of amino acid imaging in paediatric brain tumours." Nuklearmedizin 44, no. 04 (2005): 131–36. http://dx.doi.org/10.1055/s-0038-1625755.
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Summary Purpose: To evaluate single photon emission computed tomography (SPECT) using the amino acid l-3-[123I]-α-methyl tyrosine (IMT) and contrast enhanced magnetic resonance imaging (MRI) as diagnostic tools in primary paediatric brain tumours in respect of non-invasive tumour grading. Patients, materials, methods: 45 children with primary brain tumours were retrospectively evaluated. IMT uptake was quantified as tumour/nontumour- ratio, a 4-value-scale was used to measure gadolinium enhancement on contrast enhanced MRI. Statistical analyses were performed to evaluate IMT uptake and gadolinium enhancement in low (WHO I/II) and high (WHO III/ IV) grade tumours and to disclose a potential relationship of IMT uptake to disruption of blood brain barrier as measured in corresponding MRI scans. Results: IMT uptake above background level was observed in 35 of 45 patients. IMT uptake was slightly higher in high grade tumours but the difference failed to attain statistical significance. Grading of individual tumours was neither possible by IMT SPECT nor by gadolinium enhanced MRI. Conclusion: IMT is accumulated in most brain tumours in children. Tumour grading was not possible using IMT or contrast enhancement as determined by MRI. Neither morphological nor functional imaging can replace histology in paediatric brain tumours.
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Alshammari, Qurain T., Mohammed Salih, Moawia Gameraddin, Mohamed Yousef, Bushra Abdelmalik, and Omer Loaz. "Accuracy of Magnetic Resonance Spectroscopy in Discrimination of Neoplastic and Non-Neoplastic Brain Lesions." Current Medical Imaging Formerly Current Medical Imaging Reviews 17, no. 7 (August 5, 2021): 904–10. http://dx.doi.org/10.2174/1573405617666210224112808.
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Background: Differentiation of brain lesions by conventional MRI alone is not enough. The introduction of sophisticated imaging methods, such as MR Spectroscopy (MRS), will contribute to accurate differentiation. Objective: To determine the diagnostic accuracy of MRS in differentiating neoplasm and non-neoplastic brain lesion. Methodology: This is a cross-sectional descriptive study conducted at Khartoum State from the period of 2015 to 2017. Thirty cases with brain lesions were included in the study investigated with MRS (Single-voxel spectroscopy) and conventional MRI. A comparison of MRS findings and histopathologic analysis was performed. The ratios of Cho/Cr and Cho/NAA were analyzed and compared between neoplastic and non-neoplastic brain masses. Data were analyzed using SPSS version 23. Results: Out of the 30 patients affected with brain lesions, there were 16 females and 14 males with a mean age of 44 +- 18 years. The ratios of Cho/Cr and Cho/NAA were higher in gliomas, astrocytoma, and meningioma than non-neoplastic lesions. Kappa statistical value (K) showed a good agreement between MRS and histopathological analysis (K= 0.60). The diagnostic accuracy of MRS was 100%, with 82.60% sensitivity, 85.71% specificity, 95% PPV, and 60% NPV. Conclusion: MRS has high diagnostic accuracy in differentiating neoplasm from non-neoplastic brain tumors. The elevation ratios of Choline-to- N-acetyl aspartate and choline-to- creatine can help neurosurgeons and clinicians differentiate benign from malignant masses.
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Yakhno, N. N., N. N. Koberskaya, V. A. Perepelov, D. S. Smirnov, V. I. Solodovnikov, M. I. Trufanov, and V. N. Gridin. "Hippocampal magnetic resonance imaging morphometry and neuropsychological parameters in patients with Alzheimer's disease." Neurology, Neuropsychiatry, Psychosomatics 11, no. 4 (December 8, 2019): 28–32. http://dx.doi.org/10.14412/2074-2711-2019-4-28-32.
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Alzheimer's disease (AD) is the leading cause of dementia in the population. Difficulties in diagnosing AD have served as an incentive for actively studying different current methods that increase the accuracy of diagnosis of the current neurodegenerative process in this disease. One of these areas is the post-processing of magnetic resonance imaging (MRI) data, by exactly calculating the volume of various anatomical formations, namely MRI morphometry. Objective: to determine the possible relationship between the results of evaluating the higher brain functions and the reduction in the hippocampal volume calculated by MRI morphometry in AD patients with mild and moderate dementia and in healthy controls. Patients and methods. Examinations were made in 41 AD patients aged 70.63±8.38 years with mild and moderate dementia and in healthy individuals. All study participants underwent neuropsychological testing that included the Mini-Mental State Examination (MMSE), the frontal lobe dysfunction battery (FLDB); immediate and delayed 12-word recall trials (12-word test); Benton's revised visual retention test; test of literal and categorical associations; clock drawing test; and series number test, Part A. MRI was performed on a Siemens Magnetom Skyra 3T MRI scanner. Statistical Parametric Mapping software was used to convert images and the volume of the hippocampus was estimated by FMRIB Software Library. Results and discussion. A statistically significant decrease in hippocampal volumes was established in patients with AD compared with healthy individuals. No statistically significant differences in hippocampal volumes were found in patients with varying degrees of dementia. Patients with mild and moderate dementia differed in all indicators of neuropsychological tests, with exception for the 12-word test and Benton's test. There was a statistically significant correlation of the total volume of the hippocampi with the indicators of MMSE, FLDB, 12- word test, clock drawing test, and test of categorical associations. Conclusion. Hippocampal MRI morphometry in combination with neuropsychological tests is an informative technique in the diagnosis of AD. There is a relationship between the degree of hippocampal atrophy and the neuropsychological characteristics of patients.
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Mirkov, Marta, and Ana Gavrovska. "Tumor detection using brain MRI and low-dimension co-occurrence feature approach." Serbian Journal of Electrical Engineering 19, no. 3 (2022): 273–89. http://dx.doi.org/10.2298/sjee2203273m.
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Research in medical imaging focuses on methods useful in computer-aided diagnosis systems. In modern times, these systems often have automatic detection of regions of interest, and imaging technologies offer numerous advantages, like the possibility of developing reliable assisting algorithms. Magnetic Resonance Imaging (MRI) provides compelling features for brain tumor detection due to good soft tissue contrast and has important clinical value. To help clinicians in making diagnoses, current algorithms for processing and medical image classification may depend on intricate deep learning designs that demand large hardware resources and lengthy execution times. This is with no doubt helpful in understanding disease mechanisms and in labeling difficult instances for brain tumor identification. On the other hand, statistical low-dimension feature sets including co-occurrence-based ones could be useful in dealing with tumor detection avoiding possible complexity. In this paper, statistical approaches for feature extraction and reduction are analyzed for MRI brain tumor classification, and the evaluation of the results is presented on one of the publicly available brain tumor detection database commonly used for machine learning tasks. Bayes and kNN classifiers are applied for the analysis, as well as four distance metrics, and two methods for feature reduction. The results seem promising in developing a simple and less hardware-demanding procedure.
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Brakus, Alma, Jelena Ostojic, and Milos Lucic. "Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation." Archive of Oncology, no. 00 (2022): 7. http://dx.doi.org/10.2298/aoo210828007b.
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Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.
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Roux, Franck-Emmanuel, Danielle Ibarrola, Michel Tremoulet, Yves Lazorthes, Patrice Henry, Jean-Christophe Sol, and Isabelle Berry. "Methodological and Technical Issues for Integrating Functional Magnetic Resonance Imaging Data in a Neuronavigational System." Neurosurgery 49, no. 5 (November 1, 2001): 1145–57. http://dx.doi.org/10.1097/00006123-200111000-00025.
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ABSTRACT OBJECTIVE The aim of this article was to analyze the technical and methodological issues resulting from the use of functional magnetic resonance image (fMRI) data in a frameless stereotactic device for brain tumor or pain surgery (chronic motor cortex stimulation). METHODS A total of 32 candidates, 26 for brain tumor surgery and six chronic motor cortex stimulation, were studied by fMRI scanning (61 procedures) and intraoperative cortical brain mapping under general anesthesia. The fMRI data obtained were analyzed with the Statistical Parametric Mapping 99 software, with an initial analysis threshold corresponding to P < 0.001. Subsequently, the fMRI data were registered in a frameless stereotactic neuronavigational device and correlated to brain mapping. RESULTS Correspondence between fMRI-activated areas and cortical mapping in primary motor areas was good in 28 patients (87%), although fMRI-activated areas were highly dependent on the choice of paradigms and analysis thresholds. Primary sensory- and secondary motor-activated areas were not correlated to cortical brain mapping. Functional mislocalization as a result of insufficient correction of the echo-planar distortion was identified in four patients (13%). Analysis thresholds (from P < 0.0001 to P < 10−12) more restrictive than the initial threshold (P < 0.001) had to be used in 25 of the 28 patients studied, so that fMRI motor data could be matched to cortical mapping spatial data. These analysis thresholds were not predictable preoperatively. Maximal tumor resection was accomplished in all patients with brain tumors. Chronic motor cortex electrode placement was successful in each patient (significant pain relief >50% on the visual analog pain scale). CONCLUSION In brain tumor surgery, fMRI data are helpful in surgical planning and guiding intraoperative brain mapping. The registration of fMRI data in anatomic slices or in the frameless stereotactic neuronavigational device, however, remained a potential source of functional mislocalization. Electrode placement for chronic motor cortex stimulation is a good indication to use fMRI data registered in a neuronavigational system and could replace somatosensory evoked potentials in detection of the central sulcus.
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Stufflebeam, Steven M., Hesheng Liu, Jorge Sepulcre, Naoaki Tanaka, Randy L. Buckner, and Joseph R. Madsen. "Localization of focal epileptic discharges using functional connectivity magnetic resonance imaging." Journal of Neurosurgery 114, no. 6 (June 2011): 1693–97. http://dx.doi.org/10.3171/2011.1.jns10482.
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Object In patients with medically refractory epilepsy the accurate localization of the seizure onset zone is critical for successful surgical treatment. The object of this study was to investigate whether the degree of coupling of spontaneous brain activity as measured with functional connectivity MR imaging (fcMR imaging) can accurately identify and localize epileptic discharges. Methods The authors studied 6 patients who underwent fcMR imaging presurgical mapping and subsequently underwent invasive electroencephalography. Results Focal regions of statistically significant increases in connectivity were identified in 5 patients when compared with an ad hoc normative sample of 300 controls. The foci identified by fcMR imaging overlapped the epileptogenic areas identified by invasive encephalography in all 5 patients. Conclusions These results suggest that fcMR imaging may provide an effective high–spatial resolution and noninvasive method of localizing epileptic discharges in patients with refractory epilepsy.
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Warren, Katherine E., Joseph A. Frank, Jeanette L. Black, Rene S. Hill, Josef H. Duyn, Alberta A. Aikin, Bobbi K. Lewis, Peter C. Adamson, and Frank M. Balis. "Proton Magnetic Resonance Spectroscopic Imaging in Children With Recurrent Primary Brain Tumors." Journal of Clinical Oncology 18, no. 5 (March 1, 2000): 1020. http://dx.doi.org/10.1200/jco.2000.18.5.1020.
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PURPOSE: Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a noninvasive technique for spatial characterization of biochemical markers in tissues. We measured the relative tumor concentrations of these biochemical markers in children with recurrent brain tumors and evaluated their potential prognostic significance. PATIENTS AND METHODS: 1H-MRSI was performed on 27 children with recurrent primary brain tumors referred to our institution for investigational drug trials. Diagnoses included high-grade glioma (n = 10), brainstem glioma (n = 7), medulloblastoma/peripheral neuroectodermal tumor (n = 6), ependymoma (n = 3), and pineal germinoma (n = 1). 1H-MRSI was performed on 1.5-T magnetic resonance imagers before treatment. The concentrations of choline (Cho) and N-acetyl-aspartate (NAA) in the tumor and normal brain were quantified using a multislice multivoxel method, and the maximum Cho:NAA ratio was determined for each patient’s tumor. RESULTS: The maximum Cho:NAA ratio ranged from 1.1 to 13.2 (median, 4.5); the Cho:NAA ratio in areas of normal-appearing brain tissue was less than 1.0. The maximum Cho:NAA ratio for each histologic subtype varied considerably; approximately equal numbers of patients within each tumor type had maximum Cho:NAA ratios above and below the median. Patients with a maximum Cho:NAA ratio greater than 4.5 had a median survival of 22 weeks, and all 13 patients died by 63 weeks. Patients with a Cho:NAA ratio less than or equal to 4.5 had a projected survival of more than 50% at 63 weeks. The difference was statistically significant (P = .0067, log-rank test). CONCLUSION: The maximum tumor Cho:NAA ratio seems to be predictive of outcome in children with recurrent primary brain tumors and should be evaluated as a prognostic indicator in newly diagnosed childhood brain tumors.
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Peck, Kyung K., Michelle Bradbury, Nicole Petrovich, Bob L. Hou, Nicole Ishill, Cameron Brennan, Viviane Tabar, and Andrei I. Holodny. "PRESURGICAL EVALUATION OF LANGUAGE USING FUNCTIONAL MAGNETIC RESONANCE IMAGING IN BRAIN TUMOR PATIENTS WITH PREVIOUS SURGERY." Neurosurgery 64, no. 4 (April 1, 2009): 644–53. http://dx.doi.org/10.1227/01.neu.0000339122.01957.0a.
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Abstract OBJECTIVE Functional magnetic resonance imaging (fMRI) is used to assess language laterality in preoperative brain tumor patients. In postsurgical patients, susceptibility artifacts can potentially alter ipsilateral fMRI activation volumes and the assessment of language laterality. The purpose of this study was to investigate the ability of fMRI to correctly measure language dominance in brain tumor patients with previous surgery because this patient cohort is vulnerable to type II statistical errors and subsequent misjudgment of laterality. METHODS Twenty-six right-handed patients with left-hemisphere gliomas (16 with and 10 without previous surgery) underwent preoperative language fMRI. Language laterality was measured using hemispheric and Broca's area regions of interest (ROIs). Hemisphere dominance, as established by laterality measurements, was compared with that determined by intraoperative electrocorticography and behavioral assessments. RESULTS Localization of primary language cortices was achieved in 24 of 26 patients studied. The hemisphere dominance evaluated by fMRI was verified by intraoperative corticography in only 14 patients (10 with and 4 without previous surgery), and only 12 of them had complete neuropsychological testing. Complete concordance of the laterality with intraoperative electrocorticography and behavioral assessments was found in patients without previous surgery. In patients with previous surgery, concordance was 75% using Broca's area ROI and 88% using hemispheric ROI, notwithstanding susceptibility artifacts. Differences in laterality between pre- and postsurgical patients, based on either hemispheric (P = 0.81) or Broca's area (P = 0.19) ROI measurements were not statistically significant. However, hemispheric ROI analyses were found to be less affected by postsurgical artifacts and may be more suitable for establishing hemisphere dominance. CONCLUSION fMRI mapping of eloquent language cortices in brain tumor patients after surgery is feasible and can serve as a useful baseline evaluation for preoperative neurosurgical planning. However, findings should be interpreted with caution in the presence of postsurgical artifacts.
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Zou, Kelly H., Hongyan Du, Shawn Sidharthan, Lisa M. DeTora, Yunmei Chen, Ann B. Ragin, Robert R. Edelman, and Ying Wu. "Statistical Evaluations of the Reproducibility and Reliability of 3-Tesla High Resolution Magnetization Transfer Brain Images: A Pilot Study on Healthy Subjects." International Journal of Biomedical Imaging 2010 (2010): 1–11. http://dx.doi.org/10.1155/2010/618747.
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Magnetization transfer imaging (MT) may have considerable promise for early detection and monitoring of subtle brain changes before they are apparent on conventional magnetic resonance images. At 3 Tesla (T), MT affords higher resolution and increased tissue contrast associated with macromolecules. The reliability and reproducibility of a new high-resolution MT strategy were assessed in brain images acquired from 9 healthy subjects. Repeated measures were taken for 12 brain regions of interest (ROIs): genu, splenium, and the left and right hemispheres of the hippocampus, caudate, putamen, thalamus, and cerebral white matter. Spearman's correlation coefficient, coefficient of variation, and intraclass correlation coefficient (ICC) were computed. Multivariate mixed-effects regression models were used to fit the mean ROI values and to test the significance of the effects due to region, subject, observer, time, and manual repetition. A sensitivity analysis of various model specifications and the corresponding ICCs was conducted. Our statistical methods may be generalized to many similar evaluative studies of the reliability and reproducibility of various imaging modalities.
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Yueniwati, Yuyun, Charles Wangsadjaja, Islana Gadis Yulidani, Sri Budhi Rianawati, and Harun Al Rasyid. "The Role of Brain Magnetic Resonance Imaging (MRI) as an Early Detector of Cognitive Impairment." Journal of Neurosciences in Rural Practice 09, no. 03 (July 2018): 350–53. http://dx.doi.org/10.4103/jnrp.jnrp_542_17.
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ABSTRACT Background: Along with the increase of the health and prosperity level will affect the life expectancy in Indonesia, there has also been an increase in degenerative disease cases. One of the problems arises is cognitive impairment. The mild version of this impairment is often associated with the increase risk that will eventually lead to dementia. Therefore, early detection of this impairment is necessary. Objective: This study is aimed at proving the correlation between Fazekas scale on brain MRI and MoCA-Ina score in defining the degree of cognitive impairment. Methods: This study employed observational analytic design and cross sectional study for its data collection method. The Fazekas scale on brain MRI of 32 patients was read by 3 radiologist, while the MoCA-Ina scoring was done by a competent neurologist. Both tests were done double blindly. Later on, the correlation between Fazekas scale and MoCA-Ina score would be assessed using Spearman Correlation. Results: Statistical calculation conducted using Spearman Correlation reveals that the coefficient is -0.519 with significant score (P) 0.002, which is smaller than α: 0.05. Therefore, it can be concluded that there is a strong negative correlation between Fazekas scale and MoCA-Ina score. Conclusion: Fazekas scale evaluation on brain MRI is necessary to be performed as it helps predicting the decline of one's cognitive function, so that an early therapy can be acted upon to prevent dementia in the future.
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Stevanovic, Aleksandar, Anja Stefanovic, Natasa Stojanovski, Gordana Tomic, Jasna Zidverc-Trajkovic, and Aleksandra Pavlovic. "Affective status in cerebral small vessel disease." Medical review 72, no. 9-10 (2019): 280–85. http://dx.doi.org/10.2298/mpns1910280s.
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Introduction. Cerebral small vessel disease is a neurological condition characterized by motor, cognitive and affective disorders, often found on brain magnetic resonance imaging scans in patients with vascular risk factors. Affective disorders may have a major impact on patients? quality of life, although they are often ignored as an entity in cerebrovascular pathology. Material and Methods. This prospective study included 80 patients with the diagnosis of cerebral small vessel disease admitted at the Clinic of Neurology, Clinical Center of Serbia in the period from January 1, 2017 to January 1, 2019. Baseline demographic data and brain magnetic resonance findings were obtained along with the results of cognitive function and affective status tests. Data were analyzed using standard statistical tests. Results. Standard screening tests revealed that 51.25% and 33.75% of our patients with cerebral small vessel disease suffer from apathy and depression, respectively. A significant correlation was found between the severity of white matter changes on magnetic resonance scans and apathy (p = 0.0092). Additionally, white matter changes were also significantly associated with depression (p = 0.021). Conclusion. Affective disorders are not uncommon in cerebral small vessel disease and apathy was the leading phenomenon among our patients. Since a strong correlation was detected between affective disorders and severity of vascular changes on magnetic resonance scans, we may conclude that both apathy and depression are key features of an underlying brain injury, rather than just comorbidity.
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Chen, Hsian-Min, Hung-Chieh Chen, Clayton Chi-Chang Chen, Yung-Chieh Chang, Yi-Ying Wu, Wen-Hsien Chen, Chiu-Chin Sung, Jyh-Wen Chai, and San-Kan Lee. "Comparison of Multispectral Image-Processing Methods for Brain Tissue Classification in BrainWeb Synthetic Data and Real MR Images." BioMed Research International 2021 (March 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/9820145.
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Accurate quantification of brain tissue is a fundamental and challenging task in neuroimaging. Over the past two decades, statistical parametric mapping (SPM) and FMRIB’s Automated Segmentation Tool (FAST) have been widely used to estimate gray matter (GM) and white matter (WM) volumes. However, they cannot reliably estimate cerebrospinal fluid (CSF) volumes. To address this problem, we developed the TRIO algorithm (TRIOA), a new magnetic resonance (MR) multispectral classification method. SPM8, SPM12, FAST, and the TRIOA were evaluated using the BrainWeb database and real magnetic resonance imaging (MRI) data. In this paper, the MR brain images of 140 healthy volunteers ( 51.5 ± 15.8 y / o ) were obtained using a whole-body 1.5 T MRI system (Aera, Siemens, Erlangen, Germany). Before classification, several preprocessing steps were performed, including skull stripping and motion and inhomogeneity correction. After extensive experimentation, the TRIOA was shown to be more effective than SPM and FAST. For real data, all test methods revealed that the participants aged 20–83 years exhibited an age-associated decline in GM and WM volume fractions. However, for CSF volume estimation, SPM8-s and SPM12-m both produced different results, which were also different compared with those obtained by FAST and the TRIOA. Furthermore, the TRIOA performed consistently better than both SPM and FAST for GM, WM, and CSF volume estimation. Compared with SPM and FAST, the proposed TRIOA showed more advantages by providing more accurate MR brain tissue classification and volume measurements, specifically in CSF volume estimation.
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Naveed, Muhammad Atif, Pradeep Goyal, Ajay Malhotra, Xiang Liu, Sonali Gupta, Manisha Mangla, and Rajiv Mangla. "Grading of oligodendroglial tumors of the brain with apparent diffusion coefficient, magnetic resonance spectroscopy, and dynamic susceptibility contrast imaging." Neuroradiology Journal 31, no. 4 (February 22, 2018): 379–85. http://dx.doi.org/10.1177/1971400918757217.
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Purpose We explored whether advanced magnetic resonance (MR) imaging techniques could grade oligodendrogliomas. Methods Forty patients (age 9–61 years) with oligodendroglial tumors were selected. There were 23 patients with World Health Organization grade II (group 1) and 17 patients with grade III (group 2) tumors. Apparent diffusion coefficient (ADC) maps were calculated by b values of 0 and 1000 s/mm2. Dynamic susceptibility contrast (DSC) images were obtained during the first pass of a bolus of gadolinium-based contrast. These data were post-processed and cerebral blood volume (CBV) maps and permeability (PS) were calculated. MR spectroscopy was acquired after drawing a region of interest on the tumor using two-dimensional chemical shift imaging. Statistical analysis was performed using SPSS software. Results When the rPSmax was combined with the rCBVmax, there was a significant difference between the two groups ( p ≤ 0.03) with area under the curve of 0.742 (95% CI: 0.412–0.904). rCBV, rADC, choline/creatine, and choline/NAA alone were able to differentiate between the two groups; however, they did not show any statistical difference with p values of ≤ 0.121, ≤ 0.722, and ≤ 0.582, respectively. A CBV PS product threshold of 0.53 provided a sensitivity of 80% and a specificity of 83.3% in detection of grade III tumors. Conclusion Combined rCBVmax and rPSmax can be utilized to grade oligodendrogliomas. ADC values, relative cerebral blood volume (rCBV), and MR spectroscopy alone can be utilized to differentiate between the two groups of oligodendrogliomas but without statistical significance.
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Pang, Yaowen, and Xiang Peng. "Analysis of Mental State of Patients after Drug Addiction and Withdrawal Guided by PETCT Image Based on Optimized Image Fusion Algorithm." Scientific Programming 2021 (October 4, 2021): 1–9. http://dx.doi.org/10.1155/2021/5943410.
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Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) studies have shown that drug-dependent patients are activated in different addictive brain areas under the stimulation of relevant environmental cues, which in turn leads to craving and relapse. This study uses magnetic resonance spectroscopy to measure brain temperature to explore the brain temperature changes in different addictive brain regions of heroin and methamphetamine addicts in a short-term withdrawal state and to explore whether the quantitative index of brain temperature change can be used as a diagnostic drug Methods. The subjects were scanned by resting-state MRI spectroscopy first and then subjected to MRI spectroscopy scanning under visual stimulation. The subjects were required to watch the heroin/meth-related clue pictures carefully during visual stimulation. The measured chemical shift value of N-acetyl-aspartic acid (NAA) is substituted into the brain temperature calculation formula T = 37 + 100 to obtain the brain temperature before and after visual stimulation. In addition, the anxiety and depression states of heroin and methamphetamine-dependent patients were evaluated. Results. There was no statistically significant change in the brain temperature of the prefrontal cortex before and after visual stimulation in heroin and methamphetamine-dependent subjects; compared with the normal group, there was no change in prefrontal cortex brain temperature before and after visual stimulation in heroin and methamphetamine-dependent subjects. Statistical Significance. The changes of hippocampal temperature before and after visual stimulation in methamphetamine-dependent patients were not statistically significant; compared with the normal group, there was no statistically significant difference in the changes of hippocampal temperature before and after visual stimulation in methamphetamine-dependent patients. Conclusion. This study initially found that the visual cues related to heroin and methamphetamine were not enough to cause significant changes in the brain temperature of the prefrontal cortex.
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M. Mostafa, Mohamed. "Functional neuroimaging applications in marketing: some methodological and statistical considerations." Qualitative Market Research: An International Journal 17, no. 4 (September 2, 2014): 343–72. http://dx.doi.org/10.1108/qmr-06-2011-0003.
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Purpose – The purpose of this paper is to review recent applications of functional magnetic resonance imaging (fMRI) and other neuroimaging techniques in marketing and advertising, and to present some methodological and statistical considerations that should be taken into consideration when applying fMRI to study consumers’ cognitive behavior related to marketing phenomena. Design/methodology/approach – A critical approach to investigate three methodological issues related to fMRI applications in marketing is adopted. These issues deal mainly with brain activation regions, event-related fMRI and signal-to-noise ratio. Statistical issues related to fMRI data pre-processing, analyzing and reporting are also investigated. Findings – Neuroimaging cognitive techniques have great potential in marketing and advertising. This is because, unlike conventional marketing research methods, neuroimaging data are much less susceptible to social desirability and “interviewer’s” effect. Thus, it is expected that using neuroimaging methods to investigate which areas in a consumer’s brain are activated in response to a specific marketing stimulus can provide a much more honest indicator of their cognition compared to traditional marketing research tools such as focus groups and questionnaires. Originality/value – By merging disparate fields, such as marketing, neuroscience and cognitive psychology, this research presents a comprehensive critical review of how neuroscientific methods can be used to test existing marketing theories.
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Liu, Guang-Rui, Pei-Yi Gao, Yan Lin, Jing Xue, Xiao-Chun Wang, Bin-Bin Sui, Li Ma, Zhi-Nong Xi, Qin Bai, and Hao Shen. "Brain magnetic resonance elastography on healthy volunteers: A safety study." Acta Radiologica 50, no. 4 (May 2009): 423–29. http://dx.doi.org/10.1080/02841850902751681.
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Background: Magnetic resonance elastography (MRE) is a recently developed imaging technique that can directly visualize and quantitatively measure tissue elasticity. Purpose: To evaluate the safety of brain MRE on human subjects. Material and Methods: The study included 20 healthy volunteers. MRE sequence scan (drive signal not applied to external force actuator) and MRE study were separately performed on each volunteer at an interval of more than 24 hours. The heart rate and blood pressure of each volunteer were measured immediately before and after MRE sequence scan and MRE study. Electroencephalography (EEG) was also performed within 2 hours after each scan. The volunteers were asked about their experience of the two scans. Randomized-block analysis of variance (ANOVA) was used to analyze the data of blood pressure and heart rate. Paired t test was used to analyze the data of the two EEG examinations. The volunteers were followed up 1 week after the examination. Results: All procedures were performed on each volunteer, and no one complained of obvious discomfort. No related adverse events were reported during follow-up. There was no statistically significant difference in heart rate or blood pressure. There was a statistically significant difference ( P<0.05) in EEG results in the right temporoparietal region. Increased power was found in the theta, delta, alpha, and beta2 bands. No brain injury was detected by the EEG examinations. Conclusion: Based on the study results, brain MRE examinations are safe to perform on human subjects.
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Raghuram, Karthikram, Aditya Durgam, and Stephen Sartin. "Assessment of the Inferior Petrosal Sinus on T1-Weighted Contrast-Enhanced Magnetic Resonance Imaging." Journal of Clinical Imaging Science 8 (June 18, 2018): 22. http://dx.doi.org/10.4103/jcis.jcis_1_18.
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Context: Skull base venous anatomy. Aims: While prior studies have focused on the efficacy of conventional fluoroscopic venography and multidetector computed tomography venography to evaluate the inferior petrosal sinus (IPS) before image-guided intervention (such as dural venous sinus sampling), we believe that routine magnetic resonance imaging (MRI) may provide reliable structural information helpful for planning without the need for further imaging. Settings and Design: Retrospective review of brain MRI. Materials and Methods: Retrospective analysis was carried out on IPSs on contrast-enhanced T1-weighted MR images. Qualitative measurements were made regarding the grade of patency of the IPS, variation in IPS drainage pattern, and grading of the ipsilateral transverse and sigmoid sinuses (TS and SS). Statistical Analysis Used: Pearson's product-moment correlation. Results: Evaluation of a total of 148 IPSs revealed that 91% of cases were grade 3 or grade 2 (either fully or mostly visualized), with 65% of cases demonstrating “typical” (type A) drainage directly into the internal jugular vein and no statistically significant correlation between the patency of the IPS and the dominance of the ipsilateral TS/SS. A bilateral concordance rate of 77% was also observed. Conclusions: Our analysis indicates that routine thin-slice contrast-enhanced T1-weighted MRI can provide sufficient anatomic detail to identify typical drainage pattern of the IPS in a majority of cases. In cases where routine drainage was not identified, spatial resolution was not sufficient to further delineate complex drainage anatomy. No correlation was observed between the TS/SS dominance and patency of the ipsilateral IPS.
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Bieza, Anvita, and Gaida Krumina. "The Value of Magnetic Resonance in Differentiation between Brain Glioma and Treatment Induced Injury." Acta Chirurgica Latviensis 12, no. 1 (December 1, 2012): 24–28. http://dx.doi.org/10.2478/v10163-012-0005-9.
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SummaryIntroduction.The further therapeutic management decisions in glioma patients after the radiation/chemotherapy may be difficult because the treatment induced brain injury can mimic tumor recurrence clinically and on neuroimaging.Aim of the Studywas to assess the usefulness of magnetic resonance spectroscopy (MRS) and diffusion tensor imaging (DTI) in differentiation between glial tumor recurrence and radiation/chemotherapy-induced changes in the brain.Material and methods.73 patients with primary brain gliomas and 77 gliomas patients after combined therapy with possibly treatment induced changes underwent MRS and DTI. Fractional anisotropy (FA) and metabolite ratios were measured in the tumor and pathological signal intensity area adjacent to post-surgical cavity.Results.Mean choline/creatine (Cho/Cr), myoinositol/creatine (MI/Cr), lactate-lipid/creatine (LL/Cr) ratios of brain gliomas was statistically significant higher and FA values lower than those in the pathological signal intensity area adjacent to post-surgical cavity. No differences were found in mean N-acetyl aspartate/creatine (NAA/Cr) ratios among two groups.Conclusions.Our study suggests that Cho/Cr, MI/Cr, LL/Cr and FA measures should be recommended as additional highly informative tool to conventional structural magnetic resonance imaging (MRI) when monitoring gliomas patients after combined therapy.
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Janarthanan, Vasanthapriya, Kulasekaran Nadhamuni, Sibhithran Rajakumar, Elamparidhi Padmanaban, Umamageswari Amirthalingam, and Yashkumar Achantani. "Accuracy of Magnetic Resonance Parkinsonism Index in Differentiating Progressive Supranuclear Palsy from Parkinson's Disease among South Indian Population: A Retrospective Case Control Study." Indian Journal of Radiology and Imaging 31, no. 03 (July 2021): 596–600. http://dx.doi.org/10.1055/s-0041-1736402.
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Abstract Context Progressive supranuclear palsy (PSP) is a neurodegenerative disorder which comes under Parkinsonism plus syndrome. As this spectrum of disease has many overlapping clinical as well as imaging findings, some quantitative parameters like magnetic resonance Parkinsonism index and midbrain/pons ratio are useful to differentiate PSP from other PD patients. Aims The study aimed to detect sensitivity and specificity of magnetic resonance Parkinsonism index in differentiating PSP from PD. Settings and Design It was a retrospective case–control study conducted in Sri Manankula Vinayagar Medical College, Puducherry, during the period of January 2018 to June 2019. Materials and Methods The 87 subjects, who were diagnosed and grouped into three categories (PSP, PD, and control) after performing magnetic resonance imaging brain, were reviewed. The parameters like the area of Pons and midbrain, width of MCP and SCP, P/M, M/P, and MRPI were calculated. Statistical Analysis One-way ANOVA and Chi-square test was used. The sensitivity, specificity, diagnostic accuracy, and cut-off values obtained with receiver operating characteristic curve analysis were determined. Results The mean age of presentation was approximately 75 years with male predominance. The cut-off value of MRPI obtained in this study was 13.4 with 100% sensitivity and specificity. Even though M/P ratio was found to be statistically significant among PSP patients; cut-off value was not obtained. Conclusion MRPI was concluded as the better tool in diagnosing PSP compared with the M/P ratio. Hence the combined qualitative as well as quantitative measurement of MRPI will increase the diagnostic accuracy of PSP.
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Jacobsen, Cecilie, Robert Zivadinov, Kjell-Morten Myhr, Turi O. Dalaker, Ingvild Dalen, Niels Bergsland, and Elisabeth Farbu. "Brain atrophy and employment in multiple sclerosis patients: a 10-year follow-up study." Multiple Sclerosis Journal - Experimental, Translational and Clinical 6, no. 1 (January 2020): 205521732090248. http://dx.doi.org/10.1177/2055217320902481.
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Background Multiple sclerosis is often associated with unemployment. The contribution of grey matter atrophy to unemployment is unclear. Objectives To identify magnetic resonance imaging biomarkers of grey matter and clinical symptoms associated with unemployment in multiple sclerosis patients. Methods Demographic, clinical data and 1.5 T magnetic resonance imaging scans were collected in 81 patients at the time of inclusion and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. Statistical analysis was performed using a mixed linear model. Results At baseline 31 (38%) of the patients were unemployed, at 5-year follow-up 44 (59%) and at 10-year follow-up 34 (81%) were unemployed. The unemployed patients had significantly lower subcortical deep grey matter volume ( P < 0.001), specifically thalamus, pallidus, putamen and hippocampal volumes, and cortical volume ( P = 0.011); and significantly greater T1 ( P < 0.001)/T2 ( P < 0.001) lesion volume than the employed patient group at baseline. Subcortical deep grey matter volumes, and to a lesser degree cortical volume, were significantly associated with unemployment throughout the follow-up. Conclusion We found significantly greater atrophy of subcortical deep grey matter and cortical volume at baseline and during follow-up in the unemployed patient group. Atrophy of subcortical deep grey matter showed a stronger association to unemployment than atrophy of cortical volume during the follow-up.
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Nagesh, Nagashree, Premjyoti Patil, Shantakumar Patil, and Mallikarjun Kokatanur. "An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (April 1, 2022): 1768. http://dx.doi.org/10.11591/ijece.v12i2.pp1768-1775.
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The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI images. Our work focused mainly on developing the architecture of convolution neural networks (CNN) combining the genetic algorithm. Such artificial intelligence (AI) techniques were very much needed for training as they gave better accuracy compared to traditional statistical methods.