Multivariate tests based on interpoint distances with application to magnetic resonance imaging

2016 ◽  
Vol 25 (6) ◽  
pp. 2593-2610 ◽  
Author(s):  
Marco Marozzi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
High Dimensional Data ◽  
Small Sample ◽  
High Dimensional ◽  
Dependence Structure ◽  
Finite Sample ◽  
Resonance Imaging ◽  
Interpoint Distance ◽  
Multivariate Tests

The multivariate location problem is addressed. The most familiar method to address the problem is the Hotelling test. When the hypothesis of normal distributions holds, the Hotelling test is optimal. Unfortunately, in practice the distributions underlying the samples are generally unknown and without assuming normality the finite sample unbiasedness of the Hotelling test is not guaranteed. Moreover, high-dimensional data are increasingly encountered when analyzing medical and biological problems, and in these situations the Hotelling test performs poorly or cannot be computed. A test that is unbiased for non-normal data, for small sample sizes as well as for two-sided alternatives and that can be computed for high-dimensional data has been recently proposed and is based on the ranks of the interpoint Euclidean distances between observations. Five modifications of this test are proposed and compared to the original test and the Hotelling test. Unbiasedness and consistency of the tests are proven and the problem of power computation is addressed. It is shown that two of the modified interpoint distance-based tests are always more powerful than the original test. Particularly, the modified test based on the Tippett criterium is suggested when the assumption of normality is not tenable and/or in case of high-dimensional data with complex dependence structure which are typical in molecular biology and medical imaging. A practical application to a case-control study where functional magnetic resonance imaging is used is discussed.

Magnetic resonance imaging findings and its association to electroencephalogram in children with partial epilepsy (Preprint)

2020 ◽  
Author(s):  
Huynh Quang Huy
Keyword(s):  
Magnetic Resonance Imaging ◽  
Preschool Children ◽  
Magnetic Resonance ◽  
Brain Mri ◽  
Partial Epilepsy ◽  
Small Sample ◽  
Normal Brain ◽  
Resonance Imaging ◽  
Normal Children ◽  
Eeg Abnormalities

BACKGROUND It is important to identify the neuroimaging features that are associated with partial epilepsy in preschool children. Advances in technology recently to localize focal epileptogenic lesions, especially that of high-resolution structural imaging with magnetic resonance imaging (MRI). The recommendation that electroencephalography (EEG) should be gold criteria and that M.R.I should be optional has been questioned. OBJECTIVE The present study aims to to explore the brain lesions on MRI and its association to electroencephalogram in children with partial epilepsy. METHODS The present study was conducted among 112 preschool children with history of partial seizures. All patients underwent EEG and brain MRI. The epileptogenic lesions were identified on the basis of the signal intensities and morphological abnormalities seen on MRI. The correlation between MRI and EEG abnormalities was explored using a chi-square test. RESULTS Abnormal MRI were found in 34.8% (n = 39) of the sample. The EEG and MRI agreed with respect to classify into abnormal or normal in 48.2% (n = 54). Of the 27 patients with a normal EEG, six (22.2%) were seen to have an abnormal MRI. CONCLUSIONS A number of MRI abnormalities was found in our study of otherwise normal children, although the correlation between these results was not clear. Follow-up of these children will help us identify the important abnormalities. Despite of small sample, our results showed that a normal E.E.G findings does not predict a normal brain MRI in children with partial epilepsy.

scholarly journals Clinical Outcome in Ischemic Stroke Predicted by Early Diffusion-Weighted and Perfusion Magnetic Resonance Imaging: A Preliminary Analysis

1996 ◽  
Vol 16 (1) ◽  
pp. 53-59 ◽  
Author(s):  
Steven Warach ◽  
John F. Dashe ◽  
Robert R. Edelman
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Mri ◽  
Small Sample ◽  
Perfusion Magnetic Resonance Imaging ◽  
Resonance Imaging ◽  
Conventional Mri ◽  
Diffusion Weighted ◽  
And Diffusion ◽  
Clinical Deficits

Perfusion and diffusion-weighted magnetic resonance imaging (MRI) can demonstrate, respectively, cerebral ischemia and ischemic brain injury in the first several hours after onset of symptoms, when proton density and T2-weighted MRI may appear normal. It is hypothesized that these techniques could distinguish regions destined for infarction from those that will not progress to infarction. We provide preliminary evidence from an analysis of 19 patients with severely disabling clinical deficits attributable to ischemia in at least an entire division of the middle cerebral artery, that initial perfusion and diffusion MRI were more accurate than conventional MRI in predicting no, partial or complete improvement—17 of 19 cases ( p < 0.0001) versus 10 of 19 cases, respectively. In the subset of patients studied within 6 h of onset, diffusion/perfusion MRI was an even better predictor than conventional MRI—11 of 12 versus four of 12, respectively. In this small sample of patients with severe clinical deficits, perfusion and diffusion MRI were highly accurate in distinguishing those who would improve from those who would not. These results need to be confirmed in a larger prospective study, which may support a future role in the initial screening, selection, and evaluation of patients with stroke for acute pharmacologic interventions.

scholarly journals The quality of artificial intelligence algorithms for identifying manifestations of multiple sclerosis on magnetic resonance imaging (systematic review)

2021 ◽  
Vol 15 (4) ◽  
pp. 54-65
Author(s):  
Galina N. Chernyaeva ◽  
Sergey P. Morozov ◽  
Anton V. Vladzimirskyy
Keyword(s):  
Magnetic Resonance Imaging ◽  
Artificial Intelligence ◽  
Multiple Sclerosis ◽  
Systematic Review ◽  
Magnetic Resonance ◽  
Small Sample ◽  
Clinical Test ◽  
Data Sets ◽  
Resonance Imaging ◽  
Methodological Errors

A systematic review was undertaken to summarize the data regarding accuracy and effectiveness of artificial intelligence algorithms for identifying MRI manifestations of multiple sclerosis. The review included 39 papers, whose authors put forth a multitude of corresponding algorithms and mathematical models. However, quality assessment of these developments was limited by retrospective testing on repeat data sets. Clinical test results were almost entirely absent, and there were no prospective independent studies of accuracy and applicability. The relatively high values obtained for the main measures (similarity, sensitivity and specificity coefficients, which were 7585%) were offset by the methodological errors when creating the baseline data sets, and lack of validation using independent data. Due to small sample sizes and methodological errors when measuring the result accuracy, most of the studies did not meet the criteria for evidence-based research. Studies with the highest methodological quality had algorithms that achieved a sensitivity of 51.677.0%, with a SrensenDice coefficient of 53.556.0%. These numbers are not high, but they indicate that automatic identification of multiple sclerosis manifestations on magnetic resonance imaging may be achievable. Further development of computer-aided analysis requires the creation of clinical use scenarios and testing methodology, and prospective clinical testing.

scholarly journals Qualitative and Quantitative Wall Enhancement on Magnetic Resonance Imaging Is Associated With Symptoms of Unruptured Intracranial Aneurysms

Stroke ◽  
2021 ◽  
Vol 52 (1) ◽  
pp. 213-222
Author(s):  
Qichang Fu ◽  
Yuting Wang ◽  
Yi Zhang ◽  
Yong Zhang ◽  
Xinbin Guo ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Odds Ratio ◽  
Vessel Wall ◽  
Intracranial Aneurysms ◽  
Small Sample ◽  
Oculomotor Nerve Palsy ◽  
Resonance Imaging ◽  
Qualitative And Quantitative ◽  
Unruptured Intracranial Aneurysms

Background and Purpose: Aneurysmal wall enhancement (AWE) on vessel wall magnetic resonance imaging (VW-MRI) has been described as a new imaging biomarker of unstable unruptured intracranial aneurysms (UIAs). Previous studies of symptomatic UIAs are limited due to small sample sizes and lack of AWE quantification. Our study aims to investigate whether qualitative and quantitative assessment of AWE can differentiate symptomatic and asymptomatic UIAs. Methods: Consecutive patients with UIAs were prospectively recruited for vessel wall magnetic resonance imaging at 3T from October 2014 to October 2019. UIAs were categorized as symptomatic if presenting with sentinel headache or oculomotor nerve palsy directly related to the aneurysm. Evaluation of wall enhancement included enhancement pattern (0=none, 1=focal, and 2=circumferential) and quantitative wall enhancement index (WEI). Univariate and multivariate analyses were used to identify the parameters associated with symptoms. Results: Two hundred sixty-seven patients with 341 UIAs (93 symptomatic and 248 asymptomatic) were included in this study. Symptomatic UIAs more frequently showed circumferential AWE than asymptomatic UIAs (66.7% versus 17.3%, P <0.001), as well as higher WEI (median [interquartile range], 1.3 [1.0–1.9] versus 0.3 [0.1–0.9], P <0.001). In multivariate analysis, both AWE pattern and WEI were independent factors associated with symptoms (odds ratio=2.03 across AWE patterns [95% CI, 1.21–3.39], P =0.01; odds ratio=3.32 for WEI [95% CI, 1.51–7.26], P =0.003). The combination of AWE pattern and WEI had an area under the curve of 0.91 to identify symptomatic UIAs, with a sensitivity of 95.7% and a specificity of 73.4%. Conclusions: In a large cohort of UIAs with vessel wall magnetic resonance imaging, both AWE pattern and WEI were independently associated with aneurysm-related symptoms. The qualitative and quantitative features of AWE can potentially be used to identify unstable intracranial aneurysms.

scholarly journals Clinical surveillance compared with clinical and magnetic resonance imaging surveillance for brain metastasis: a feasibility survey

2016 ◽  
Vol 23 (5) ◽  
pp. 356 ◽  
Author(s):  
K.C.Y. Yiu ◽  
J.N. Greenspoon
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Brain Metastases ◽  
Group Performance ◽  
Eastern Cooperative Oncology Group ◽  
Small Sample ◽  
High Rate ◽  
Resonance Imaging ◽  
Subgroup Analyses ◽  
Clinical Surveillance

Introduction After stereotactic radiosurgery (srs) for brain metastases, patients are routinely monitored with magnetic resonance imaging (mri). The high rate of new brain metastases after srs treatment alone might not be as concerning with modern mri and target localization treatment. Intensive surveillance might induce anxiety, lowering the patient’s quality of life (qol). The present work is the feasibility component of a prospective study evaluating the role of surveillance mri on qol in patients with limited (1–3) brain metastases.Methods Patients with limited brain metastases treated with srs alone, an Eastern Cooperative Oncology Group performance status of 2 or less, and documented stability in treated lesions, with no new lesions seen on mri at weeks 6–10 after srs, were eligible. All were asked about their interest in participating in the control (mri and clinical surveillance) or the experimental arm (symptom-directed mri and clinical surveillance). If 33% or more agreed to participate in the experimental arm, it would be considered feasible to conduct the prospective study.Results From November 2014 to July 2015, 45% of patients (10 of 22) agreed to participate in the experimental arm. Subgroup analyses found that the decision to participate has no statistically significant association with time of presentation (p = 0.696), display of symptoms (p = 0.840), age (p = 0.135), or number of lesions (p = 0.171).Conclusions Results show that it is feasible to conduct the prospective cohort study. Because of the small sample size, we are limited in the conclusions able to be drawn in the subgroup analyses. However, the future study would allow for a better understanding of the attitudes of patients toward mri and its effect on qol.

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