Magnetic Resonance Imaging of the Brain in the Pediatric Emergency Department

Lee-Jayaram, Jannet; Goerner, Lee N.; Yamamoto, Loren G.

doi:10.1097/pec.0000000000002286

Cited by 7 publications

(6 citation statements)

References 38 publications

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“…Clinical indications, advantages, and disadvantages of CT versus MRI in PED were given Table 5. [16][17][18][19] There were studies in the literature aimed at reducing the frequency of CT imaging by year or to determine the trend by year. 5,20,21 In 10 years the rate of CT decreased from 3.9% to 2.9%, and the rate of MRI increased from 0.3% to 0.6%.…”

Section: Discussionmentioning

confidence: 99%

“…Although CT is mostly preferred in cases of bone structures and acute trauma, VP shunt dysfunction, newly developed MRI techniques such as FAST MRI can support clinicians in making clinical decision pathways. 16,19 In children with neurological symptoms and for whom CT cannot clearly guide the differential diagnosis, MRI may provide more benefit to the clinician and the patient. The inability of young children and infants to follow orders during MRI, difficulty standing in a fixed posture, and requiring sedation may cause physicians to turn to CT in the chaotic environment of the PED.…”

Section: Discussionmentioning

confidence: 99%

“…Magnetic resonance imaging can be ordered either as a first-line examination or as a follow-up examination after a CT scan. Clinical indications, advantages, and disadvantages of CT versus MRI in PED were given Table 5 16–19 . There were studies in the literature aimed at reducing the frequency of CT imaging by year or to determine the trend by year 5,20,21 .…”

Section: Discussionmentioning

confidence: 99%

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Predictors of Clinically Important Neuroimaging Findings in Children Presenting Pediatric Emergency Department

Gungor,

Haliloglu,

Yalnizoglu

et al. 2024

Pediatr Emer Care

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Objective The aim of the study is to evaluate predictors of clinically important neuroimaging results, that is, computed tomography and magnetic resonance imaging in children in an academic pediatric emergency department (PED) from 2015 to 2019. Methods This study was conducted in an academic PED. The patient's demographic and clinical characteristics of PED visits and neuroimaging findings requested at the PED were recorded for January 1, 2015, to December 31, 2019. In addition, descriptive statistics and logistic regression analyses were conducted. We described and determined the predictors of clinically important neuroimaging findings in children. Results Clinically important neuroimaging findings were detected in patients with blurred vision (P = 0.001), ataxia (P = 0.003), unilateral weakness (P = 0.004), and altered level of consciousness (P = 0.026). Clinically important neuroimaging was found 9.4 times higher in patients with altered level of consciousness, 7.4 times higher in patients with focal weakness, 4.6 times higher in patients with blurred vision, and 3.5 times more in patients presenting with ataxia. Conclusions Advanced neuroimaging, especially for selected patients in PED, can improve the quality of health care for patients. On the other hand, irrelevant neuroimaging findings can lead physicians away from prompt diagnosis and accurate management. According to our study, advanced neuroimaging can be performed in the early period for both diagnosis and early treatment, especially in selected patients with ataxia, blurred vision, altered consciousness, and unilateral weakness. In other cases, clinicians may find more supporting evidence.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Predictors of Clinically Important Neuroimaging Findings in Children Presenting Pediatric Emergency Department

Gungor,

Haliloglu,

Yalnizoglu

et al. 2024

Pediatr Emer Care

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“…For a given GMM, it is also necessary to determine the unknowns contained in each Gaussian component of the model, such as mean, covariance, and mixing coefficient. This study uses the expectation-maximization algorithm based on maximum likelihood estimation to estimate the model parameters [ 16 ]. It is assumed that the sample set is X ={ x 1 , x 2 , x 3 , ⋯, x m } and the value set of the implied variable r is R ={ r 1 , r 2 , r 3 , ⋯, r m }, and then, the logarithmic likelihood function of the sample set can be expressed as follows:

…”

Section: Methodsmentioning

confidence: 99%

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

Yang

et al. 2022

Contrast Media & Molecular Imaging

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The purpose of this study was to explore the diagnostic value of different sequence scanning of nonparametric variable model-based cranial magnetic resonance imaging (MRI) for ischemic stroke. A histogram analysis-based nonparametric variable model was proposed first, which was compared with the parametric deformation (PD) model and geometric deformation (GD) model. Then, 116 patients with acute ischemic stroke were selected as the research subjects. Routine MRI (T2WI, T1WI, FLAIR, DWI, SWI, and 3D TOF MRA) and MR SCALE-PWI were performed. The results showed that the nonparametric variable model algorithm was relatively complete in the actual segmentation results of MRI images, and the display clarity of lesions was better than PD and GD algorithms. The diagnostic sensitivity, specificity, and overall performance of the variable model algorithm were significantly higher than those of the other two algorithms ( P < 0.05 ). According to ROC curve analysis, the AUC areas of DWI, SWI, 3D TOF MRA, and MR SCALE-PWI for the diagnosis of ischemic penumbra were 0.793, 0.825, 0.871, and 0.933, respectively. In summary, the segmentation results of MRI images by the nonparametric variable model based on histogram analysis were relatively complete, and the clarity of lesions was better than that of the traditional model. MRI images can effectively identify the occurrence of ischemic stroke. Moreover, MR SCALE-PWI had a good early identification effect on ischemic penumbra, which can reduce unnecessary treatment for patients.

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“…Magnetic resonance imaging (MRI) has facilitated our current understanding of the processes underlying mental health and illness, and is routinely used in pediatric clinical and neuroimaging research (Figueiro Longo et al, 2022; Lee-Jayaram et al, 2020; Lerch et al, 2017). Ongoing advancements in neuroimaging technology and the development of state-of-the-art statistical techniques have helped to improve our understanding of the etiology of youth mental health disorders, and novel brain-based risk markers show promise in identifying and providing prognostic information for at-risk individuals (Andreou & Borgwardt, 2020; Ellis et al, 2020; Worthington et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people

Cooper,

Hayes,

Corcoran

et al. 2024

Preprint

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BackgroundPortable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people.MethodsT1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age=20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: 1) processing via a convolutional neural network (‘SynthSR’), and 2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches.ResultsSingle pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (rrange=0.60-0.88). Correspondence was lower for cerebral white matter volume (r=0.32,p=.007,q=.009) and non-significant for mean cortical thickness (r=-0.05,p=.664,q =.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (rrange=0.85-0.97), with the exception of global mean cortical thickness (r=0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume.Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness.ConclusionsApplying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

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Magnetic Resonance Imaging of the Brain in the Pediatric Emergency Department

Cited by 7 publications

References 38 publications

Predictors of Clinically Important Neuroimaging Findings in Children Presenting Pediatric Emergency Department

Predictors of Clinically Important Neuroimaging Findings in Children Presenting Pediatric Emergency Department

Traumatic Brain Magnetic Resonance Imaging Feature Extraction Based on Variable Model Algorithm in Stroke Examination

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people

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