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Outpacing movement - ultrafast volume coverage in neuropediatric magnetic resonance imaging.

ABSTRACT: BACKGROUND:Conventional MRI sequences are often affected in neuropediatric imaging by unavoidable movements. Therefore, children younger than 6 years usually have to be examined under sedation/anesthesia. A new real-time MRI technique with automatic slice advancement allows for motion-robust T2-weighted volume coverage of the whole brain within a few seconds in adults. OBJECTIVE:To evaluate to which extent the new volume coverage method can be used to visualize cerebrospinal fluid and reduce the need for anesthesia in children. MATERIALS AND METHODS:We assessed 30 children ages 6 years and younger with suspected or proven hydrocephalus, hygroma or macrocephalus using volume coverage sequences with 20 slices per second in three planes. If necessary, a parent was placed in the bore together with the child for calming and gentle immobilization. We compared visualization of cerebrospinal fluid spaces and course of the shunt catheter in volume coverage sequences vs. fast spin-echo sequences. RESULTS:The clinical issue could be sufficiently assessed in all children with use of volume coverage sequences, whereas conventional fast spin-echo sequences performed moderately to poorly. Visualization of the tip of a shunt failed in 16% of volume coverage scans and 27% of turbo spin-echo scans. A subsequent examination under anesthesia was never necessary. None of the examinations had to be stopped prematurely. CONCLUSION:The motion-robust volume coverage sequences with T2-type contrast can be used to avoid sedation of children in the evaluation of cerebrospinal fluid spaces, even in the presence of vigorous motion. For other indications and contrasts, the technique must still be evaluated.

SUBMITTER: Grafe D

PROVIDER: S-EPMC7604272 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Outpacing movement - ultrafast volume coverage in neuropediatric magnetic resonance imaging.

Gräfe Daniel D Roth Christian C Weisser Margit M Krause Matthias M Frahm Jens J Voit Dirk D Hirsch Franz Wolfgang FW

Pediatric radiology 20200919 12

<h4>Background</h4>Conventional MRI sequences are often affected in neuropediatric imaging by unavoidable movements. Therefore, children younger than 6 years usually have to be examined under sedation/anesthesia. A new real-time MRI technique with automatic slice advancement allows for motion-robust T2-weighted volume coverage of the whole brain within a few seconds in adults.<h4>Objective</h4>To evaluate to which extent the new volume coverage method can be used to visualize cerebrospinal fluid ...[more]

PMID: 32949250

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Project description:Introduction: Functional movement disorder (FMD) is a type of functional neurological disorder characterized by abnormal movements that patients do not perceive as self-generated. Prior imaging studies show a complex pattern of altered activity, linking regions of the brain involved in emotional responses, motor control, and agency. This study aimed to better characterize these relationships by building a classifier using a support vector machine to accurately distinguish between 61 FMD patients and 59 healthy controls using features derived from resting-state functional magnetic resonance imaging. Materials and Methods: First, we selected 66 seed regions based on prior related studies, then we calculated the full correlation matrix between them before performing recursive feature elimination to winnow the feature set to the most predictive features and building the classifier. Results: We identified 29 features of interest that were highly predictive of the FMD condition, classifying patients and controls with 80% accuracy. Several key features included regions in the right sensorimotor cortex, left dorsolateral prefrontal cortex, left cerebellum, and left posterior insula. Conclusions: The features selected by the model highlight the importance of the interconnected relationship between areas associated with emotion, reward, and sensorimotor integration, potentially mediating communication between regions associated with motor function, attention, and executive function. Exploratory machine learning was able to identify this distinctive abnormal pattern, suggesting that alterations in functional linkages between these regions may be a consistent feature of the condition in many FMD patients. Clinical-Trials.gov ID: NCT00500994 Impact statement Our research presents novel results that further elucidate the pathophysiology of functional movement disorder (FMD) with a machine learning model that classifies FMD and healthy controls correctly 80% of the time. Herein, we demonstrate how known differences in resting-state functional magnetic resonance imaging connectivity in FMD patients can be leveraged to better understand the complex pattern of neural changes in these patients. Knowing that there are measurable predictable differences in brain activity in patients with FMD may help both clinicians and patients conceptualize and better understand the illness at the point of diagnosis and during treatment. Our methods demonstrate how an effective combination of machine learning and qualitative approaches to analyzing functional brain connectivity can enhance our understanding of abnormal patterns of brain activity in FMD patients.

Dataset Information

Outpacing movement - ultrafast volume coverage in neuropediatric magnetic resonance imaging.

Publications

Outpacing movement - ultrafast volume coverage in neuropediatric magnetic resonance imaging.

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