Quality Of MRI Data Research Articles

Classification of lumbar spine disorders using large language models and MRI segmentation

BackgroundMRI is critical for diagnosing lumbar spine disorders but its complexity challenges diagnostic accuracy. This study proposes a BERT-based large language model (LLM) to enhance precision in classifying lumbar spine disorders through the integration of MRI data, textual reports, and numerical measurements.MethodsThe segmentation quality of MRI data is evaluated using dice coefficients (cut-off: 0.92) and intersection over union (IoU) metrics (cut-off: 0.88) to ensure precise anatomical feature extraction. The CNN extracts key lumbar spine features, such as lumbar lordotic angle (LLA) and disc heights, which are tokenized as direct scalar values representing positional relationships. A data source of 28,065 patients with various disorders, including degenerative disc disease, spinal stenosis, and spondylolisthesis, is used to establish diagnostic standards. These standards are refined through post-CNN processing of MRI texture features. The BERT-based spinal LLM model integrates these CNN-extracted MRI features and numerical values through early fusion layers.ResultsSegmentation analysis illustrate various lumbar spine disorders and their anatomical changes. The model achieved high performance, with all key metrics nearing 0.9, demonstrating its effectiveness in classifying conditions like spondylolisthesis, herniated disc, and spinal stenosis. External validation further confirmed the model’s generalizability across different populations. External validation on 514 expert-validated MRI cases further confirms the model’s clinical relevance and generalizability. The BERT-based model classifies 61 combinations of lumbar spine disorders.ConclusionsThe BERT-based spinal LLM significantly improves the precision of lumbar spine disorder classification, supporting accurate diagnosis and treatment planning.

Dissociating distinct cortical networks associated with subregions of the human medial temporal lobe using precision neuroimaging

Tract-tracing studies in primates indicate that different subregions of the medial temporal lobe (MTL) are connected with multiple brain regions. However, no clear framework defining the distributed anatomy associated with the human MTL exists. This gap in knowledge originates in notoriously low MRI data quality in the anterior human MTL and in group-level blurring of idiosyncratic anatomy between adjacent brain regions, such as entorhinal and perirhinal cortices, and parahippocampal areas TH/TF. Using MRI, we intensively scanned four human individuals and collected whole-brain data with unprecedented MTL signal quality. Following detailed exploration of cortical networks associated with MTL subregions within each individual, we discovered three biologically meaningful networks associated with the entorhinal cortex, perirhinal cortex, and parahippocampal area TH, respectively. Our findings define the anatomical constraints within which human mnemonic functions must operate and are insightful for examining the evolutionary trajectory of the MTL connectivity across species.

Applying machine learning classifiers to automate quality assessment of paediatric dynamic susceptibility contrast (DSC-) MRI data.

Investigate the performance of qualitative review (QR) for assessing dynamic susceptibility contrast (DSC-) MRI data quality in paediatric normal brain and develop an automated alternative to QR. 1027 signal-time courses were assessed by Reviewer 1 using QR. 243 were additionally assessed by Reviewer 2 and % disagreements and Cohen's κ (κ) were calculated. The signal drop-to-noise ratio (SDNR), root mean square error (RMSE), full width half maximum (FWHM) and percentage signal recovery (PSR) were calculated for the 1027 signal-time courses. Data quality thresholds for each measure were determined using QR results. The measures and QR results trained machine learning classifiers. Sensitivity, specificity, precision, classification error and area under the curve from a receiver operating characteristic curve were calculated for each threshold and classifier. Comparing reviewers gave 7% disagreements and κ = 0.83. Data quality thresholds of: 7.6 for SDNR; 0.019 for RMSE; 3 s and 19 s for FWHM; and 42.9 and 130.4% for PSR were produced. SDNR gave the best sensitivity, specificity, precision, classification error and area under the curve values of 0.86, 0.86, 0.93, 14.2% and 0.83. Random forest was the best machine learning classifier, giving sensitivity, specificity, precision, classification error and area under the curve of 0.94, 0.83, 0.93, 9.3% and 0.89. The reviewers showed good agreement. Machine learning classifiers trained on signal-time course measures and QR can assess quality. Combining multiple measures reduces misclassification. A new automated quality control method was developed, which trained machine learning classifiers using QR results.

Inter-rater reliability of functional MRI data quality control assessments: A standardised protocol and practical guide using pyfMRIqc.

Quality control is a critical step in the processing and analysis of functional magnetic resonance imaging data. Its purpose is to remove problematic data that could otherwise lead to downstream errors in the analysis and reporting of results. The manual inspection of data can be a laborious and error-prone process that is susceptible to human error. The development of automated tools aims to mitigate these issues. One such tool is pyfMRIqc, which we previously developed as a user-friendly method for assessing data quality. Yet, these methods still generate output that requires subjective interpretations about whether the quality of a given dataset meets an acceptable standard for further analysis. Here we present a quality control protocol using pyfMRIqc and assess the inter-rater reliability of four independent raters using this protocol for data from the fMRI Open QC project (https://osf.io/qaesm/). Data were classified by raters as either "include," "uncertain," or "exclude." There was moderate to substantial agreement between raters for "include" and "exclude," but little to no agreement for "uncertain." In most cases only a single rater used the "uncertain" classification for a given participant's data, with the remaining raters showing agreement for "include"/"exclude" decisions in all but one case. We suggest several approaches to increase rater agreement and reduce disagreement for "uncertain" cases, aiding classification consistency.

MRI data quality assessment for the RIN - Neuroimaging Network using the ACR phantoms

PurposeGenerating big-data is becoming imperative with the advent of machine learning. RIN-Neuroimaging Network addresses this need by developing harmonized protocols for multisite studies to identify quantitative MRI (qMRI) biomarkers for neurological diseases. In this context, image quality control (QC) is essential. Here, we present methods and results of how the RIN performs intra- and inter-site reproducibility of geometrical and image contrast parameters, demonstrating the relevance of such QC practice. MethodsAmerican College of Radiology (ACR) large and small phantoms were selected. Eighteen sites were equipped with a 3T scanner that differed by vendor, hardware/software versions, and receiver coils. The standard ACR protocol was optimized (in-plane voxel, post-processing filters, receiver bandwidth) and repeated monthly. Uniformity, ghosting, geometric accuracy, ellipse’s ratio, slice thickness, and high-contrast detectability tests were performed using an automatic QC script. ResultsMeasures were mostly within the ACR tolerance ranges for both T1- and T2-weighted acquisitions, for all scanners, regardless of vendor, coil, and signal transmission chain type. All measurements showed good reproducibility over time. Uniformity and slice thickness failed at some sites. Scanners that upgraded the signal transmission chain showed a decrease in geometric distortion along the slice encoding direction. Inter-vendor differences were observed in uniformity and geometric measurements along the slice encoding direction (i.e. ellipse’s ratio). ConclusionsUse of the ACR phantoms highlighted issues that triggered interventions to correct performance at some sites and to improve the longitudinal stability of the scanners. This is relevant for establishing precision levels for future multisite studies of qMRI biomarkers.

Real-time motion monitoring improves functional MRI data quality in infants

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

Regional brain volume predicts response to methylphenidate treatment in individuals with ADHD

BackgroundDespite the effectiveness of methylphenidate for treating ADHD, up to 30% of individuals with ADHD show poor responses to methylphenidate. Neuroimaging biomarkers to predict medication responses remain elusive. This study characterized neuroanatomical features that differentiated between clinically good and poor methylphenidate responders with ADHD.MethodsUsing a naturalistic observation design selected from a larger cohort, we included 79 drug-naive individuals (aged 6–42 years) with ADHD without major psychiatric comorbidity, who had acceptable baseline structural MRI data quality. Based on a retrospective chart review, we defined responders by individuals’ responses to at least one-month treatment with methylphenidate. A nonparametric mass-univariate voxel-based morphometric analysis was used to compare regional gray matter volume differences between good and poor responders. A multivariate pattern recognition based on the support vector machine was further implemented to identify neuroanatomical indicators to predict an individual’s response.Results63 and 16 individuals were classified in the good and poor responder group, respectively. Using the small-volume correction procedure based on the hypothesis-driven striatal and default-mode network masks, poor responders had smaller regional volumes of the left putamen as well as larger precuneus volumes compared to good responders at baseline. The machine learning approach identified that volumetric information among these two regions alongside the left frontoparietal regions, occipital lobes, and posterior/inferior cerebellum could predict clinical responses to methylphenidate in individuals with ADHD.ConclusionOur results suggest regional striatal and precuneus gray matter volumes play a critical role in mediating treatment responses in individuals with ADHD.

An Electronic Aerosol Delivery System for Functional Magnetic Resonance Imaging.

Background:Public health concerns over the addictive potential of electronic cigarettes (e-cigs) have heightened in recent years. Brain function during e-cig use could provide an objective measure of the addictive potential of new vaping products to facilitate research; however, there are limited methods for delivering e-cig aerosols during functional magnetic resonance imaging (fMRI). The current study describes the development and feasibility testing of a prototype to deliver up to four different e-cig aerosols during fMRI.Methods:Standardized methods were used to test the devices’ air flow variability, nicotine yield, and free radical production. MRI scans were run with and without the device present to assess its safety and effects on MRI data quality. Five daily smokers were recruited to assess plasma nicotine absorption from e-liquids containing nicotine concentrations of 8, 11, 16, 24, and 36 mg/ml. Feedback was collected from participants through a semi-structured interview and computerized questionnaire to assess comfort and subjective experiences of inhaling aerosol from the device.Results:Nicotine yield captured from the aerosol produced by the device was highly correlated with the nicotine concentration of the e-liquids used (R2 = 0.965). Nicotine yield was reduced by a mean of 48% and free radical production by 17% after traveling through the device. The e-liquid containing the highest nicotine concentration tested (36 mg/ml) resulted in the highest plasma nicotine boost (6.6 ng/ml). Overall, participants reported that the device was comfortable to use and inhaling the e-cig aerosols was tolerable. The device was determined to be safe for use during fMRI and had insignificant effects on scan quality.Conclusions:With the current project, we were able to design a working prototype that safely and effectively delivers e-cig aerosols during fMRI. The device has the potential to be used to assess brain activation during e-cig use and to compare brain reactivity to varying flavors, nicotine concentrations, and other e-cig characteristics.

Genetic and environmental influences on MRI scan quantity and quality

The current study provides an overview of quantity and quality of MRI data in a large developmental twin sample (N = 512, aged 7–9), and investigated to what extent scan quantity and quality were influenced by genetic and environmental factors. This was examined in a fixed scan protocol consisting of two functional MRI tasks, high resolution structural anatomy (3DT1) and connectivity (DTI) scans, and a resting state scan. Overall, scan quantity was high (88% of participants completed all runs), while scan quality decreased with increasing session length. Scanner related distress was negatively associated with scan quantity (i.e., completed runs), but not with scan quality (i.e., included runs). In line with previous studies, behavioral genetic analyses showed that genetics explained part of the variation in head motion, with heritability estimates of 29% for framewise displacement and 65% for absolute displacement. Additionally, our results revealed that subtle head motion (after exclusion of excessive head motion) showed lower heritability estimates (0–14%), indicating that findings of motion-corrected and quality-controlled MRI data may be less confounded by genetic factors. These findings provide insights in factors contributing to scan quality in children, an issue that is highly relevant for the field of developmental neuroscience.

P01.091 A robust method for rapidly simulating TTFields distributions within patient-specific computational head models

Tumor Treating Fields (TTFields) are delivered to the brain through two pairs of transducer arrays placed on the patient’s scalp. TTFields distribution in the brain depends on the array’s position, patient anatomy and the electric properties of the tissues and tumor. We aim to create realistic computational patient models in order to understand how field distribution influences disease progression. However, preparation of such models is a highly time consuming task. In addition, in clinical scenarios, MRI acquisition time is often reduced by increased slice spacing, limited field of view, or increased scan speed, leading to difficulties in creating automated models. Our method is designed to enable model creation under those restrictions. It does so by using a realistic head model of a healthy individual used as a deformable template with which the patient model is derived. A highly detailed healthy head model serves as a deformable template from which patient models are created. The first step is pre-processing, involving denoising and background noise reduction, as well as super-resolution algorithms when needed. To create the patient model, the tumor is first segmented manually and masked, leaving only healthy tissues in the MRI, which is then registered to the template space to yield the transformation from patient space to template space. The template is then deformed into the patient space using the inverse transformation, and the tumor is placed back creating a full patient model. Next, automatic identification of landmarks on the patient’s head is used to position the transducer arrays on the head, which are then introduced into the model. Finally, boundary conditions are set, and field distribution is simulated using Finite Differences Time Domain (FDTD) method (Sim4Life V3.0, ZMT-Zurich). We have simulated TTFields distribution of 317 patients treated with TTFields as part of the EF-14 trial. Our method is optimized for accurate contouring of tissues highly influencing the distribution of electric field (Scalp, skull, CSF, ventricles), and it is robust, having the ability to give sufficient results even when MRI data quality is low. Thus enabling a study correlating the spatial distribution of TTFields and patient outcome. Our process for rapidly creating patient models presents a breakthrough that enables the first study in which the spatial distribution of therapeutic electric fields correlates with patient outcome. In the future this method can be used for clinical studies investigating other clinical indications of large datasets.

Characterizing the Effects of MR Image Quality Metrics on Intrinsic Connectivity Brain Networks: A Multivariate Approach.

Motion-induced artifact detection has become a fixture in the assessment of functional magnetic resonance imaging (fMRI) quality control. However, the effects of other MR image quality (IQ) metrics on intrinsic connectivity brain networks are largely unexplored. Accordingly, we report herein the initial assessment of the effects of a comprehensive list of IQ metrics on resting state networks using a multivariate analysis of covariance (MANCOVA) approach based on high-order spatial independent component analysis (ICA). Three categories of MR IQ metrics were considered: (1) metrics for artifacts including the AFNI outlier ratio and quality index, framewise displacement, and ghost to signal ratio, (2) metrics for the temporal quality of MRI data including the temporal framewise change in global BOLD signals (DVARS), global correlation of time-series, and temporal signal to noise ratio, (3) metrics for the structural quality of MRI data including the entropy focus criterion, foreground-background energy ratio, full-width half maximum smoothness, and static signal to noise ratio. After FDR-correction for multiple comparisons, results showed significant effects of the static and temporal signal to noise ratios on the spatial map intensities of the basal ganglia, default-mode and cerebellar networks. AFNI outlier ratio, framewise displacement and DVARS exhibited significant effects on the BOLD power spectra of sensorimotor networks. The global correlation of time-series displayed wide-spread modulation of the spectral power in most networks. Further investigations of the effect of IQ metrics on the characteristics of intrinsic connectivity brain networks allow more accurate interpretation of the fMRI results.

QUALITY ASSURANCE IN DELCODE: A MULTI-CENTER NEUROIMAGING STUDY

During the last years clinical multi-institutional studies are becoming more popular, since they offer neuroscientists the possibility of pooling larger datasets at the cost of strict data standardization and quality assessment rules. This work presents a set of rules and strategies, developed for the DELCODE study, designed to ensure data homogeneity across sites. DELCODE stands for: DZNE longitudinal cognitive impairment and dementia study. Starting in 2014, it is conducted over ten sites across Germany and currently contains about 1.350 scanning sessions from about 825 subjects. It aims to scan 1.000 subjects annually over a period of five years to establish the progression of dementia in the pre-clinical phase. Based on standard operating procedures designed for the study, every scan undergoes a quality check. Acquisition parameters, sequence- and protocol completeness are assessed automatically. Artifacts and coverage of the brain are evaluated manually, spatial orientation is inspected semi-automatically. The results are used as feedback in each center to minimize errors. Data is classified into three categories: A) Complete datasets without deviations from the protocol, B) deviations from the protocol but complete acquisition of scans e.g. suboptimal FoV positioning; and C) incomplete datasets e.g. only structural scans. In 2014, of 84 scans, 52,4% were of type A, 27,4% were of type B and 20,2% were classified as type C. In 2015, of 562 scans, 66,2% were type A, 17,3% were type B and 16,5% were type C. In 2016, of 683 scans, 68,8% were type A, 18,2% were type B and 13% were type C. This shows an increase of quality in data during the first year of feedback, followed by a steady quality assurance in the passing years with increased data acquisition. We were able to establish a harmonized scanning protocol for a multi-center study, assuring a steady monitoring of MRI data quality. Optimization is an ongoing process. The numbers above prove the usefulness of the proposed QA rules and effectiveness of regular feedback to sites. In the future, the currently manual/semi-automatic quality check information will be used to train an algorithm to assess the image quality automatically.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality

To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI. Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner. Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets. High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Choosing the polarity of the phase-encoding direction in diffusion MRI: Does it matter for group analysis?

Notorious for degrading diffusion MRI data quality are so-called susceptibility-induced off-resonance fields, which cause non-linear geometric image deformations. While acquiring additional data to correct for these distortions alleviates the adverse effects of this artifact drastically – e.g., by reversing the polarity of the phase-encoding (PE) direction – this strategy is often not an option due to scan time constraints. Especially in a clinical context, where patient comfort and safety are of paramount importance, acquisition specifications are preferred that minimize scan time, typically resulting in data obtained with only one PE direction. In this work, we investigated whether choosing a different polarity of the PE direction would affect the outcome of a specific clinical research study. To address this methodological question, fractional anisotropy (FA) estimates of FreeSurfer brain regions were obtained in civilian and combat controls, remitted posttraumatic stress disorder (PTSD) patients, and persistent PTSD patients before and after trauma-focused therapy and were compared between diffusion MRI data sets acquired with different polarities of the PE direction (posterior-to-anterior, PA and anterior-to-posterior, AP). Our results demonstrate that regional FA estimates differ on average in the order of 5% between AP and PA PE data. In addition, when comparing FA estimates between different subject groups for specific cingulum subdivisions, the conclusions for AP and PA PE data were not in agreement. These findings increase our understanding of how one of the most pronounced data artifacts in diffusion MRI can impact group analyses and should encourage users to be more cautious when interpreting and reporting study outcomes derived from data acquired along a single PE direction.

The Preprocessed Connectomes Project Quality Assessment Protocol - a resource for measuring the quality of MRI data.

The Preprocessed Connectomes Project Quality Assessment Protocol - a resource for measuring the quality of MRI data.

Implementation and evaluation of simultaneous video-electroencephalography and functional magnetic resonance imaging

The objective of this study was to demonstrate that the addition of simultaneous and synchronised video to electroencephalography (EEG)-correlated functional magnetic resonance imaging (fMRI) could increase recorded information without data quality reduction. We investigated the effect of placing EEG, video equipment and their required power supplies inside the scanner room, on EEG, video and MRI data quality, and evaluated video-EEG-fMRI by modelling a hand motor task. Gradient-echo, echo-planner images (EPI) were acquired on a 3-T MRI scanner at variable camera positions in a test object [with and without radiofrequency (RF) excitation], and human subjects. EEG was recorded using a commercial MR-compatible 64-channel cap and amplifiers. Video recording was performed using a two-camera custom-made system with EEG synchronization. An in-house script was used to calculate signal to fluctuation noise ratio (SFNR) from EPI in test object with variable camera positions and in human subjects with and without concurrent video recording. Five subjects were investigated with video-EEG-fMRI while performing hand motor task. The fMRI time series data was analysed using statistical parametric mapping, by building block design general linear models which were paradigm prescribed and video based. Introduction of the cameras did not alter the SFNR significantly, nor did it show any signs of spike noise during RF off conditions. Video and EEG quality also did not show any significant artefact. The Statistical Parametric Mapping{T} maps from video based design revealed additional blood oxygen level-dependent responses in the expected locations for non-compliant subjects compared to the paradigm prescribed design. We conclude that video-EEG-fMRI set up can be implemented without affecting the data quality significantly and may provide valuable information on behaviour to enhance the analysis of fMRI data.

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

Within the last decade there has been an increase in the use of structural and functional magnetic resonance imaging (fMRI) to investigate the neural basis of human perception, cognition and behavior. Moreover, this non-invasive imaging method has grown into a tool for clinicians and researchers to explore typical and atypical brain development. Although advances in neuroimaging tools and techniques are apparent, (f)MRI in young pediatric populations remains relatively infrequent. Practical as well as technical challenges when imaging children present clinicians and research teams with a unique set of problems. To name just a few, the child participants are challenged by a need for motivation, alertness and cooperation. Anxiety may be an additional factor to be addressed. Researchers or clinicians need to consider time constraints, movement restriction, scanner background noise and unfamiliarity with the MR scanner environment. A progressive use of functional and structural neuroimaging in younger age groups, however, could further add to our understanding of brain development. As an example, several research groups are currently working towards early detection of developmental disorders, potentially even before children present associated behavioral characteristics. Various strategies and techniques have been reported as a means to ensure comfort and cooperation of young children during neuroimaging sessions. Play therapy, behavioral approaches and simulation, the use of mock scanner areas, basic relaxation and a combination of these techniques have all been shown to improve the participant's compliance and thus MRI data quality. Even more importantly, these strategies have proven to increase the comfort of families and children involved. One of the main advances of such techniques for the clinical practice is the possibility of avoiding sedation or general anesthesia (GA) as a way to manage children's compliance during MR imaging sessions. In the current video report, we present a pediatric neuroimaging protocol with guidelines and procedures that have proven to be successful to date in young children.

Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure

Within the last decade there has been an increase in the use of structural and functional magnetic resonance imaging (fMRI) to investigate the neural basis of human perception, cognition and behavior 1, 2. Moreover, this non-invasive imaging method has grown into a tool for clinicians and researchers to explore typical and atypical brain development. Although advances in neuroimaging tools and techniques are apparent, (f)MRI in young pediatric populations remains relatively infrequent 2. Practical as well as technical challenges when imaging children present clinicians and research teams with a unique set of problems 3, 2. To name just a few, the child participants are challenged by a need for motivation, alertness and cooperation. Anxiety may be an additional factor to be addressed. Researchers or clinicians need to consider time constraints, movement restriction, scanner background noise and unfamiliarity with the MR scanner environment2,4-10. A progressive use of functional and structural neuroimaging in younger age groups, however, could further add to our understanding of brain development. As an example, several research groups are currently working towards early detection of developmental disorders, potentially even before children present associated behavioral characteristics e.g.11. Various strategies and techniques have been reported as a means to ensure comfort and cooperation of young children during neuroimaging sessions. Play therapy 12, behavioral approaches 13, 14,15, 16-18 and simulation 19, the use of mock scanner areas 20,21, basic relaxation 22 and a combination of these techniques 23 have all been shown to improve the participant's compliance and thus MRI data quality. Even more importantly, these strategies have proven to increase the comfort of families and children involved 12. One of the main advances of such techniques for the clinical practice is the possibility of avoiding sedation or general anesthesia (GA) as a way to manage children's compliance during MR imaging sessions 19,20. In the current video report, we present a pediatric neuroimaging protocol with guidelines and procedures that have proven to be successful to date in young children.

Perfusion precision in bolus‐tracking MRI: Estimation using the wild‐bootstrap method

Bolus-tracking MRI involves the bolus injection of contrast agent, and the resultant concentration time course can be used to calculate perfusion by deconvolution analysis. However, most deconvolution methods do not provide a measure of precision. Precision could be estimated from many repeated measurements; however, this would involve a series of successive bolus injections, which is not a feasible option in practice due to contrast agent dose constraints. In this work, a method is presented to estimate precision in bolus-tracking MRI using the wild-bootstrap method. This approach is able to estimate the uncertainty in perfusion measurements from the data acquired during a single bolus of contrast agent injection. The methodology was assessed using numerical simulations and applied to real data with a range of image qualities. The method is shown to provide useful estimates of precision, and the results from real data were consistent with the underlying MRI data quality. The methodology provides an important complementary tool in clinical and research applications of bolus-tracking MRI.

Effects of simultaneous EEG recording on MRI data quality at 1.5, 3 and 7 tesla

Although the focus of attention on data degradation during simultaneous MRI/EEG recording has to date largely been upon EEG artefacts, the presence of the conducting wires and electrodes of the EEG recording system also causes some degradation of MRI data quality. This may result from magnetic susceptibility effects which lead to signal drop-out and image distortion, as well as the perturbation of the radiofrequency fields, which can cause local signal changes and a global reduction in the signal to noise ratio (SNR) of magnetic resonance images. Here, we quantify the effect of commercially available 32 and 64 electrode caps on the quality of MR images obtained in scanners operating at magnetic fields of 1.5, 3 and 7 T, via the use of MR-based, field-mapping techniques and analysis of the SNR in echo planar image time series. The electrodes are shown to be the dominant source of magnetic field inhomogeneity, although the localised nature of the field perturbation that they produce means that the effect on the signal intensity from the brain is not significant. In the particular EEG caps investigated here, RF inhomogeneity linked to the longer ECG and EOG leads causes some reduction in the signal intensity in images obtained at 3 and 7 T. Measurements of the standard deviation of white matter signal in EPI time series indicates that the introduction of the EEG cap produces a small reduction in the image signal to noise ratio, which increases with the number of electrodes used.