Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI.
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ABSTRACT:
SUBMITTER: Alexander-Bloch A
PROVIDER: S-EPMC5110234 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundA change in MRI hardware impacts brain volume measurements. The aim of this study was to use MRI data from multiple sclerosis (MS) patients and healthy control subjects (HCs) to statistically model how to adjust brain atrophy measures in MS patients after a major scanner upgrade.MethodsWe scanned 20 MS patients and 26 HCs before and three months after a major scanner upgrade (1.5 T Siemens Healthineers Magnetom Avanto to 3 T Siemens Healthineers Skyra Fit). The patient group also underwent standardized serial MRIs before and after the scanner change. Percentage whole brain volume changes (PBVC) measured by Structural Image Evaluation using Normalization of Atrophy (SIENA) in the HCs was used to estimate a corrective term based on a linear model. The factor was internally validated in HCs, and then applied to the MS group.ResultsMean PBVC during the scanner change was higher in MS than HCs (-4.1 ± 0.8 % versus -3.4 ± 0.6 %). A fixed corrective term of 3.4 (95% confidence interval: 3.13-3.67)% was estimated based on the observed average changes in HCs. Age and gender did not have a significant influence on this corrective term. After adjustment, a linear mixed effects model showed that the brain atrophy measures in MS during the scanner upgrade were not anymore associated with the scanner type (old vs new scanner; p = 0.29).ConclusionA scanner change affects brain atrophy measures in longitudinal cohorts. The inclusion of a corrective term based on changes observed in HCs helps to adjust for the known and unknown factors associated with a scanner upgrade on a group level.
| S-EPMC9424626 | biostudies-literature
Project description:High field MRI systems, such as 7 Tesla (T) scanners, can deliver higher signal to noise ratio (SNR) than lower field scanners and thus allow for the acquisition of data with higher spatial resolution, which is often demanded by users in the fields of clinical and neuroscientific imaging. However, high resolution scans may require long acquisition times, which in turn increase the discomfort for the subject and the risk of subject motion. Even with a cooperative and trained subject, involuntary motion due to heartbeat, swallowing, respiration and changes in muscle tone can cause image artifacts that reduce the effective resolution. In addition, scanning with higher resolution leads to increased sensitivity to even very small movements. Prospective motion correction (PMC) at 3T and 7T has proven to increase image quality in case of subject motion. Although the application of prospective motion correction is becoming more popular, previous articles focused on proof of concept studies and technical descriptions, whereas this paper briefly describes the technical aspects of the optical tracking system, marker fixation and cross calibration and focuses on the application of PMC to very high resolution imaging without intentional motion. In this study we acquired in vivo MR images at 7T using prospective motion correction during long acquisitions. As a result, we present images among the highest, if not the highest resolution of in vivo human brain MRI ever acquired.
| S-EPMC4520483 | biostudies-literature
Project description:The use of 3D intraoral scanners (IOS) and software that can support automated detection and objective monitoring of oral diseases such as caries, tooth wear or periodontal diseases, is increasingly receiving attention from researchers and industry. This study clinically validates an automated caries scoring system for occlusal caries detection and classification, previously defined for an IOS system featuring fluorescence (TRIOS 4, 3Shape TRIOS A/S, Denmark). Four algorithms (ALG1, ALG2, ALG3, ALG4) are assessed for the IOS; the first three are based only on fluorescence information, while ALG4 also takes into account the tooth color information. The diagnostic performance of these automated algorithms is compared with the diagnostic performance of the clinical visual examination, while histological assessment is used as reference. Additionally, possible differences between in vitro and in vivo diagnostic performance of the IOS system are investigated. The algorithms show comparable in vivo diagnostic performance to the visual examination with no significant difference in the area under the ROC curves ([Formula: see text]). Only minor differences between their in vitro and in vivo diagnostic performance are noted but no significant differences in the area under the ROC curves, ([Formula: see text]). This novel IOS system exhibits encouraging performance for clinical application on occlusal caries detection and classification. Different approaches can be investigated for possible optimization of the system.
| S-EPMC8553860 | biostudies-literature
Project description:BackgroundThe inter-scanner reproducibility of brain volumetry is important in multi-site neuroimaging studies, where the reliability of automated brain segmentation (ABS) tools plays an important role. This study aimed to evaluate the influence of ABS tools on the consistency and reproducibility of the quantified brain volumetry from different scanners.MethodsWe included fifteen healthy volunteers who were scanned with 3D isotropic brain T1-weighted sequence on three different 3.0 Tesla MRI scanners (GE, Siemens and Philips). For each individual, the time span between image acquisitions on different scanners was limited to 1 h. All the T1-weighted images were processed with FreeSurfer v6.0, FSL v5.0 and AccuBrain® with default settings to obtain volumetry of brain tissues (e.g. gray matter) and substructures (e.g. basal ganglia structures) if available. Coefficient of variation (CV) was calculated to test inter-scanner variability in brain volumetry of various structures as quantified by these ABS tools.ResultsThe mean inter-scanner CV values per brain structure among three MRI scanners ranged from 6.946 to 12.29% (mean, 9.577%) for FreeSurfer, 7.245 to 20.98% (mean, 12.60%) for FSL and 1.348 to 8.800% (mean value, 3.546%) for AccuBrain®. In addition, AccuBrain® and FreeSurfer achieved the lowest mean values of region-specific CV between GE and Siemens scanners (from 0.818 to 5.958% for AccuBrain®, and from 0.903 to 7.977% for FreeSurfer), while FSL-FIRST had the lowest mean values of region-specific CV between GE and Philips scanners (from 2.603 to 16.310%). AccuBrain® also had the lowest mean values of region-specific CV between Siemens and Philips scanners (from 1.138 to 6.615%).ConclusionThere is a large discrepancy in the inter-scanner reproducibility of brain volumetry when using different processing software. Image acquisition protocols and selection of ABS tool for brain volumetry quantification have impact on the robustness of results in multi-site studies.
| S-EPMC7472704 | biostudies-literature
Project description:ObjectivesTo demonstrate the image quality that can be obtained for multiple contrasts using ultra-high resolution MRI (highest nominal resolution: 350 μm isotropic) at 7T using appropriate motion-correction.Materials and methodsAn MRI-based fat-excitation motion navigator (which requires no additional hardware) was incorporated into T1-weighted (MP2RAGE, 350 μm nominal isotropic resolution, total scan time 124 mins over 2 sessions. The MP2RAGE also provides quantitative T1-maps), 3D-TSE (380 μm nominal isotropic resolution, total scan time 58 mins) and T2*-weighted protocols (3D-GRE, 380 μm nominal isotropic resolution, total scan time 42 mins) on a 7T MR system. Images from each contrast are presented from a single healthy adult male volunteer (34 years) for direct comparison. The subject provided written consent in accordance with the local review board.ResultsImages of various brain structures are revealed at unprecedented quality for in-vivo MRI. The presented images permit, for example, to delimit the internal structure of the basal ganglia and thalamus. The single digitationes of the hippocampus are visible, and the gyrus dentatus can be visualized. Intracortical contrast was also observed in the neocortex, including the stria of Gennari of the primary visual cortex.ConclusionsAppropriate motion-correction allows MRI scans to be performed with extended scan times enabling exceptionally high resolution scans with high image quality, with the use of a 7T scanner allowing large brain coverage for 350-380 μm isotropic voxels with total scan times for each contrast ranging from 42 to 124 minutes.
| S-EPMC4861298 | biostudies-literature
Project description:Multiple studies have shown that data quality is a critical confound in the construction of brain networks derived from functional MRI. This problem is particularly relevant for studies of human brain development where important variables (such as participant age) are correlated with data quality. Nevertheless, the impact of head motion on estimates of structural connectivity derived from diffusion tractography methods remains poorly characterized. Here, we evaluated the impact of in-scanner head motion on structural connectivity using a sample of 949 participants (ages 8-23 years old) who passed a rigorous quality assessment protocol for diffusion magnetic resonance imaging (dMRI) acquired as part of the Philadelphia Neurodevelopmental Cohort. Structural brain networks were constructed for each participant using both deterministic and probabilistic tractography. We hypothesized that subtle variation in head motion would systematically bias estimates of structural connectivity and confound developmental inference, as observed in previous studies of functional connectivity. Even following quality assurance and retrospective correction for head motion, eddy currents, and field distortions, in-scanner head motion significantly impacted the strength of structural connectivity in a consistency- and length-dependent manner. Specifically, increased head motion was associated with reduced estimates of structural connectivity for network edges with high inter-subject consistency, which included both short- and long-range connections. In contrast, motion inflated estimates of structural connectivity for low-consistency network edges that were primarily shorter-range. Finally, we demonstrate that age-related differences in head motion can both inflate and obscure developmental inferences on structural connectivity. Taken together, these data delineate the systematic impact of head motion on structural connectivity, and provide a critical context for identifying motion-related confounds in studies of structural brain network development.
| S-EPMC5911236 | biostudies-literature
Project description:Motion-contaminated T1-weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion-induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20-89 years) were analyzed to reveal stable features of in-scanner head motion. The magnitude of head motion increased with age and exhibited within-participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to "flag" individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age- and gender-matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472-492, 2017. © 2016 Wiley Periodicals, Inc.
| S-EPMC5217095 | biostudies-literature
Project description:IntroductionThe complexity of Magnetic Resonance Imaging (MRI) sequences requires expert knowledge about the underlying contrast mechanisms to select from the wide range of available applications and protocols. Automation of this process using machine learning (ML) can support the radiologists and MR technicians by complementing their experience and finding the optimal MRI sequence and protocol for certain applications.MethodsWe define domain-specific languages (DSL) both for describing MRI sequences and for formulating clinical demands for sequence optimization. By using various abstraction levels, we allow different key users exact definitions of MRI sequences and make them more accessible to ML. We use a vendor-independent MRI framework (gammaSTAR) to build sequences that are formulated by the DSL and export them using the generic file format introduced by the Pulseq framework, making it possible to simulate phantom data using the open-source MR simulation framework JEMRIS to build a training database that relates input MRI sequences to output sets of metrics. Utilizing ML techniques, we learn this correspondence to allow efficient optimization of MRI sequences meeting the clinical demands formulated as a starting point.ResultsML methods are capable of capturing the relation of input and simulated output parameters. Evolutionary algorithms show promising results in finding optimal MRI sequences with regards to the training data. Simulated and acquired MRI data show high correspondence to the initial set of requirements.DiscussionThis work has the potential to offer optimal solutions for different clinical scenarios, potentially reducing exam times by preventing suboptimal MRI protocol settings. Future work needs to cover additional DSL layers of higher flexibility as well as an optimization of the underlying MRI simulation process together with an extension of the optimization method.
| S-EPMC10406289 | biostudies-literature
Project description:Magnetic resonance imaging is a key diagnostic tool in modern healthcare, yet it can be cost-prohibitive given the high installation, maintenance and operation costs of the machinery. There are approximately seven scanners per million inhabitants and over 90% are concentrated in high-income countries. We describe an ultra-low-field brain MRI scanner that operates using a standard AC power outlet and is low cost to build. Using a permanent 0.055 Tesla Samarium-cobalt magnet and deep learning for cancellation of electromagnetic interference, it requires neither magnetic nor radiofrequency shielding cages. The scanner is compact, mobile, and acoustically quiet during scanning. We implement four standard clinical neuroimaging protocols (T1- and T2-weighted, fluid-attenuated inversion recovery like, and diffusion-weighted imaging) on this system, and demonstrate preliminary feasibility in diagnosing brain tumor and stroke. Such technology has the potential to meet clinical needs at point of care or in low and middle income countries.
| S-EPMC8671508 | biostudies-literature
Project description:BackgroundThe brainstem contains grey matter nuclei and white matter tracts to be identified in clinical practice. The small size and the low contrast among them make their in vivo visualisation challenging using conventional magnetic resonance imaging (MRI) sequences at high magnetic field strengths. Combining higher spatial resolution, signal- and contrast-to-noise ratio and sensitivity to magnetic susceptibility (χ), susceptibility-weighted 7-T imaging could improve the assessment of brainstem anatomy.MethodsWe acquired high-resolution 7-T MRI of the brainstem in a 46-year-old female healthy volunteer (using a three-dimensional multi-echo gradient-recalled-echo sequence; spatial resolution 0.3 × 0.3 × 1.2 mm3) and in a brainstem sample from a 48-year-old female body donor that was sectioned and stained. Images were visually assessed; nuclei and tracts were labelled and named according to the official nomenclature.ResultsThis in vivo imaging revealed structures usually evaluated through light microscopy, such as the accessory olivary nuclei, oculomotor nucleus and the medial longitudinal fasciculus. Some fibre tracts, such as the medial lemniscus, were visible for most of their course. Overall, in in vivo acquisitions, χ and frequency maps performed better than T2*-weighted imaging and allowed for the evaluation of a greater number of anatomical structures. All the structures identified in vivo were confirmed by the ex vivo imaging and histology.ConclusionsThe use of multi-echo GRE sequences at 7 T allowed the visualisation of brainstem structures that are not visible in detail at conventional magnetic field and opens new perspectives in the diagnostic and therapeutical approach to brain disorders.Relevance statementIn vivo MR imaging at UHF provides detailed anatomy of CNS substructures comparable to that obtained with histology. Anatomical details are fundamentals for diagnostic purposes but also to plan a direct targeting for a minimally invasive brain stimulation or ablation.Key points• The in vivo brainstem anatomy was explored with ultrahigh field MRI (7 T). • In vivo T2*-weighted magnitude, χ, and frequency images revealed many brainstem structures. • Ex vivo imaging and histology confirmed all the structures identified in vivo. • χ and frequency imaging revealed more brainstem structures than magnitude imaging.
| S-EPMC10651583 | biostudies-literature