MRI Acquisition Protocol Research Articles

Quantitative magnetization transfer imaging in relapsing-remitting multiple sclerosis: a systematic review and meta-analysis.

Myelin-sensitive MRI such as magnetization transfer imaging has been widely used in multiple sclerosis. The influence of methodology and differences in disease subtype on imaging findings is, however, not well established. Here, we systematically review magnetization transfer brain imaging findings in relapsing-remitting multiple sclerosis. We examine how methodological differences, disease effects and their interaction influence magnetization transfer imaging measures. Articles published before 06/01/2021 were retrieved from online databases (PubMed, EMBASE and Web of Science) with search terms including ‘magnetization transfer’ and ‘brain’ for systematic review, according to a pre-defined protocol. Only studies that used human in vivo quantitative magnetization transfer imaging in adults with relapsing-remitting multiple sclerosis (with or without healthy controls) were included. Additional data from relapsing-remitting multiple sclerosis subjects acquired in other studies comprising mixed disease subtypes were included in meta-analyses.Data including sample size, MRI acquisition protocol parameters, treatments and clinical findings were extracted and qualitatively synthesized. Where possible, effect sizes were calculated for meta-analyses to determine magnetization transfer (i) differences between patients and healthy controls; (ii) longitudinal change and (iii) relationships with clinical disability in relapsing-remitting multiple sclerosis. Eighty-six studies met inclusion criteria. MRI acquisition parameters varied widely, and were also underreported. The majority of studies examined the magnetization transfer ratio in white matter, but magnetization transfer metrics, brain regions examined and results were heterogeneous. The analysis demonstrated a risk of bias due to selective reporting and small sample sizes. The pooled random-effects meta-analysis across all brain compartments revealed magnetization transfer ratio was 1.17 per cent units (95% CI −1.42 to −0.91) lower in relapsing-remitting multiple sclerosis than healthy controls (z-value: −8.99, P < 0.001, 46 studies). Linear mixed-model analysis did not show a significant longitudinal change in magnetization transfer ratio across all brain regions [β = 0.12 (−0.56 to 0.80), t-value = 0.35, P = 0.724, 14 studies] or normal-appearing white matter alone [β = 0.037 (−0.14 to 0.22), t-value = 0.41, P = 0.68, eight studies]. There was a significant negative association between the magnetization transfer ratio and clinical disability, as assessed by the Expanded Disability Status Scale [r = −0.32 (95% CI −0.46 to −0.17); z-value = −4.33, P < 0.001, 13 studies]. Evidence suggests that magnetization transfer imaging metrics are sensitive to pathological brain changes in relapsing-remitting multiple sclerosis, although effect sizes were small in comparison to inter-study variability. Recommendations include: better harmonized magnetization transfer acquisition protocols with detailed methodological reporting standards; larger, well-phenotyped cohorts, including healthy controls; and, further exploration of techniques such as magnetization transfer saturation or inhomogeneous magnetization transfer ratio.

Neuroimaging in patients with COVID-19: a neuroradiology expert group consensus.

Neurological and neuroradiological manifestations in patients with COVID-19 have been extensively reported. Available imaging data are, however, very heterogeneous. Hence, there is a growing need to standardise clinical indications for neuroimaging, MRI acquisition protocols, and necessity of follow-up examinations. A NeuroCovid working group with experts in the field of neuroimaging in COVID-19 has been constituted under the aegis of the Subspecialty Committee on Diagnostic Neuroradiology of the European Society of Neuroradiology (ESNR). The initial objectives of this NeuroCovid working group are to address the standardisation of the imaging in patients with neurological manifestations of COVID-19 and to give advice based on expert opinion with the aim of improving the quality of patient care and ensure high quality of any future clinical studies.Key Points • In patients with COVID-19 and neurological manifestations, neuroimaging should be performed in order to detect underlying causal pathology. • The basic MRI recommended protocol includes T2-weighted, FLAIR (preferably 3D), and diffusion-weighted images, as well as haemorrhage-sensitive sequence (preferably SWI), and at least for the initial investigation pre and post-contrast T1 weighted-images. • 3D FLAIR should be acquired after gadolinium administration in order to optimise the detection of leptomeningeal contrast enhancement.

Adapting UK Biobank imaging for use in a routine memory clinic setting: The Oxford Brain Health Clinic

The Oxford Brain Health Clinic (BHC) is a joint clinical-research service that provides memory clinic patients and clinicians access to high-quality assessments not routinely available, including brain MRI aligned with the UK Biobank imaging study (UKB).In this work we present how we 1) adapted the UKB MRI acquisition protocol to be suitable for memory clinic patients, 2) modified the imaging analysis pipeline to extract measures that are in line with radiology reports and 3) explored the alignment of measures from BHC patients to the largest brain MRI study in the world (ultimately 100,000 participants).Adaptations of the UKB acquisition protocol for BHC patients include dividing the scan into core and optional sequences (i.e., additional imaging modalities) to improve patients’ tolerance for the MRI assessment. We adapted the UKB structural MRI analysis pipeline to take into account the characteristics of a memory clinic population (e.g., high amount of white matter hyperintensities and hippocampal atrophy). We then compared the imaging derived phenotypes (IDPs) extracted from the structural scans to visual ratings from radiology reports, non-imaging factors (age, cognition) and to reference distributions derived from UKB data.Of the first 108 BHC attendees (August 2020-November 2021), 92.5 % completed the clinical scans, 88.0 % consented to use of data for research, and 43.5 % completed the additional research sequences, demonstrating that the protocol is well tolerated. The high rates of consent to research makes this a valuable real-world quality research dataset routinely captured in a clinical service. Modified tissue-type segmentation with lesion masking greatly improved grey matter volume estimation. CSF-masking marginally improved hippocampal segmentation. The IDPs were in line with radiology reports and showed significant associations with age and cognitive performance, in line with the literature. Due to the age difference between memory clinic patients of the BHC (age range 65–101 years, average 78.3 years) and UKB participants (44–82 years, average 64 years), additional scans on elderly healthy controls are needed to improve reference distributions. Current and future work aims to integrate automated quantitative measures in the radiology reports and evaluate their clinical utility.

Effect of grey-level discretization on texture feature on different weighted MRI images of diverse disease groups.

Many studies of MRI radiomics do not include the discretization method used for the analyses, which might indicate that the discretization methods used are considered irrelevant. Our goals were to compare three frequently used discretization methods (lesion relative resampling (LRR), lesion absolute resampling (LAR) and absolute resampling (AR)) applied to the same data set, along with two different lesion segmentation approaches. We analyzed the effects of altering bin widths or bin numbers for the three different sampling methods using 40 texture indices (TIs). The impact was evaluated on brain MRI studies obtained for 71 patients divided into three different disease groups: multiple sclerosis (MS, N = 22), ischemic stroke (IS, N = 22), cancer patients (N = 27). Two different MRI acquisition protocols were considered for all patients, a T2- and a post-contrast 3D T1-weighted MRI sequence. Elliptical and manually drawn VOIs were employed for both imaging series. Three different types of gray-level discretization methods were used: LRR, LAR and AR. Hypothesis tests were done among all diseased and control areas to compare the TI values in these areas. We also did correlation analyses between TI values and lesion volumes. In general, no significant differences were reported in the results when employing the AR and LAR discretization methods. It was found that employing 38 TIs introduced variation in the results when the number of bin parameters was altered, suggesting that both the degree and direction of monotonicity between each TI value and binning parameters were characteristic for each TI. Furthermore, while TIs were changing with altering binning values, no changes correlated to neither disease nor the MRI sequence. We found that most indices correlated weakly with the volume, while the correlation coefficients were independent of both diseases analyzed and MR contrast. Several cooccurrence-matrix based texture parameters show a definite higher correlation when employing the LRR discretization method However, with the best correlations obtained for the manually drawn VOI. Hypothesis tests among all disease and control areas (co-lateral hemisphere) revealed that the AR or LAR discretization techniques provide more suitable texture features than LRR. In addition, the manually drawn segmentation gave fewer significantly different TIs than the ellipsoid segmentations. In addition, the amount of TIs with significant differences was increasing with increasing the number of bins, or decreasing bin widths. Our findings indicate that the AR discretization method may offer the best texture analysis in MR image assessments. Employing too many bins or too large bin widths might reduce the selection of TIs that can be used for differential diagnosis. In general, more statistically different TIs were observed for elliptical segmentations when compared to the manually drawn VOIs. In the texture analysis of MR studies, studies and publications should report on all important parameters and methods related to data collection, corrections, normalization, discretization, and segmentation.

A graph neural network framework for causal inference in brain networks

A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully comprehending the interplay between structure and function is still challenging and an area of intense research. In this paper we present a graph neural network (GNN) framework, to describe functional interactions based on the structural anatomical layout. A GNN allows us to process graph-structured spatio-temporal signals, providing a possibility to combine structural information derived from diffusion tensor imaging (DTI) with temporal neural activity profiles, like that observed in functional magnetic resonance imaging (fMRI). Moreover, dynamic interactions between different brain regions discovered by this data-driven approach can provide a multi-modal measure of causal connectivity strength. We assess the proposed model’s accuracy by evaluating its capabilities to replicate empirically observed neural activation profiles, and compare the performance to those of a vector auto regression (VAR), like that typically used in Granger causality. We show that GNNs are able to capture long-term dependencies in data and also computationally scale up to the analysis of large-scale networks. Finally we confirm that features learned by a GNN can generalize across MRI scanner types and acquisition protocols, by demonstrating that the performance on small datasets can be improved by pre-training the GNN on data from an earlier study. We conclude that the proposed multi-modal GNN framework can provide a novel perspective on the structure-function relationship in the brain. Accordingly this approach appears to be promising for the characterization of the information flow in brain networks.

Automated Analysis of Multiparametric Magnetic Resonance Imaging/Magnetic Resonance Elastography Exams for Prediction of Nonalcoholic Steatohepatitis.

Nonalcoholic fatty liver disease (NAFLD) affects 25% of the global population. The standard of diagnosis, biopsy, is invasive and affected by sampling error and inter-reader variability. We hypothesized that widely available rapid MRI techniques could be used to predict nonalcoholic steatohepatitis (NASH) noninvasively by measuring liver stiffness, with magnetic resonance elastography (MRE), and liver fat, with chemical shift-encoded (CSE) MRI. Besides, we validate an automated image analysis technique to maximize the utility of these methods. To implement and test an automated system for analyzing CSE-MRI and MRE data coupled with model-based prediction of NASH. Prospective. Eighty-three patients with suspected NAFLD. A 1.5 T using a flow-compensated motion-encoded gradient echo MRE sequence and a multiecho CSE-MRI sequence. The MRE and CSE-MRI data were analyzed by two readers (5+ and 1 years of experience) and an automated algorithm. A logistic regression model to predict pathology-diagnosed NASH was trained based on stiffness and proton density fat fraction, and the area under the receiver operating characteristic curve (AUROC) was calculated using 10-fold cross validation for models based on both automated and manual measurements. A separate model was trained to predict the NASH severity score (NAS). Pearson's correlation, Bland-Altman, AUROC, C-statistic. The agreement between automated measurements and the more experienced reader (R2 =0.87 for stiffness and R2 =0.99 for proton density fat fraction [PDFF]) was slightly better than the agreement between readers (R2 =0.85 and 0.98). The model for predicting biopsy-diagnosed NASH had an AUROC of 0.87. The NAS-prediction model had a C-statistic of 0.85. We demonstrated a workflow that used a limited MRI acquisition protocol and fully automated analysis to predict NASH with high accuracy. These methods show promise to provide a reliable noninvasive alternative to biopsy for NASH-screening in populations with NAFLD. 2 TECHNICAL EFFICACY STAGE: 2.

Using MRI to quantify skeletal muscle pathology in Duchenne muscular dystrophy: A systematic mapping review.

There is a great demand for accurate non‐invasive measures to better define the natural history of disease progression or treatment outcome in Duchenne muscular dystrophy (DMD) and to facilitate the inclusion of a large range of participants in DMD clinical trials. This review aims to investigate which MRI sequences and analysis methods have been used and to identify future needs. Medline, Embase, Scopus, Web of Science, Inspec, and Compendex databases were searched up to 2 November 2019, using keywords “magnetic resonance imaging” and “Duchenne muscular dystrophy.” The review showed the trend of using T1w and T2w MRI images for semi‐qualitative inspection of structural alterations of DMD muscle using a diversity of grading scales, with increasing use of T2map, Dixon, and MR spectroscopy (MRS). High‐field (>3T) MRI dominated the studies with animal models. The quantitative MRI techniques have allowed a more precise estimation of local or generalized disease severity. Longitudinal studies assessing the effect of an intervention have also become more prominent, in both clinical and animal model subjects. Quality assessment of the included longitudinal studies was performed using the Newcastle‐Ottawa Quality Assessment Scale adapted to comprise bias in selection, comparability, exposure, and outcome. Additional large clinical trials are needed to consolidate research using MRI as a biomarker in DMD and to validate findings against established gold standards. This future work should use a multiparametric and quantitative MRI acquisition protocol, assess the repeatability of measurements, and correlate findings to histologic parameters.

Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks.

We propose a dictionary-matching-free pipeline for multi-parametric quantitative MRI image computing. Our approach has two stages based on compressed sensing reconstruction and deep learned quantitative inference. The reconstruction phase is convex and incorporates efficient spatiotemporal regularisations within an accelerated iterative shrinkage algorithm. This minimises the under-sampling (aliasing) artefacts from aggressively short scan times. The learned quantitative inference phase is purely trained on physical simulations (Bloch equations) that are flexible for producing rich training samples. We propose a deep and compact encoder-decoder network with residual blocks in order to embed Bloch manifold projections through multi-scale piecewise affine approximations, and to replace the non-scalable dictionary-matching baseline. Tested on a number of datasets we demonstrate effectiveness of the proposed scheme for recovering accurate and consistent quantitative information from novel and aggressively subsampled 2D/3D quantitative MRI acquisition protocols.

Alzheimer’s disease–related atrophy patterns in different definitons of subjective cognitive decline

AbstractBackgroundDifferent criteria sets were suggested for better operationalization of subjective cognitive decline. In this study, we aimed to assess structural neuroimaging biomarkers of AD in different definitions of SCD.MethodThe study was conducted at Hacettepe University, Turkey. 101 participants over 55 years of age, with memory complaints and no identifiable impairment in a valid cognitive screening test (Z scores&gt;‐1.5 in the Modified Mini Mental Test 3MS) were included. We evaluated whether (i) using a scale (Subjective Memory Complaints Questionnaire‐SMCQ) rather than a single question assessing the subjective complaint; (ii) utilizing neuropsychological test data in addition to a screening test; (iii) evaluating SCD + criteria (additional qualitative features of the complaint) and (iv) the exclusion of comorbidities that may be associated with memory complaints; would be associated with increased atrophy in AD‐related areas. Three different cortical thickness and two cortical volume patterns based on previous research were determined as the dependent variables. MR images were obtained with a 1.5 Tesla MRI scanner. Cortical thickness and gray matter volumes were estimated using the FreeSurfer Software package version 6.0 on 3D T1‐weighted MRI scans (MP RAGE), in accordance with ADNI‐1 MRI acquisition protocol. The multivariate regression models were performed for evaluating the relationship of each independent variable.ResultResults suggested that age, sex, age of onset of memory complaints, depression or anxiety symptoms and vitamin B12 and vitamin D levels were associated with alterations in the structural markers of AD. Regarding different definitions of SCD; assessment of SCD + criteria, utilizing SMCQ, addition of neuropsychological test data and excluding individuals with comorbidities did not contribute to the validity of SCD in terms of the AD morphology.ConclusionOur findings indicate the importance of psychiatric evaluation in individuals with subjective memory complaints. The presence of depression and anxiety symptoms in a person with SCD may increase the likelihood of AD‐related brain morphological changes. Longitudinal studies, better assessment of the phenotype, inclusion of different diagnostic groups may help to refine the definition of SCD and the addition of criterion which may point to early morphological changes. This study has been funded by TUBITAK 214S048, Hacettepe THD‐2018‐17363.

Combined imaging of potassium and sodium in human skeletal muscle tissue at 7 T.

To validate the feasibility of quantitative combined potassium (39 K) and sodium (23 Na) MRI in human calf muscle tissue, as well as to evaluate the reproducibility of the apparent tissue potassium concentration (aTPC) and apparent tissue sodium concentration (aTSC) determination in healthy muscle tissue. Quantitative 23 Na and 39 K MRI acquisition protocols were implemented on a 7 T MR system. A double-resonant 23 Na/39 K birdcage RF coil was used. Measurements of human lower leg were performed in a total acquisition time of TANa = 10:54 min/TAK = 8:06 min and using a nominal spatial resolution of 2.5 × 2.5 × 15 mm3 /7.5 × 7.5 × 30 mm3 for 23 Na/39 K MRI. Two aTSC and aTPC examinations in muscle tissue were performed during the same day on 10 healthy subjects. The proposed acquisition and postprocessing workflow for 23 Na and 39 K MRI data sets provided reproducible aTSC and aTPC measurements. In human calf muscle tissue, the coefficient of variation between scan and re-scan was 5.7% for both aTSC and aTPC determination. Overall, mean values of aTSC = (17 ± 1) mM and aTPC = (85 ± 5) mM were measured. Moreover, for 39 K in calf muscle tissue, components of = (1.2 ± 0.2) ms and = (7.9 ± 0.9) ms, as well as a residual quadrupolar interaction of = (143 ± 17) Hz, were determined. The fraction of the fast component was f = (58 ± 4)%. Using the presented measurement and postprocessing approach, a reproducible aTSC and aTPC determination using 23 Na and 39 K MRI at 7 T in human skeletal muscle tissue is feasible in clinically acceptable acquisition durations.

An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region.

Quantitative Susceptibility Mapping (QSM) is an emerging technique sensitive to disease-related changes including oxygenation. It is extensively used in brain studies and has increasing clinical applications outside the brain. Here we present the first MRI acquisition protocol and QSM pipeline optimized for the head-and-neck region together with a repeatability analysis performed in healthy volunteers. We investigated both the intrasession and the intersession repeatability of the optimized method in 10 subjects. We also implemented two, Tikhonov-regularisation-based susceptibility calculation techniques that were found to have higher contrast-to-noise than existing methods in the head-and-neck region. Repeatability was evaluated by calculating the distributions of susceptibility differences between repeated scans and the corresponding minimum detectable effect sizes (MDEs). Deep brain regions had higher QSM repeatability than neck regions. As expected, intrasession repeatability was generally better than intersession repeatability. Susceptibility maps calculated using projection onto dipole fields for background field removal were more repeatable than using the Laplacian boundary value method in the head-and-neck region. Small (short-axis diameter <5 mm) lymph nodes had the lowest repeatability (MDE = 0.27 ppm) as imperfect segmentation included some of the surrounding paramagnetic fatty fascia, highlighting the importance of accurate region delineation. MDEs in the larger lymph nodes (0.16 ppm), submandibular glands (0.10 ppm), and especially the parotid glands (0.06 ppm) were much lower, comparable to those of the brain regions. The high repeatability of the acquisition and pipeline optimized for QSM will facilitate clinical studies in the head-and-neck region.

Outcome prediction of head and neck squamous cell carcinoma by MRI radiomic signatures

ObjectivesHead and neck squamous cell carcinoma (HNSCC) shows a remarkable heterogeneity between tumors, which may be captured by a variety of quantitative features extracted from diagnostic images, termed radiomics. The aim of this study was to develop and validate MRI-based radiomic prognostic models in oral and oropharyngeal cancer.Materials and MethodsNative T1-weighted images of four independent, retrospective (2005–2013), patient cohorts (n = 102, n = 76, n = 89, and n = 56) were used to delineate primary tumors, and to extract 545 quantitative features from. Subsequently, redundancy filtering and factor analysis were performed to handle collinearity in the data. Next, radiomic prognostic models were trained and validated to predict overall survival (OS) and relapse-free survival (RFS). Radiomic features were compared to and combined with prognostic models based on standard clinical parameters. Performance was assessed by integrated area under the curve (iAUC).ResultsIn oral cancer, the radiomic model showed an iAUC of 0.69 (OS) and 0.70 (RFS) in the validation cohort, whereas the iAUC in the oropharyngeal cancer validation cohort was 0.71 (OS) and 0.74 (RFS). By integration of radiomic and clinical variables, the most accurate models were defined (iAUC oral cavity, 0.72 (OS) and 0.74 (RFS); iAUC oropharynx, 0.81 (OS) and 0.78 (RFS)), and these combined models outperformed prognostic models based on standard clinical variables only (p < 0.001).ConclusionsMRI radiomics is feasible in HNSCC despite the known variability in MRI vendors and acquisition protocols, and radiomic features added information to prognostic models based on clinical parameters.Key Points• MRI radiomics can predict overall survival and relapse-free survival in oral and HPV-negative oropharyngeal cancer.• MRI radiomics provides additional prognostic information to known clinical variables, with the best performance of the combined models.• Variation in MRI vendors and acquisition protocols did not influence performance of radiomic prognostic models.

1283-P: Multicenter Assessment of the Pancreas in Type 1 Diabetes (MAP-T1D)

Individuals both at risk for and newly diagnosed with type 1 diabetes (T1D) have a reduced pancreas volume as assessed by MRI. Furthermore, multiparametric quantitative MRI techniques have detected focal alterations in the T1D pancreas. However, performing quantitative MRI techniques in multi-site trials is challenging due to differences in MRI hardware and software. To determine if quantitative MRI measurements in T1D can be performed at different institutions, 5 academic centers (see author affiliation) have established the Multicenter Assessment of the Pancreas in T1D (MAP-T1D) group to develop harmonized MRI acquisition and image processing protocols. We established and implemented an MRI protocol for pancreas imaging on both Siemens and Philips MRI platforms at 5 imaging centers, consisting of T1- and T2-weighted anatomical images, diffusion-weighted MRI, magnetization transfer MRI, and longitudinal relaxation (T1) mapping. Imaging phantoms assessing volume, diffusion, magnetization transfer ratio, and T1 were created and imaged at each institution to test the reproducibility of multiparametric MRI. Measurement of phantom volume yielded an average error of 1.5% from the true volume. The apparent diffusion coefficient, magnetization transfer ratio, and T1 phantoms had coefficients of variation of 6.0%, 10.0%, and 9.5%, respectively, between sites. To assess the reproducibility of human MRI scanning, we recruited 5 healthy subjects to travel to 4 sites for imaging using the standardized MRI protocol. Pancreas volume measured at multiple sites had an average coefficient of variation between scans of 8.3% with a maximum difference of 17% (n = 9 MRIs). This study demonstrates the feasibility of quantitative pancreas MRI across multiple sites. Standardization of pancreas MRI acquisition and processing will accelerate integration of pancreas volume and other quantitative MRI measurements in future multi-center clinical trials of T1D. Disclosure J. Virostko: None. J.M. Williams: None. M.A. Hilmes: None. L. Du: None. H. Kang: None. W.E. Russell: None. T.M. Triolo: None. L.H. Philipson: None. E.K. Sims: None. C. Evans-Molina: Consultant; Self; Bristol-Myers Squibb. H.E. Thomas: None. T. Kay: None. S.W. Greeley: None. A. Steck: None. A.C. Powers: None. D.J. Moore: None. Funding JDRF (3-SRA-2019-759-M-B, 3-SRA-2015-102-M-B)

Chapter 18 - Structural neuroimaging

Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.

Multiple Sclerosis multimodal lesion simulation tool (MS-MIST)

Background: Multiple Sclerosis (MS) diagnosis and evaluation is often a challenging task due to its growing need for multimodal MRI acquisition protocol. Recently, the scientific community offers several computational alternatives to the time-consuming and subjective task of manual MS lesion segmentation. Although there is an increasing number of MS lesion segmentation methods, a controlled and realistic simulation environment can benefit the community for a reliable evaluation procedure. Methods: This study proposes an automatic parametric MS lesion simulation framework (MS-MIST) with the objective to emulate real MS-like pattern on MRI data of healthy individuals. The voxel gray-level patterns, spatial location, and shapes extracted from MS patient allow consistent simulation features. We used both visual evaluation from an expert radiologist in the field of MS diagnosis and SPM Lesion Segmentation Tool (LST) for qualitative and quantitative simulation quality, respectively. Results: Our results show that both the agreement between the automatic segmentation with the simulated lesions (Pearson’s correlation R = 0.977) and the segmentation quality scores, i.e., sensitivity (mean = 0.9050), specificity (mean = 0.9992), dice similarity (mean = 0.8972), and accuracy (mean = 0.9984), are consistent between MS-MIST and real clinical settings. Conclusions: MS-MIST proposes a practical solution to common issues discussed in the literature, such as inconsistency with real lesions geometry, imprecise lesion spatial and signal variability, lack of multiple MRI modalities and restrictions to simulate different lesion loads. It is worth noting that the simulation platform is freely available as an open-source code to the community.

Partial volume transfer (PVT) conversion of cerebral tissue volumes between different magnetic fields MRI

In neurodegenerative clinical studies, patients are usually followed up for several years frequently examined in different MRI scanners and base magnetic fields. Technical specification differences in MRI scanners, e.g., acquisition protocols and spatial resolution, are two crucial limitations for longitudinal long-term cerebral atrophy investigations. Although widely known that MRI base magnetic field (B0) boldly affects brain tissue volume measurements, no systematic study has been proposed to maintain brain volume consistency through longitudinal exams. We propose a method to convert the measured volumes of segmented brain compartments from one MRI scanner with lower B0 and resolutions to volumes consistent with a higher resolution. The proposed partial volume transfer (PVT) method is consistent with partial volume effect to correct the volume measures. The PVT demands at least one pair of simultaneous acquisitions on both MRI scanners to estimate three unknown coefficients completing the PVT system. We selected ten healthy subjects from a standard dataset on the web, ten other healthy subjects, and ten patients with multiple sclerosis diseases scanned by two different scanners, i.e., 1.5 T and 3.0 T from local hospital dataset, to evaluate the PVT. We obtained the mean relative errors ranging from 0.28 to 1.14% of total brain volume using the PVT conversion for brain compartments, significantly less than interpolation methods which were more than 6.54%. By providing a real longitudinal sequence of images during 9-years follow-up of a patient, the PVT conversion was used to accurately convert cerebral tissues volumes acquired in different spatial resolutions.

Abnormal Cochleovestibular Anatomy and Hearing Outcomes: Pediatric Patients with a Questionable Cochleovestibular Nerve Status May Benefit from Cochlear Implantation and/or Hearing Aids

Objective: Imaging characteristics and hearing outcomes in children with cochleovestibular or cochleovestibular nerve (CVN) abnormalities. Study Design: Retrospective, critical review. Setting: Tertiary referral academic center. Patients: Twenty-seven children with CVN abnormalities with magnetic resonance (MRI) and/or computed tomography (CT). Study Intervention(s): None. Main Outcome Measure(s): Determine the likely presence or absence of a CNV and auditory stimulation responses. Results: Two of 27 cases had unilateral hearing loss, and all others had bilateral loss. Eleven (46%) were identified with a disability or additional condition. Twenty-two (42%) ears received a cochlear implant (CI) and 9 ears (17%) experienced no apparent benefit from the device. MRI acquisition protocols were suboptimal for identification of the nerve in 22 (42%) ears. A likely CVN absence was associated with a narrow cochlear aperture and internal auditory canal and cochlear malformation. Thirteen (48%) children with an abnormal nerve exhibited normal cochleae on the same side. Hearing data were available for 30 ears, and 25 ears (83%) exhibited hearing with or without an assistive device. One child achieved closed set speech recognition with a hearing aid, another with a CI. One child achieved open set speech recognition with a CI. Conclusions: Current imaging cannot accurately characterize the functional status of the CVN or predict an assistive device benefit. Children who would have otherwise been denied a CI exhibited auditory responses after implantation. A CI should be considered in children with abnormal CVN. Furthermore, imaging acquisition protocols need standardization for clear temporal bone imaging.

Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7 T and 3 T: The Impact of the Experimental Design

The recent introduction of advanced magnetic resonance (MR) imaging techniques to characterize focal and global degeneration in multiple sclerosis (MS), like the Composite Hindered and Restricted Model of Diffusion, or CHARMED, diffusional kurtosis imaging (DKI) and Neurite Orientation Dispersion and Density Imaging (NODDI) made available new tools to image axonal pathology non-invasively in vivo. These methods already showed greater sensitivity and specificity compared to conventional diffusion tensor-based metrics (e.g., fractional anisotropy), overcoming some of its limitations.While previous studies uncovered global and focal axonal degeneration in MS patients compared to healthy controls, here our aim is to investigate and compare different diffusion MRI acquisition protocols in their ability to highlight microstructural differences between MS and control tissue over several much used models. For comparison, we contrasted the ability of fractional anisotropy measurements to uncover differences between lesion, normal-appearing white matter (WM), gray matter and healthy tissue under the same imaging protocols. We show that: (1) focal and diffuse differences in several microstructural parameters are observed under clinical settings; (2) advanced models (CHARMED, DKI and NODDI) have increased specificity and sensitivity to neurodegeneration when compared to fractional anisotropy measurements; and (3) both high (3 T) and ultra-high fields (7 T) are viable options for imaging tissue change in MS lesions and normal appearing WM, while higher b-values are less beneficial under the tested short-time (10 min acquisition) conditions.

Harmonization of cortical thickness measurements across scanners and sites

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling non-biological variance introduced by differences in MRI scanners and acquisition protocols. Such unwanted sources of variation, which we refer to as “scanner effects”, can hinder the detection of imaging features associated with clinical covariates of interest and cause spurious findings. In this paper, we investigate scanner effects in two large multi-site studies on cortical thickness measurements across a total of 11 scanners. We propose a set of tools for visualizing and identifying scanner effects that are generalizable to other modalities. We then propose to use ComBat, a technique adopted from the genomics literature and recently applied to diffusion tensor imaging data, to combine and harmonize cortical thickness values across scanners. We show that ComBat removes unwanted sources of scan variability while simultaneously increasing the power and reproducibility of subsequent statistical analyses. We also show that ComBat is useful for combining imaging data with the goal of studying life-span trajectories in the brain.

Stability of MRI metrics in the advanced research core of the NCAA-DoD concussion assessment, research and education (CARE) consortium.

The NCAA-DoD Concussion Assessment, Research, and Education (CARE) consortium is performing a large-scale, comprehensive study of sport related concussions in college student-athletes and military service academy cadets. The CARE "Advanced Research Core" (ARC), is focused on executing a cutting-edge investigative protocol on a subset of the overall CARE athlete population. Here, we present the details of the CARE ARC MRI acquisition and processing protocol along with preliminary analyzes of within-subject, between-site, and between-subject stability across a variety of MRI biomarkers. Two experimental datasets were utilized for this analysis. First, two "human phantom" subjects were imaged multiple times at each of the four CARE ARC imaging sites, which utilize equipment from two imaging vendors. Additionally, a control cohort of healthy athletes participating in non-contact sports were enrolled in the study at each CARE ARC site and imaged at four time points. Multiple morphological image contrasts were acquired in each MRI exam; along with quantitative diffusion, functional, perfusion, and relaxometry imaging metrics. As expected, the imaging markers were found to have varying levels of stability throughout the brain. Importantly, between-subject variance was generally found to be greater than within-subject and between-site variance. These results lend support to the expectation that cross-site and cross-vendor advanced quantitative MRI metrics can be utilized to improve analytic power in assessing sensitive neurological variations; such as those effects hypothesized to occur in sports-related-concussion. This stability analysis provides a crucial foundation for further work utilizing this expansive dataset, which will ultimately be freely available through the Federal Interagency Traumatic Brain Injury Research Informatics System.