Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Fully-automated deep learning-based flow quantification of 2D CINE phase contrast MRI.

OBJECTIVES: Time-resolved, 2D-phase-contrast MRI (2D-CINE-PC-MRI) enables in vivo blood flow analysis. However, accurate vessel contour delineation (VCD) is required to achieve reliable results. We sought to evaluate manual analysis (MA) compared to the performance of a deep learning (DL) application for fully-automated VCD and flow quantification and corrected semi-automated analysis (corSAA). METHODS: We included 97 consecutive patients (age = 52.9 ± 16 years, 41 female) with 2D-CINE-PC-MRI imaging on 1.5T MRI systems at sinotubular junction (STJ), and 28/97 also received 2D-CINE-PC at main pulmonary artery (PA). A cardiovascular radiologist performed MA (reference) and corSAA (built-in tool) in commercial software for all cardiac time frames (median: 20, total contours per analysis: 2358 STJ, 680 PA). DL-analysis automatically performed VCD, followed by net flow (NF) and peak velocity (PV) quantification. Contours were compared using Dice similarity coefficients (DSC). Discrepant cases (> ± 10 mL or > ± 10 cm/s) were reviewed in detail. RESULTS: DL was successfully applied to 97% (121/125) of the 2D-CINE-PC-MRI series (STJ: 95/97, 98%, PA: 26/28, 93%). Compared to MA, mean DSC were 0.91 ± 0.02 (DL), 0.94 ± 0.02 (corSAA) at STJ, and 0.85 ± 0.08 (DL), 0.93 ± 0.02 (corSAA) at PA; this indicated good to excellent DL-performance. Flow quantification revealed similar NF at STJ (p = 0.48) and PA (p > 0.05) between methods while PV assessment was significantly different (STJ: p < 0.001, PA: p = 0.04). A detailed review showed noisy voxels in MA and corSAA impacted PV results. Overall, DL analysis compared to human assessments was accurate in 113/121 (93.4%) cases. CONCLUSIONS: Fully-automated DL-analysis of 2D-CINE-PC-MRI provided flow quantification at STJ and PA at expert level in > 93% of cases with results being available instantaneously. KEY POINTS: • Deep learning performed flow quantification on clinical 2D-CINE-PC series at the sinotubular junction and pulmonary artery at the expert level in > 93% of cases. • Location detection and contouring of the vessel boundaries were performed fully-automatic with results being available instantaneously compared to human assessments which approximately takes three minutes per location. • The evaluated tool indicates usability in daily practice.

Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.

Objectives: Currently, administering contrast agents is necessary for accurately visualizing and quantifying presence, location, and extent of myocardial infarction (MI) with cardiac magnetic resonance (CMR). In this study, our objective is to investigate and analyze pre- and post-contrast CMR images with the goal of predicting post-contrast information using pre-contrast information only. We propose methods and identify challenges. Methods: The study population consists of 272 retrospectively selected CMR studies with diagnoses of MI (n = 108) and healthy controls (n = 164). We describe a pipeline for pre-processing this dataset for analysis. After data feature engineering, 722 cine short-axis (SAX) images and segmentation mask pairs were used for experimentation. This constitutes 506, 108, and 108 pairs for the training, validation, and testing sets, respectively. We use deep learning (DL) segmentation (UNet) and classification (ResNet50) models to discover the extent and location of the scar and classify between the ischemic cases and healthy cases (i.e., cases with no regional myocardial scar) from the pre-contrast cine SAX image frames, respectively. We then capture complex data patterns that represent subtle signal and functional changes in the cine SAX images due to MI using optical flow, rate of change of myocardial area, and radiomics data. We apply this dataset to explore two supervised learning methods, namely, the support vector machines (SVM) and the decision tree (DT) methods, to develop predictive models for classifying pre-contrast cine SAX images as being a case of MI or healthy. Results: Overall, for the UNet segmentation model, the performance based on the mean Dice score for the test set (n = 108) is 0.75 (±0.20) for the endocardium, 0.51 (±0.21) for the epicardium and 0.20 (±0.17) for the scar. For the classification task, the accuracy, F1 and precision scores of 0.68, 0.69, and 0.64, respectively, were achieved with the SVM model, and of 0.62, 0.63, and 0.72, respectively, with the DT model. Conclusion: We have presented some promising approaches involving DL, SVM, and DT methods in an attempt to accurately predict contrast information from non-contrast images. While our initial results are modest for this challenging task, this area of research still poses several open problems.

Intra-cardiac pressure drop and flow distribution of bicuspid aortic valve disease in preserved ejection fraction.

Background: Bicuspid aortic valve (BAV) is more than a congenital defect since it is accompanied by several secondary complications that intensify induced impairments. Hence, BAV patients need lifelong evaluations to prevent severe clinical sequelae. We applied 4D-flow magnetic resonance imaging (MRI) for in detail visualization and quantification of in vivo blood flow to verify the reliability of the left ventricular (LV) flow components and pressure drops in the silent BAV subjects with mild regurgitation and preserved ejection fraction (pEF). Materials and methods: A total of 51 BAV patients with mild regurgitation and 24 healthy controls were recruited to undergo routine cardiac MRI followed by 4D-flow MRI using 3T MRI scanners. A dedicated 4D-flow module was utilized to pre-process and then analyze the LV flow components (direct flow, retained inflow, delayed ejection, and residual volume) and left-sided [left atrium (LA) and LV] local pressure drop. To elucidate significant diastolic dysfunction in our population, transmitral early and late diastolic 4D flow peak velocity (E-wave and A-wave, respectively), as well as E/A ratio variable, were acquired. Results: The significant means differences of each LV flow component (global measurement) were not observed between the two groups (p > 0.05). In terms of pressure analysis (local measurement), maximum and mean as well as pressure at E-wave and A-wave timepoints at the mitral valve (MV) plane were significantly different between BAV and control groups (p: 0.005, p: 0.02, and p: 0.04 and p: <0.001; respectively). Furthermore, maximum pressure and pressure difference at the A-wave timepoint at left ventricle mid and left ventricle apex planes were significant. Although we could not find any correlation between LV diastolic function and flow components, Low but statistically significant correlations were observed with local pressure at LA mid, MV and LV apex planes at E-wave timepoint (R: -0.324, p: 0.005, R: -0.327, p: 0.004, and R: -0.306, p: 0.008, respectively). Conclusion: In BAV patients with pEF, flow components analysis is not sensitive to differentiate BAV patients with mild regurgitation and healthy control because flow components and EF are global parameters. Inversely, pressure (local measurement) can be a more reliable biomarker to reveal the early stage of diastolic dysfunction.

Validation of a machine learning software tool for automated large vessel occlusion detection in patients with suspected acute stroke.

PURPOSE: CT angiography (CTA) is the imaging standard for large vessel occlusion (LVO) detection in patients with acute ischemic stroke. StrokeSENS LVO is an automated tool that utilizes a machine learning algorithm to identify anterior large vessel occlusions (LVO) on CTA. The aim of this study was to test the algorithm's performance in LVO detection in an independent dataset. METHODS: A total of 400 studies (217 LVO, 183 other/no occlusion) read by expert consensus were used for retrospective analysis. The LVO was defined as intracranial internal carotid artery (ICA) occlusion and M1 middle cerebral artery (MCA) occlusion. Software performance in detecting anterior LVO was evaluated using receiver operator characteristics (ROC) analysis, reporting area under the curve (AUC), sensitivity, and specificity. Subgroup analyses were performed to evaluate if performance in detecting LVO differed by subgroups, namely M1 MCA and ICA occlusion sites, and in data stratified by patient age, sex, and CTA acquisition characteristics (slice thickness, kilovoltage tube peak, and scanner manufacturer). RESULTS: AUC, sensitivity, and specificity overall were as follows: 0.939, 0.894, and 0.874, respectively, in the full cohort; 0.927, 0.857, and 0.874, respectively, in the ICA occlusion cohort; 0.945, 0.914, and 0.874, respectively, in the M1 MCA occlusion cohort. Performance did not differ significantly by patient age, sex, or CTA acquisition characteristics. CONCLUSION: The StrokeSENS LVO machine learning algorithm detects anterior LVO with high accuracy from a range of scans in a large dataset.

Assessment of right ventricular size and function from cardiovascular magnetic resonance images using artificial intelligence.

BACKGROUND: Theoretically, artificial intelligence can provide an accurate automatic solution to measure right ventricular (RV) ejection fraction (RVEF) from cardiovascular magnetic resonance (CMR) images, despite the complex RV geometry. However, in our recent study, commercially available deep learning (DL) algorithms for RVEF quantification performed poorly in some patients. The current study was designed to test the hypothesis that quantification of RV function could be improved in these patients by using more diverse CMR datasets in addition to domain-specific quantitative performance evaluation metrics during the cross-validation phase of DL algorithm development. METHODS: We identified 100 patients from our prior study who had the largest differences between manually measured and automated RVEF values. Automated RVEF measurements were performed using the original version of the algorithm (DL1), an updated version (DL2) developed from a dataset that included a wider range of RV pathology and validated using multiple domain-specific quantitative performance evaluation metrics, and conventional methodology performed by a core laboratory (CORE). Each of the DL-RVEF approaches was compared against CORE-RVEF reference values using linear regression and Bland-Altman analyses. Additionally, RVEF values were classified into 3 categories: ≤ 35%, 35-50%, and ≥ 50%. Agreement between RVEF classifications made by the DL approaches and the CORE measurements was tested. RESULTS: CORE-RVEF and DL-RVEFs were obtained in all patients (feasibility of 100%). DL2-RVEF correlated with CORE-RVEF better than DL1-RVEF (r = 0.87 vs. r = 0.42), with narrower limits of agreement. As a result, DL2 algorithm also showed increasing accuracy from 0.53 to 0.80 for categorizing RV function. CONCLUSIONS: The use of a new DL algorithm cross-validated on a dataset with a wide range of RV pathology using multiple domain-specific metrics resulted in a considerable improvement in the accuracy of automated RVEF measurements. This improvement was demonstrated in patients whose images were the most challenging and resulted in the largest RVEF errors. These findings underscore the critical importance of this strategy in the development of DL approaches for automated CMR measurements.

Evaluating nnU-Net for early ischemic change segmentation on non-contrast computed tomography in patients with Acute Ischemic Stroke.

Identifying the presence and extent of early ischemic changes (EIC) on Non-Contrast Computed Tomography (NCCT) is key to diagnosing and making time-sensitive treatment decisions in patients that present with Acute Ischemic Stroke (AIS). Segmenting EIC on NCCT is however a challenging task. In this study, we investigated a 3D CNN based on nnU-Net, a self-adapting CNN technique that has become the state-of-the-art in medical image segmentation, for segmenting EIC in NCCT of AIS patients. We trained and tested this model on a sizeable and heterogenous dataset of 534 patients, split into 438 for training and validation and 96 for testing. On this test set, we additionally assessed the inter-rater performance by comparing the proposed approach against two reference segmentation annotations by expert neuroradiologist readers, using this as the benchmark against which to compare our model. In terms of spatial agreement, we report median Dice Similarity Coefficients (DSCs) of 39.8% for the model vs. Reader-1, 39.4% for the model vs. Reader-2, and 55.6% for Reader-2 vs. Reader-1. In terms of lesion volume agreement, we report Intraclass Correlation Coefficients (ICCs) of 83.4% for model vs. Reader-1, 80.4% for model vs. Reader-2, and 94.8% for Reader-2 vs. Reader-1. Based on these results, we conclude that our model performs well relative to expert human performance and therefore may be useful as a decision-aid for clinicians.

Effect of age and sex on fully automated deep learning assessment of left ventricular function, volumes, and contours in cardiac magnetic resonance imaging.

Deep learning algorithms for left ventricle (LV) segmentation are prone to bias towards the training dataset. This study assesses sex- and age-dependent performance differences when using deep learning for automatic LV segmentation. Retrospective analysis of 100 healthy subjects undergoing cardiac MRI from 2012 to 2018, with 10 men and women in the following age groups: 18-30, 31-40, 41-50, 51-60, and 61-80 years old. Subjects underwent 1.5 T, 2D CINE SSFP MRI. 35 pathologic cases from local clinical exams and the SCMR 2015 consensus contours dataset were also analyzed. A fully convolutional network (FCN) similar to U-Net trained on the U.K. Biobank was used to automatically segment LV endocardial and epicardial contours. FCN and manual segmentation were compared using Dice metrics and measurements of end-diastolic volume (EDV), end-systolic volume (ESV), mass (LVM), and ejection fraction (LVEF). Paired t-tests and linear regressions were used to analyze measurement differences with respect to sex and age. Dice metrics (median ± IQR) for n = 135 cases were 0.94 ± 0.04/0.87 ± 0.10 (ED endocardium/ES endocardium). Measurement biases (mean ± SD) among the healthy cohort were - 0.3 ± 10.1 mL for EDV, - 6.7 ± 9.6 mL for ESV, 4.6 ± 6.4% for LVEF, and - 2.2 ± 11.0 g for LVM; biases were independent of sex and age. Biases among the 35 pathologic cases were 0.1 ± 19 mL for EDV, - 4.8 ± 19 mL for ESV, 2.0 ± 7.6% for LVEF, and 1.0 ± 20 g for LVM. In conclusion, automatic segmentation by the Biobank-trained FCN was independent of age and sex. Improvements in end-systolic basal slice detection are needed to decrease bias and improve precision in ESV and LVEF.

Bicuspid aortic valve disease is associated with abnormal wall shear stress, viscous energy loss, and pressure drop within the ascending thoracic aorta: A cross-sectional study.

ABSTRACT: Bicuspid aortic valve (BAV) disease has significant gaps in its clinical management practices. To highlight the potential utility of advanced hemodynamic biomarkers in strengthening BAV assessment, we used 4-dimentional flow magnetic resonance imaging to investigate altered hemodynamics in the ascending aorta (AAo).A total of 32 healthy controls and 53 age-matched BAV patients underwent cardiac magnetic resonance imaging at 3T, with cine imaging and 4D-flow. Analysis planes were placed along 3D-segmented aortas at the left ventricular outflow tract (LVOT), sinuses of Valsalva, mid-ascending aorta (MAA), and proximal to the first aortic branch. Locations were analyzed for aortic diameter (normalized to body surface area), pressure drop (PD), viscous energy loss (EL), and wall shear stress (WSS) sub-vectors (axial wall shear stress, circumferential wall shear stress [WSSC], magnitude wall shear stress). Student's t tests, or non-parametric equivalents, compared parameters between cohorts. Univariable and multivariable analyses explored the associations of AAo diameter with hemodynamics within the BAV cohort.Compared to control cohort, BAV patients showed significantly greater PD (MAA: 9.5 ±â€Š8.0 vs 2.8 ±â€Š2.4 mm Hg; P < .01), EL (from LVOT-AA1: 7.39 ±â€Š4.57 mW vs 2.90 ±â€Š1.07 mW; P < .01), and WSSC (MAA: 0.3 ±â€Š0.1 vs 0.2 ±â€Š0.06 Pa; P ≤ .01) throughout the AAo. Correlational analyses revealed an inverse association between AAo diameter and both magnitude wall shear stress and axial wall shear stress.BAV patients exhibited increased PD, EL, and WSSC in the AAo, and an inverse association between AAo diameter and WSS sub-vectors. This demonstrated the impact of PD, EL, and WSS in BAV disease and the importance of altered hemodynamics in aortic remodelling.

Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge.

The emergence of deep learning has considerably advanced the state-of-the-art in cardiac magnetic resonance (CMR) segmentation. Many techniques have been proposed over the last few years, bringing the accuracy of automated segmentation close to human performance. However, these models have been all too often trained and validated using cardiac imaging samples from single clinical centres or homogeneous imaging protocols. This has prevented the development and validation of models that are generalizable across different clinical centres, imaging conditions or scanner vendors. To promote further research and scientific benchmarking in the field of generalizable deep learning for cardiac segmentation, this paper presents the results of the Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation (M&Ms) Challenge, which was recently organized as part of the MICCAI 2020 Conference. A total of 14 teams submitted different solutions to the problem, combining various baseline models, data augmentation strategies, and domain adaptation techniques. The obtained results indicate the importance of intensity-driven data augmentation, as well as the need for further research to improve generalizability towards unseen scanner vendors or new imaging protocols. Furthermore, we present a new resource of 375 heterogeneous CMR datasets acquired by using four different scanner vendors in six hospitals and three different countries (Spain, Canada and Germany), which we provide as open-access for the community to enable future research in the field.

Aortic and mitral flow quantification using dynamic valve tracking and machine learning: Prospective study assessing static and dynamic plane repeatability, variability and agreement.

BACKGROUND: Blood flow is a crucial measurement in the assessment of heart valve disease. Time-resolved flow using magnetic resonance imaging (4 D flow MRI) can provide a comprehensive assessment of heart valve hemodynamics but it relies in manual plane analysis. In this study, we aimed to demonstrate the feasibility of automate the detection and tracking of aortic and mitral valve planes to assess blood flow from 4 D flow MRI. METHODS: In this prospective study, a total of n = 106 subjects were enrolled: 19 patients with mitral disease, 65 aortic disease patients and 22 healthy controls. Machine learning was employed to detect aortic and mitral location and motion in a cine three-chamber plane and a perpendicular projection was co-registered to the 4 D flow MRI dataset to quantify flow volume, regurgitant fraction, and a peak velocity. Static and dynamic plane association and agreement were evaluated. Intra- and inter-observer, and scan-rescan reproducibility were also assessed. RESULTS: Aortic regurgitant fraction was elevated in aortic valve disease patients as compared with controls and mitral valve disease patients (p < 0.05). Similarly, mitral regurgitant fraction was higher in mitral valve patients (p < 0.05). Both aortic and mitral total flow were high in aortic patients. Static and dynamic were good (r > 0.6, p < 0.005) for aortic total flow and peak velocity, and mitral peak velocity and regurgitant fraction. All measurements showed good inter- and intra-observer, and scan-rescan reproducibility. CONCLUSION: We demonstrated that aortic and mitral hemodynamics can efficiently be quantified from 4 D flow MRI using assisted valve detection with machine learning.

Retraining Convolutional Neural Networks for Specialized Cardiovascular Imaging Tasks: Lessons from Tetralogy of Fallot.

Ventricular contouring of cardiac magnetic resonance imaging is the gold standard for volumetric analysis for repaired tetralogy of Fallot (rTOF), but can be time-consuming and subject to variability. A convolutional neural network (CNN) ventricular contouring algorithm was developed to generate contours for mostly structural normal hearts. We aimed to improve this algorithm for use in rTOF and propose a more comprehensive method of evaluating algorithm performance. We evaluated the performance of a ventricular contouring CNN, that was trained on mostly structurally normal hearts, on rTOF patients. We then created an updated CNN by adding rTOF training cases and evaluated the new algorithm's performance generating contours for both the left and right ventricles (LV and RV) on new testing data. Algorithm performance was evaluated with spatial metrics (Dice Similarity Coefficient (DSC), Hausdorff distance, and average Hausdorff distance) and volumetric comparisons (e.g., differences in RV volumes). The original Mostly Structurally Normal (MSN) algorithm was better at contouring the LV than the RV in patients with rTOF. After retraining the algorithm, the new MSN + rTOF algorithm showed improvements for LV epicardial and RV endocardial contours on testing data to which it was naïve (N = 30; e.g., DSC 0.883 vs. 0.905 for LV epicardium at end diastole, p < 0.0001) and improvements in RV end-diastolic volumetrics (median %error 8.1 vs 11.4, p = 0.0022). Even with a small number of cases, CNN-based contouring for rTOF can be improved. This work should be extended to other forms of congenital heart disease with more extreme structural abnormalities. Aspects of this work have already been implemented in clinical practice, representing rapid clinical translation. The combined use of both spatial and volumetric comparisons yielded insights into algorithm errors.

A Systematic Quality Scoring Analysis to Assess Automated Cardiovascular Magnetic Resonance Segmentation Algorithms.

BACKGROUND: The quantitative measures used to assess the performance of automated methods often do not reflect the clinical acceptability of contouring. A quality-based assessment of automated cardiac magnetic resonance (CMR) segmentation more relevant to clinical practice is therefore needed. OBJECTIVE: We propose a new method for assessing the quality of machine learning (ML) outputs. We evaluate the clinical utility of the proposed method as it is employed to systematically analyse the quality of an automated contouring algorithm. METHODS: A dataset of short-axis (SAX) cine CMR images from a clinically heterogeneous population (n = 217) were manually contoured by a team of experienced investigators. On the same images we derived automated contours using a ML algorithm. A contour quality scoring application randomly presented manual and automated contours to four blinded clinicians, who were asked to assign a quality score from a predefined rubric. Firstly, we analyzed the distribution of quality scores between the two contouring methods across all clinicians. Secondly, we analyzed the interobserver reliability between the raters. Finally, we examined whether there was a variation in scores based on the type of contour, SAX slice level, and underlying disease. RESULTS: The overall distribution of scores between the two methods was significantly different, with automated contours scoring better than the manual (OR (95% CI) = 1.17 (1.07-1.28), p = 0.001; n = 9401). There was substantial scoring agreement between raters for each contouring method independently, albeit it was significantly better for automated segmentation (automated: AC2 = 0.940, 95% CI, 0.937-0.943 vs manual: AC2 = 0.934, 95% CI, 0.931-0.937; p = 0.006). Next, the analysis of quality scores based on different factors was performed. Our approach helped identify trends patterns of lower segmentation quality as observed for left ventricle epicardial and basal contours with both methods. Similarly, significant differences in quality between the two methods were also found in dilated cardiomyopathy and hypertension. CONCLUSIONS: Our results confirm the ability of our systematic scoring analysis to determine the clinical acceptability of automated contours. This approach focused on the contours' clinical utility could ultimately improve clinicians' confidence in artificial intelligence and its acceptability in the clinical workflow.

Four-dimensional Flow Magnetic Resonance Imaging Quantification of Blood Flow in Bicuspid Aortic Valve.

BACKGROUND: Four-dimensional (D) flow magnetic resonance imaging (MRI) is limited by time-consuming and nonstandardized data analysis. We aimed to test the efficiency and interobserver reproducibility of a dedicated 4D flow MRI analysis workflow. MATERIALS AND METHODS: Thirty retrospectively identified patients with bicuspid aortic valve (BAV, age=47.8±11.8 y, 9 male) and 30 healthy controls (age=48.8±12.5 y, 21 male) underwent Aortic 4D flow MRI using 1.5 and 3 T MRI systems. Two independent readers performed 4D flow analysis on a dedicated workstation including preprocessing, aorta segmentation, and placement of four 2D planes throughout the aorta for quantification of net flow, peak velocity, and regurgitant fraction. 3D flow visualization using streamlines was used to grade aortic valve outflow jets and extent of helical flow. RESULTS: 4D flow analysis workflow time for both observers: 5.0±1.4 minutes per case (range=3 to 10 min). Valve outflow jets and flow derangement was visible in all 30 BAV patients (both observers). Net flow, peak velocity, and regurgitant fraction was significantly elevated in BAV patients compared with controls except for regurgitant fraction in plane 4 (91.1±29.7 vs. 62.6±19.6 mL/s, 37.1% difference; 121.7±49.7 vs. 90.9±26.4 cm/s, 28.9% difference; 9.3±10.1% vs. 2.0±3.4%, 128.0% difference, respectively; P<0.001). Excellent intraclass correlation coefficient agreement for net flow: 0.979, peak velocity: 0.931, and regurgitant fraction: 0.928. CONCLUSION: Our study demonstrates the potential of an efficient data analysis workflow to perform standardized 4D flow MRI processing in under 10 minutes and with good-to-excellent reproducibility for flow and velocity quantification in the thoracic aorta.

Alteration in Cerebral Activation during a Working Memory Task after Pediatric Mild Traumatic Brain Injury: A Prospective Controlled Cohort Study.

Mild traumatic brain injuries (mTBIs) commonly occur in children and adolescents and can result in persistent cognitive symptoms. The neurophysiological changes that underlie persistent post-concussive symptoms (PPCS) have not been characterized. Our objective was to compare working-memory related functional magnetic resonance imaging (fMRI) response in children with persistent symptoms after mTBI at one month post-injury to children with typical recovery and healthy controls. This was a prospective, controlled cohort study of children with mTBI at one month post-injury. PPCS was defined as children with a 10-point increase in their post-concussion symptom inventory score (compared with pre-injury score) at one month post-injury and a two-point increase in at least two symptom categories compared with pre-injury. One hundred and seven participants (60 PPCS, 30 recovered mTBI, and 17 controls) with a mean age of 14.2 years (standard deviation [SD] 2.5) (44% male) were assessed 38 (SD 5.9) days after mTBI. The primary outcome measures were visuospatial n-back working memory task performance and fMRI blood oxygen level dependent (BOLD) signal change. Children with PPCS had decreased activation relative to children with typical recovery in the posterior cingulate and precuneus during the one-back working memory condition, despite similar task performance. Differences in cortical activation in children with PPCS at one month highlight the persistent neurobiological consequences of pediatric mTBI on working memory cortical activation. These findings encourage recommendations to avoid contact sports and provide continued care at school for children with persistent symptoms at one month post-injury.

Statistical inference of dynamic resting-state functional connectivity using hierarchical observation modeling.

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks; however, most analysis techniques assume functional connections are constant over time. This may be problematic in the case of neurological disease, where functional connections may be highly variable. Recently, several methods have been proposed to determine moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity). Here a novel analysis framework based on a hierarchical observation modeling approach was proposed, to permit statistical inference of the presence of dynamic connectivity. A two-level linear model composed of overlapping sliding windows of fMRI signals, incorporating the fact that overlapping windows are not independent was described. To test this approach, datasets were synthesized whereby functional connectivity was either constant (significant or insignificant) or modulated by an external input. The method successfully determines the statistical significance of a functional connection in phase with the modulation, and it exhibits greater sensitivity and specificity in detecting regions with variable connectivity, when compared with sliding-window correlation analysis. For real data, this technique possesses greater reproducibility and provides a more discriminative estimate of dynamic connectivity than sliding-window correlation analysis. Hum Brain Mapp 37:4566-4580, 2016. © 2016 Wiley Periodicals, Inc.