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Background: After traumatic brain injury (TBI), plasma concentration of glial fibrillary acidic protein (GFAP) correlates with intracranial injury visible on CT scan. Some patients with suspected TBI with normal CT findings show pathology on MRI. We assessed the discriminative ability of GFAP to identify MRI abnormalities in patients with normal CT findings., Methods: TRACK-TBI is a prospective cohort study that enrolled patients with TBI who had a clinically indicated head CT scan within 24 h of injury at 18 level 1 trauma centres in the USA. For this analysis, we included patients with normal CT findings (Glasgow Coma Scale score 13-15) who consented to venepuncture within 24 h post injury and who had an MRI scan 7-18 days post injury. We compared MRI findings in these patients with those of orthopaedic trauma controls and healthy controls recruited from the study sites. Plasma GFAP concentrations (pg/mL) were measured using a prototype assay on a point-of-care platform. We used receiver operating characteristic (ROC) analysis to evaluate the discriminative ability of GFAP for positive MRI scans in patients with negative CT scans over 24 h (time between injury and venepuncture). The primary outcome was the area under the ROC curve (AUC) for GFAP in patients with CT-negative and MRI-positive findings versus patients with CT-negative and MRI-negative findings within 24 h of injury. The Dunn Kruskal-Wallis test was used to compare GFAP concentrations between MRI lesion types with Benjamini-Hochberg correction for multiple comparisons. This study is registered with ClinicalTrials.gov, number NCT02119182., Findings: Between Feb 26, 2014, and June 15, 2018, we recruited 450 patients with normal head CT scans (of whom 330 had negative MRI scans and 120 had positive MRI scans), 122 orthopaedic trauma controls, and 209 healthy controls. AUC for GFAP in patients with CT-negative and MRI-positive findings versus patients with CT-negative and MRI-negative findings was 0·777 (95% CI 0·726-0·829) over 24 h. Median plasma GFAP concentration was highest in patients with CT-negative and MRI-positive findings (414·4 pg/mL, 25-75th percentile 139·3-813·4), followed by patients with CT-negative and MRI-negative findings (74·0 pg/mL, 17·5-214·4), orthopaedic trauma controls (13·1 pg/mL, 6·9-20·0), and healthy controls (8·0 pg/mL, 3·0-14·0; all comparisons between patients with CT-negative MRI-positive findings and other groups p<0·0001)., Interpretation: Analysis of blood GFAP concentrations using prototype assays on a point-of-care platform within 24 h of injury might improve detection of TBI and identify patients who might need subsequent MRI and follow-up., Funding: National Institute of Neurological Disorders and Stroke and US Department of Defense., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

T2-weighted (T2W) imaging is the most important sequence for detection of acute traumatic spinal cord pathology in clinical practice. Intramedullary hemorrhage on T2W imaging is associated with some component of irreversible injury and arguably the most robust MR imaging predictor of injury severity. The MR imaging appearance of the injured spinal cord in the early stages of injury is highly dynamic, and the time delay from injury to imaging must be considered in image interpretation. Diffusion imaging offers promise as specific tool for interrogating spinal cord integrity, although well-designed, prospective clinical studies validating its application remain limited., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques., Purpose: To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA., Study Type: Longitudinal study for comparison of progressive and nonprogressive subjects., Population: In all, 102 subjects with and without radiographic signs of hip osteoarthritis., Field Strength/sequence: 3T, SPGR 3D MAPSS T 1ρ /T 2 , intermediate-weighted fat-suppressed fast spin-echo (FSE)., Assessment: Multidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS)., Statistical Tests: Analysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons., Results: Subjects in the later stages of the disease had an increased SHOMRI score (P < 0.0001), increased KL (P = 0.0012), and older age (P < 0.0001). Subjects in the healthier group showed intact cartilage and less pain. Subjects found between these two groups had a range of symptoms. Analysis of this subgroup identified knee biomechanics (P < 0.0001) as an initial marker of the disease that is noticeable before the morphological progression and degeneration. Further analysis of an OA subgroup with femoroacetabular impingement (FAI) showed anterior labral tears to be the most significant marker (P = 0.0017) between those FAI subjects with and without OA symptoms., Data Conclusion: The data-driven analysis obtained with TDA proposes new phenotypes of these subjects that partially overlap with the radiographic-based classical disease status classification and also shows the potential for further examination of an early onset biomechanical intervention., Level of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1046-1058., (© 2018 International Society for Magnetic Resonance in Medicine.)

Purpose: To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA., Materials and Methods: We mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T 1ρ and T 2 and R 2 -R 1ρ (1/T 2 -1/T 1ρ ) acquired at 3T and whole-organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov-Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing., Results: The results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T 2 and T 1ρ values (T 2 lateral femur P = 1.45*10 -8 , T 1ρ medial tibia P = 1.05*10 -5 ), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10 -4 ) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R 2 -R 1ρ and the longitudinal progression of cartilage lesions., Conclusion: The analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R 2 -R 1ρ may be an imaging biomarker for early OA., Level of Evidence: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:78-90., (© 2017 International Society for Magnetic Resonance in Medicine.)

Literature examining magnetic resonance imaging (MRI) in acute spinal cord injury (SCI) has focused on cervical SCI. Reproducible systems have been developed for MRI-based grading; however, it is unclear how they apply to thoracic SCI. Our hypothesis is that MRI measures will group as coherent multivariate principal component (PC) ensembles, and that distinct PCs and individual variables will show discriminant validity for predicting early impairment in thoracic SCI. We undertook a retrospective cohort study of 25 patients with acute thoracic SCI who underwent MRI on admission and had American Spinal Injury Association Impairment Scale (AIS) assessment at hospital discharge. Imaging variables of axial grade, sagittal grade, length of injury, thoracolumbar injury classification system (TLICS), maximum canal compromise (MCC), and maximum spinal cord compression (MSCC) were collected. We performed an analytical workflow to detect multivariate PC patterns followed by explicit hypothesis testing to predict AIS at discharge. All imaging variables loaded positively on PC1 (64.3% of variance), which was highly related to AIS at discharge. MCC, MSCC, and TLICS also loaded positively on PC2 (22.7% of variance), while variables concerning cord signal abnormality loaded negatively on PC2. PC2 was highly related to the patient undergoing surgical decompression. Variables of signal abnormality were all negatively correlated with AIS at discharge with the highest level of correlation for axial grade as assessed with the Brain and Spinal Injury Center (BASIC) score. A multiple variable model identified BASIC as the only statistically significant predictor of AIS at discharge, signifying that BASIC best captured the variance in AIS within our study population. Our study provides evidence of convergent validity, construct validity, and clinical predictive validity for the sampled MRI measures of SCI when applied in acute thoracic and thoracolumbar SCI.

Object: Previous studies that have evaluated the prognostic value of abnormal changes in signals on T2-weighted MRI scans of an injured spinal cord have focused on the longitudinal extent of this signal abnormality in the sagittal plane. Although the transverse extent of injury and the degree of spared spinal cord white matter have been shown to be important for predicting outcomes in preclinical animal models of spinal cord injury (SCI), surprisingly little is known about the prognostic value of altered T2 relaxivity in humans in the axial plane., Methods: The authors undertook a retrospective chart review of 60 patients who met the inclusion criteria of this study and presented to the authors' Level I trauma center with an acute blunt traumatic cervical SCI. Within 48 hours of admission, all patients underwent MRI examination, which included axial and sagittal T2 images. Neurological symptoms, evaluated with the grades according to the American Spinal Injury Association (ASIA) Impairment Scale (AIS), at the time of admission and at hospital discharge were correlated with MRI findings. Five distinct patterns of intramedullary spinal cord T2 signal abnormality were defined in the axial plane at the injury epicenter. These patterns were assigned ordinal values ranging from 0 to 4, referred to as the Brain and Spinal Injury Center (BASIC) scores, which encompassed the spectrum of SCI severity., Results: The BASIC score strongly correlated with neurological symptoms at the time of both hospital admission and discharge. It also distinguished patients initially presenting with complete injury who improved by at least one AIS grade by the time of discharge from those whose injury did not improve. The authors' proposed score was rapid to apply and showed excellent interrater reliability., Conclusions: The authors describe a novel 5-point ordinal MRI score for classifying acute SCIs on the basis of axial T2-weighted imaging. The proposed BASIC score stratifies the SCIs according to the extent of transverse T2 signal abnormality during the acute phase of the injury. The new score improves on current MRI-based prognostic descriptions for SCI by reflecting functionally and anatomically significant patterns of intramedullary T2 signal abnormality in the axial plane.

Objective: To determine the clinical relevance, if any, of traumatic intracranial findings on early head computed tomography (CT) and brain magnetic resonance imaging (MRI) to 3-month outcome in mild traumatic brain injury (MTBI)., Methods: One hundred thirty-five MTBI patients evaluated for acute head injury in emergency departments of 3 LEVEL I trauma centers were enrolled prospectively. In addition to admission head CT, early brain MRI was performed 12 ± 3.9 days after injury. Univariate and multivariate logistic regression were used to assess for demographic, clinical, socioeconomic, CT, and MRI features that were predictive of Extended Glasgow Outcome Scale (GOS-E) at 3 months postinjury., Results: Twenty-seven percent of MTBI patients with normal admission head CT had abnormal early brain MRI. CT evidence of subarachnoid hemorrhage was associated with a multivariate odds ratio of 3.5 (p = 0.01) for poorer 3-month outcome, after adjusting for demographic, clinical, and socioeconomic factors. One or more brain contusions on MRI, and ≥4 foci of hemorrhagic axonal injury on MRI, were each independently associated with poorer 3-month outcome, with multivariate odds ratios of 4.5 (p = 0.01) and 3.2 (p = 0.03), respectively, after adjusting for head CT findings and demographic, clinical, and socioeconomic factors., Interpretation: In this prospective multicenter observational study, the clinical relevance of abnormal findings on early brain imaging after MTBI is demonstrated. The addition of early CT and MRI markers to a prognostic model based on previously known demographic, clinical, and socioeconomic predictors resulted in a >2-fold increase in the explained variance in 3-month GOS-E., (Copyright © 2012 American Neurological Association.)

Background and purposeAcute markers of spinal cord injury are essential for both diagnostic and prognostic purposes. The goal of this study was to assess the relationship between early MR imaging biomarkers after acute cervical spinal cord injury and to evaluate their predictive validity of neurologic impairment.Materials and methodsWe performed a retrospective cohort study of 95 patients with acute spinal cord injury and preoperative MR imaging within 24 hours of injury. The American Spinal Injury Association Impairment Scale was used as our primary outcome measure to define neurologic impairment. We assessed several MR imaging features of injury, including axial grade (Brain and Spinal Injury Center score), sagittal grade, length of injury, maximum canal compromise, and maximum spinal cord compression. Data-driven nonlinear principal component analysis was followed by correlation and optimal-scaled multiple variable regression to predict neurologic impairment.ResultsNonlinear principal component analysis identified 2 clusters of MR imaging variables related to 1) measures of intrinsic cord signal abnormality and 2) measures of extrinsic cord compression. Neurologic impairment was best accounted for by MR imaging measures of intrinsic cord signal abnormality, with axial grade representing the most accurate predictor of short-term impairment, even when correcting for surgical decompression and degree of cord compression.ConclusionsThis study demonstrates the utility of applying nonlinear principal component analysis for defining the relationship between MR imaging biomarkers in a complex clinical syndrome of cervical spinal cord injury. Of the assessed imaging biomarkers, the intrinsic measures of cord signal abnormality were most predictive of neurologic impairment in acute spinal cord injury, highlighting the value of axial T2 MR imaging.