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Introduction: We developed a new neuroprognostication method for cardiac arrest (CA) using the relative volume of the most dominant cluster of low apparent diffusion coefficient (ADC) voxels and tested its performance in a multicenter setting., Methods: Adult (>15 years) out-of-hospital CA patients from three different facilities who underwent an MRI 12h after resuscitation were retrospectively analyzed. Patients with unknown long-term prognosis or poor baseline neurologic function were excluded. Average ADCs (mean and median), LADCV (relative volume of low-ADC voxels) and DC-LADCV (relative volume of most dominant cluster of low-ADC voxels) were extracted using different thresholds between 400 and 800 × 10(-6) mm(2) s(-1) at 10 × 10(-6) mm(2) s(-1) intervals. Area under the receiver operating characteristic curve (AUROC) and sensitivity for poor outcome (6-month cerebral performance category score >2) while maintaining 100% specificity were measured., Results: 110 patients were analyzed. Average ADCs showed fair performance with an AUROC of 0.822 (95% confidence interval [CI], 0.744-0.900) for the mean and 0.799 (95% CI, 0.716-0.882) for the median. LADCV showed better performance with a higher AUROC (maximum, 0.925) in an ADC threshold range of 400 to 690 × 10(-6) mm(2) s(-1). DC-LADCV showed the best performance with a higher AUROC (maximum, 0.955) compared with LADCV in an ADC threshold range of 600 to 680 × 10(-6) mm(2) s(-1). DC-LADCV had a high sensitivity for poor outcomes (>80%) in a wide threshold range from 400 to 580 × 10(-6) mm(2) s(-1) with a maximum of 89.2%., Conclusions: Quantitative analysis using DC-LADCV showed impressive performance in determining the prognosis of out-of-hospital CA patients in a multicenter setting., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Background: Symptomatic intracranial vertebral artery (VA) disease has various clinical features and poor neurological outcomes. The disease is believed to result from atherosclerotic stenosis, occlusion, or spontaneous VA dissection. The underlying histopathology of symptomatic intracranial VA disease has only been studied at postmortem, and no in vivo imaging investigation has been conducted despite the increased sophistication of imaging techniques., Methods: The authors performed high-resolution magnetic resonance imaging (HR-MRI) of intracranial vertebral arteries in 9 patients, suspected of a VA pathology by magnetic resonance imaging (MRI), magnetic resonance angiography, and digital subtraction angiography., Results: HR-MRI allowed the authors to determine the following: (1) atherosclerotic plaque is composed of a large lipid core with intraplaque hemorrhage and calcification, (2) nonstenotic atherosclerosis exhibits diffuse vessel wall thickening and plaque protruding toward perforating arteries, and (3) spontaneous VA dissection exhibits large intramural hematoma in a false lumen with complete occlusion of the true lumen. In addition, VA hypoplasia was easily differentiated from atherosclerotic stenosis, by direct visualization of a narrow lumen diameter without arterial wall thickening. Furthermore, etiologic diagnoses based on classical MRI, angiography, and digital subtraction angiography were changed in 3 patients after HR-MRI. Additional information on plaque stability, indicating the possibility of unstable plaque, was found in 4 patients., Conclusions: The application of HR-MRI in stroke patients with VA pathologies enabled the authors to determine the underlying pathophysiologies. These findings could be used to improve risk stratification and treatment decision making in symptomatic intracranial VA disease., (Copyright © 2014 National Stroke Association. Published by Elsevier Inc. All rights reserved.)

Objective: Recent studies suggested quantitative analysis of diffusion-weighted magnetic resonance imaging as a promising tool for early prognostication of cardiac arrest patients. However, most of their methods involve significant manual image handling often subjective and difficult to reproduce. Therefore developing a computerized analysis method using easy-to-define characteristics would be useful., Methods: Comatose out-of-hospital cardiac arrest (OHCA) patients who underwent brain MRI between January 2008 and July 2012 were identified from an OHCA registry. Apparent diffusion coefficient (ADC) axial images were analyzed using a program to detect and characterize clusters of low ADC pixels from six brain regions including frontal, occipital, parietal, rolandic and temporal and basal ganglia region. Identified clusters were ranked according to size, mean ADC and minimum ADC to assess the regional maximum cluster size (MCS), lowest mean ADC (LMEAN) and lowest minimum ADC (LMIN). Their power to predict poor outcome, defined as 6-month CPC 3 or higher, was assessed by contingency table analyses., Results: 51 OHCA patients were eligible during the study period. The sensitivities of MCS, LMEAN and LMIN to detect poor outcome varied according to brain region from 62.5 to 90.0%, 50.0 to 72.5% and 42.5 to 82.5% with their specificities set to 100%, respectively. The MCS of occipital region showed most favorable test profile (sensitivity 90%, specificity 100%; AUROC 0.940, 95% confidence interval 0.874-1.000)., Conclusion: The cluster-based computerized image analysis might be a simple but useful method for prediction of poor neurologic outcome. Future studies validating its prognostic performance are required., (Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.)

Background: MRI-based prognostication of comatose cardiac arrest survivors has shown promising results. However, the technique has not been validated in patients treated with therapeutic hypothermia and it is unknown how it might add to NSE-based prognostication. We sought to evaluate the prognostic performance of regional apparent diffusion coefficient (ADC) in comatose out-of-hospital cardiac arrest (OHCA) patients treated with mild hypothermia and its added value to NSE-based prognostication., Methods: An OHCA registry was analyzed to identify OHCA patients older than 15 who were treated with therapeutic hypothermia and underwent brain MRI between 2008 and 2011. Quantitative measurement of regional ADCs was performed by a radiologist blinded to the clinical outcome., Results: Of the 43 eligible patients, 11 (18.6 %) achieved a good outcome (6-month CPC of 1 or 2). The regional ADC of the occipital cortex showed the highest discriminatory power with an area under the curve of receiver operating characteristic (AUROC) of 0.943 (95 % CI, 0.872-1.000) and predicted poor outcomes with a sensitivity of 90.6 % and a specificity of 100 %. The AUROC for NSE levels (48-h) was 0.911 (95 % CI, 0.801-1.000) which was significantly correlated with the regional ADC (Pearson's r = -0.674, p < .001). The ADC-based predictions identified an additional 5 (35.7 %) poor outcome patients out of 14 with 48-h NSE levels less than 78.9 ng/mL, which is the cutoff point suggested in a previous study. However, additional prognostic information was not provided when the 48-h NSE levels were >78.9 ng/mL., Conclusions: Regional ADC-based prognostication was accurate in OHCA patients who were treated with mild hypothermia. However, it only provided additional prognostic information when the 48-h NSE levels indicated a good prognosis (48-h NSE <78.9 ng/mL).

Introduction: Transient global amnesia (TGA) is a benign disease with a sudden-onset, transient memory disturbance. Characteristic punctate high-signal intensity lesions in the hippocampus on diffusion-weighted imaging (DWI) have been reported in variable frequencies. We investigated the influence of the timing of DWI and the magnetic field strength on the lesion detectability in TGA., Methods: Seventy-three patients diagnosed with TGA underwent DWI within 24 h after the symptom onset and again on day 3 at either 1.5 (n = 31) or 3 T (n = 42). The patients were divided into three subgroups according to the time lapse after the symptom onset to the first DWI (0-6 h, 6-12 h, and 12-24 h)., Results: The detection rate of the lesions during the first 24 h rose statistically significantly with increased time lapse after the symptom onset (34% in 0-6 h, 62% in 6-12 h, and 67% in 12-24 h). It increased up to 75% on day 3. The detection rate was higher at 3 T than at 1.5 T in all time points (41% vs. 27% in 0-6 h, 70% vs. 44% in 6-12 h, 80% vs. 57% in 12-24 h, and 86% vs. 61% on day 3), but the statistical significance was achieved only on day 3., Conclusion: Awareness of the different lesion detectability on DWI according to the time lapse after the symptom onset can help in diagnosing the patients with suspected TGA. High field strength is another important factor to increase the lesion detectability on DWI.

Accurate assessment of nodal metastasis in head and neck squamous cell carcinoma (SCC) is important, and diffusion-weighted imaging (DWI) has emerged as a potential technique in differentiating benign from malignant lymph nodes (LNs). This study aims to evaluate the diagnostic performance of texture analysis using apparent diffusion coefficient (ADC) data of multi-shot echo-planar imaging-based DWI (msEPI-DWI) in predicting metastatic LNs of head and neck SCC. 36 patients with pathologically proven head and neck SCC were included in this study. A total of 204 MRI-detected LNs, including 176 subcentimeter-sized LNs, were assigned to metastatic or benign groups. Texture features of LNs were compared using independent t-test. Hierarchical cluster analysis was performed to exclude redundant features. Multivariate logistic regression and receiver operating characteristic analysis were performed to assess the diagnostic performance. The discriminative texture features for predicting metastatic LNs were complexity, energy and roundness. Areas under the curves (AUCs) for diagnosing metastasis in all/subcentimeter-sized LNs were 0.829/0.767 using complexity, 0.699/0.685 using energy and 0.671/0.638 using roundness, respectively. The combination of three features resulted in higher AUC values of 0.836/0.781. In conclusion, texture analysis of ADC data using msEPI-DWI could be a useful tool for nodal staging in head and neck SCC. [ABSTRACT FROM AUTHOR]