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Background: Early prehospital stroke identification is crucial for goal directed hospital admission especially in rural areas. However, clinical prehospital stroke scales are designed to identify any stroke but cannot sufficiently differentiate hemorrhagic from ischemic stroke, including large vessel occlusion (LVO) amenable to mechanical thrombectomy. We report on a novel small, portable and battery driven point-of-care ultrasound system (SONAS®) specifically developed for mobile non-invasive brain perfusion ultrasound (BPU) measurement after bolus injection of an echo-enhancing agent suitable for the use in prehospital stroke diagnosis filling a current, unmet and critical need for LVO identification., Methods: In a phase I study of healthy volunteers we performed comparative perfusion-weighted magnetic resonance imaging (PWI) and BPU measurements, including safety analysis., Results: Twelve volunteers (n = 7 females, n = 5 males, age ranging between 19 and 55 years) tolerated the measurement extremely well including analysis of blood-brain barrier integrity, and the correlation coefficient between the generated time kinetic curves after contrast agent bolus between PWI and BPU transducers ranged between 0.89 and 0.76., Conclusions: Mobile BPU using the SONAS® device is feasible and safe with results comparable to PWI. When applied in conjunction with prehospital stroke scales this may lead to a more accurate stroke diagnosis and patients bypassing regular stroke units to comprehensive stroke centers. Further studies are needed in acute stroke patients and in the prehospital phase including assessment of immediate and long-term morbidity and mortality in stroke., Trial Registration: Clinical trials.gov, registered 28.Sep.2017, Identifier: NCT03296852., (© 2022. The Author(s).)

Background: Multiparametric MRI (mpMRI) is commonly recommended as a triage test prior to any prostate biopsy. However, there exists limited consensus on which patients with a negative prostate mpMRI could avoid prostate biopsy., Purpose: To identify which patient could safely avoid prostate biopsy when the prostate mpMRI is negative, via a radiomics-based machine learning approach., Study Type: Retrospective., Subjects: Three hundred thirty patients with negative prostate 3T mpMRI between January 2016 and December 2018 were included., Field Strength/sequence: A 3.0 T/T2-weighted turbo spin echo (TSE) imaging (T 2 WI) and diffusion-weighted imaging (DWI)., Assessment: The integrative machine learning (iML) model was trained to predict negative prostate biopsy results, utilizing both radiomics and clinical features. The final study cohort comprised 330 consecutive patients with negative mpMRI (PI-RADS < 3) who underwent systematic transrectal ultrasound-guided (TRUS) or MR-ultrasound fusion (MRUS) biopsy within 6 months. A secondary analysis of biopsy naïve subcohort (n = 227) was also conducted., Statistical Tests: The Mann-Whitney U test and Chi-Squared test were utilized to evaluate the significance of difference of clinical features between prostate biopsy positive and negative groups. The model performance was validated using leave-one-out cross-validation (LOOCV) and measured by AUC, sensitivity, specificity, and negative predictive value (NPV)., Results: Overall, 306/330 (NPV 92.7%) of the final study cohort patients had negative biopsies, and 207/227 (NPV 91.2%) of the biopsy naïve subcohort patients had negative biopsies. Our iML model achieved NPVs of 98.3% and 98.0% for the study cohort and subcohort, respectively, superior to prostate-specific antigen density (PSAD)-based risk assessment with NPVs of 94.9% and 93.9%, respectively., Data Conclusion: The proposed iML model achieved high performance in predicting negative prostate biopsy results for patients with negative mpMRI. With improved NPVs, the proposed model can be used to stratify patients who in whom we might obviate biopsies, thus reducing the number of unnecessary biopsies., Evidence Level: 3 TECHNICAL EFFICACY: Stage 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Background and Purpose: Acute leptomeningeal collateral flow is vital for maintaining perfusion to penumbral tissue in acute ischemic stroke caused by large-vessel occlusion. In this study, we aimed to investigate the clinically available indicators of leptomeningeal collateral variability in embolic large-vessel occlusion., Materials and Methods: Among prospectively registered consecutive patients with acute embolic anterior circulation large-vessel occlusion treated with thrombectomy, we analyzed 108 patients admitted from January 2015 to December 2019 who underwent evaluation of leptomeningeal collateral status on pretreatment CTA. Clinical characteristics, extent of leukoaraiosis on MR imaging, embolic stroke subtype, time of imaging, occlusive thrombus characteristics, presenting stroke severity, and clinical outcome were collected. The clinical indicators of good collateral status (>50% collateral filling of the occluded territory) were analyzed using multivariate logistic regression analysis., Results: Good collateral status was present in 67 patients (62%) and associated with independent functional outcomes at 3 months. Reduced leukoaraiosis (total Fazekas score, 0-2) was positively related to good collateral status (OR, 9.57; 95% CI, 2.49-47.75), while the cardioembolic stroke mechanism was inversely related to good collateral status (OR, 0.17; 95% CI, 0.02-0.87). In 82 patients with cardioembolic stroke, shorter thrombus length (OR, 0.91 per millimeter increase; 95% CI, 0.82-0.99) and reduced leukoaraiosis (OR, 5.79; 95% CI, 1.40-29.61) were independently related to good collateral status., Conclusions: Among patients with embolic large-vessel occlusion, reduced leukoaraiosis, noncardiac embolism mechanisms including embolisms of arterial or undetermined origin, and shorter thrombus length in cardioembolism are indicators of good collateral flow., (© 2022 by American Journal of Neuroradiology.)

In stroke care, the extent of irreversible brain injury, termed infarct core, plays a key role in determining eligibility for acute treatments, such as intravenous thrombolysis and endovascular reperfusion therapies. Many of the pivotal randomized clinical trials testing those therapies used MRI Diffusion-Weighted Imaging (DWI) or CT Perfusion (CTP) to define infarct core. Unfortunately, these modalities are not available 24/7 outside of large stroke centers. As such, there is a need for accurate infarct core determination using faster and more widely available imaging modalities including Non-Contrast CT (NCCT) and CT Angiography (CTA). Prior studies have suggested that CTA provides improved predictions of infarct core relative to NCCT; however, this assertion has never been numerically quantified by automatic medical image computing pipelines using acquisition protocols not confounded by different scanner manufacturers, or other protocol settings such as exposure times, kilovoltage peak, or imprecision due to contrast bolus delays. In addition, single-phase CTA protocols are at present designed to optimize contrast opacification in the arterial phase. This approach works well to maximize the sensitivity to detect vessel occlusions, however, it may not be the ideal timing to enhance the ischemic infarct core signal (ICS). In this work, we propose an image analysis pipeline on CT-based images of 88 acute ischemic stroke (AIS) patients drawn from a single dynamic acquisition protocol acquired at the acute ischemic phase. We use the first scan at the time of the dynamic acquisition as a proxy for NCCT, and the rest of the scans as a proxy for CTA scans, with bolus imaged at different brain enhancement phases. Thus, we use the terms "NCCT" and "CTA" to refer to them. This pipeline enables us to answer the questions "Does the injection of bolus enhance the infarct core signal?" and "What is the ideal bolus timing to enhance the infarct core signal?" without being influenced by aforementioned factors such as scanner model, acquisition settings, contrast bolus delay, and human reader errors. We use reference MRI DWI images acquired after successful recanalization acting as our gold standard for infarct core. The ICS is quantified by calculating the difference in intensity distribution between the infarct core region and its symmetrical healthy counterpart on the contralateral hemisphere of the brain using a metric derived from information theory, the Kullback-Leibler divergence (KL divergence). We compare the ICS provided by NCCT and CTA and retrieve the optimal timing of CTA bolus to maximize the ICS. In our experiments, we numerically confirm that CTAs provide greater ICS compared to NCCT. Then, we find that, on average, the ideal CTA acquisition time to maximize the ICS is not the current target of standard CTA protocols, i.e., during the peak of arterial enhancement, but a few seconds afterward (median of 3 s; 95% CI [1.5, 3.0]). While there are other studies comparing the prediction potential of ischemic infarct core from NCCT and CTA images, to the best of our knowledge, this analysis is the first to perform a quantitative comparison of the ICS among CT based scans, with and without bolus injection, acquired using the same scanning sequence and a precise characterization of the bolus uptake, hence, reducing potential confounding factors., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Several deep learning-based techniques have been developed for prostate cancer (PCa) detection using multiparametric magnetic resonance imaging (mpMRI), but few of them have been rigorously evaluated relative to radiologists' performance or whole-mount histopathology (WMHP)., Purpose: To compare the performance of a previously proposed deep learning algorithm, FocalNet, and expert radiologists in the detection of PCa on mpMRI with WMHP as the reference., Study Type: Retrospective, single-center study., Subjects: A total of 553 patients (development cohort: 427 patients; evaluation cohort: 126 patients) who underwent 3-T mpMRI prior to radical prostatectomy from October 2010 to February 2018., Field Strength/sequence: 3-T, T2-weighted imaging and diffusion-weighted imaging., Assessment: FocalNet was trained on the development cohort to predict PCa locations by detection points, with a confidence value for each point, on the evaluation cohort. Four fellowship-trained genitourinary (GU) radiologists independently evaluated the evaluation cohort to detect suspicious PCa foci, annotate detection point locations, and assign a five-point suspicion score (1: least suspicious, 5: most suspicious) for each annotated detection point. The PCa detection performance of FocalNet and radiologists were evaluated by the lesion detection sensitivity vs. the number of false-positive detections at different thresholds on suspicion scores. Clinically significant lesions: Gleason Group (GG) ≥ 2 or pathological size ≥ 10 mm. Index lesions: the highest GG and the largest pathological size (secondary)., Statistical Tests: Bootstrap hypothesis test for the detection sensitivity between radiologists and FocalNet., Results: For the overall differential detection sensitivity, FocalNet was 5.1% and 4.7% below the radiologists for clinically significant and index lesions, respectively; however, the differences were not statistically significant (P = 0.413 and P = 0.282, respectively)., Data Conclusion: FocalNet achieved slightly lower but not statistically significant PCa detection performance compared with GU radiologists. Compared with radiologists, FocalNet demonstrated similar detection performance for a highly sensitive setting (suspicion score ≥ 1) or a highly specific setting (suspicion score = 5), while lower performance in between., Level of Evidence: 3 TECHNICAL EFFICACY STAGE: 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Background and Purpose: In symptomatic intracranial atherosclerotic stenosis (ICAS), borderzone infarct pattern and perfusion mismatch are associated with increased risk of recurrent strokes, which may reflect the shared underlying mechanism of hypoperfusion distal to the intracranial atherosclerosis. Accordingly, we hypothesized a correlation between hypoperfusion volumes and ICAS infarct patterns based on the respective underlying mechanistic subtypes., Methods: We conducted a retrospective analysis of consecutive symptomatic ICAS cases, acute strokes due to subocclusive (50%-99%) intracranial stenosis. The following mechanistic subtypes were assigned based on the infarct pattern on the diffusion-weighted imaging: Branch occlusive disease (BOD), internal borderzone (IBZ), and thromboembolic (TE). Perfusion parameters, obtained concurrently with the MRI, were studied in each group., Results: A total of 42 patients (57% women, mean age 71 ± 13 years old) with symptomatic ICAS received MRI within 24 h of acute presentation. Fourteen IBZ, 11 BOD, and 17 TE patterns were identified. IBZ pattern yielded higher total T max > 4 s and T max > 6 s perfusion delay volumes, as well as corresponding T max  > 4 s and T max  > 6 s mismatch volume, compared to BOD. TE pattern exhibited greater median T max  > 6 s hypoperfusion delay in volume compared to BOD. In IBZ versus TE, the volume difference between T max > 4 s and T max > 6 s (Δ T max  > 4 s - T max  > 6 s) was substantially greater., Conclusion: ICAS infarct patterns, in keeping with their respective underlying mechanisms, may correlate with distinct perfusion profiles., (© 2021 American Society of Neuroimaging.)

Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS recommend the use of MRI to determine eligibility for thrombolysis, but radiology assessments have high inter-reader variability. In this work, we present deep learning models that leverage MRI diffusion series to classify TSS based on clinically validated thresholds. We propose an intra-domain task-adaptive transfer learning method, which involves training a model on an easier clinical task (stroke detection) and then refining the model with different binary thresholds of TSS. We apply this approach to both 2D and 3D CNN architectures with our top model achieving an ROC-AUC value of 0.74, with a sensitivity of 0.70 and a specificity of 0.81 for classifying TSS < 4.5 h. Our pretrained models achieve better classification metrics than the models trained from scratch, and these metrics exceed those of previously published models applied to our dataset. Furthermore, our pipeline accommodates a more inclusive patient cohort than previous work, as we did not exclude imaging studies based on clinical, demographic, or image processing criteria. When applied to this broad spectrum of patients, our deep learning model achieves an overall accuracy of 75.78% when classifying TSS < 4.5 h, carrying potential therapeutic implications for patients with unknown TSS., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Stroke is an acute cerebral vascular disease that is likely to cause long-term disabilities and death. Immediate emergency care with accurate diagnosis of computed tomographic (CT) images is crucial for dealing with a hemorrhagic stroke. However, due to the high variability of a stroke's location, contrast, and shape, it is challenging and time-consuming even for experienced radiologists to locate them. In this paper, we propose a U-net based deep learning framework to automatically detect and segment hemorrhage strokes in CT brain images. The input of the network is built by concatenating the flipped image with the original CT slice which introduces symmetry constraints of the brain images into the proposed model. This enhances the contrast between hemorrhagic area and normal brain tissue. Various Deep Learning topologies are compared by varying the layers, batch normalization, dilation rates, and pre-train models. This could increase the respective filed and preserves more information on lesion characteristics. Besides, the adversarial training is also adopted in the proposed network to improve the accuracy of the segmentation. The proposed model is trained and evaluated on two different datasets, which achieve the competitive performance with human experts with the highest location accuracy 0.9859 for detection, 0.8033 Dice score, and 0.6919 IoU for segmentation. The results demonstrate the effectiveness, robustness, and advantages of the proposed deep learning model in automatically hemorrhage lesion diagnosis, which make it possible to be a clinical decision support tool in stroke diagnosis.

Multi-parametric MRI (mpMRI) is a powerful non-invasive tool for diagnosing prostate cancer (PCa) and is widely recommended to be performed before prostate biopsies. Prostate Imaging Reporting and Data System version (PI-RADS) is used to interpret mpMRI. However, when the pre-biopsy mpMRI is negative, PI-RADS 1 or 2, there exists no consensus on which patients should undergo prostate biopsies. Recently, radiomics has shown great abilities in quantitative imaging analysis with outstanding performance on computer-aid diagnosis tasks. We proposed an integrative radiomics-based approach to predict the prostate biopsy results when pre-biopsy mpMRI is negative. Specifically, the proposed approach combined radiomics features and clinical features with machine learning to stratify positive and negative biopsy groups among negative mpMRI patients. We retrospectively reviewed all clinical prostate MRIs and identified 330 negative mpMRI scans, followed by biopsy results. Our proposed model was trained and validated with 10-fold cross-validation and reached the negative predicted value (NPV) of 0.99, the sensitivity of 0.88, and the specificity of 0.63 in receiver operating characteristic (ROC) analysis. Compared with results from existing methods, ours achieved 11.2% higher NPV and 87.2% higher sensitivity with a cost of 23.2% less specificity.

Background and Purpose: When mapping the ischemic core and penumbra in patients with acute ischemic stroke using perfusion imaging, the core is currently delineated by applying the same threshold value for relative CBF at all time points from onset to imaging. We investigated whether the degree of perfusion abnormality and optimal perfusion parameter thresholds for defining ischemic core vary with time from onset to imaging., Materials and Methods: In a prospectively maintained registry, consecutive patients were analyzed who had ICA or M1 occlusion, baseline perfusion and diffusion MR imaging, treatment with IV tPA and/or endovascular thrombectomy, and a witnessed, well-documented time of onset. Ten superficial and deep MCA ROIs were analyzed in ADC and perfusion-weighted images., Results: Among the 66 patients meeting entry criteria, onset-to-imaging time was 162 minutes (range, 94-326 minutes). Of the 660 ROIs analyzed, 164 (24.8%) showed severely or moderately reduced ADC (ADC ≤ 620, ischemic core), and 496 (75.2%), mildly reduced or normal ADC (ADC  > 620). In ischemic core ADC regions, longer onset-to-imaging times were associated with more highly abnormal perfusion parameters-relative CBF: Spearman correlation, r = -0.22, P  = .005; relative CBV: r = -0.41, P  < .001; MTT: - r = -0.29, P  < .001; and time-to-maximum: r  = 0.35, P  < .001. As onset-to-imaging times increased, the best cutoff values for relative CBF and relative CBV to discriminate core from noncore tissue became progressively lower and overall accuracy of the core tissue definition increased., Conclusions: Perfusion abnormalities in ischemic core regions become progressively more abnormal with longer intervals from onset to imaging. Perfusion parameter value thresholds that best delineate ischemic core are more severely abnormal and have higher accuracy with longer onset-to-imaging times., (© 2020 by American Journal of Neuroradiology.)

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients' presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process., Competing Interests: Conflict of interest statement: The authors declare no competing interests. NC and XL are Genentech/Roche paid employees. Genentech/Roche had no role in study design, data collection, and interpretation, or the decision to submit the work for publication., (© The Author(s), 2020.)

Objective: Transcranial Doppler (TCD) ultrasonography measures pulsatile cerebral blood flow velocity in the arteries and veins of the head and neck. Similar to other real-time measurement modalities, especially in healthcare, the identification of high-quality signals is essential for clinical interpretation. Our goal is to identify poor quality beats and remove them prior to further analysis of the TCD signal., Methods: We selected objective features for this purpose including Euclidean distance between individual and average beat waveforms, cross-correlation between individual and average beat waveforms, ratio of the high-frequency power to the total beat power, beat length, and variance of the diastolic portion of the beat waveform. We developed an iterative outlier detection algorithm to identify and remove the beats that are different from others in a recording. Finally, we tested the algorithm on a dataset consisting of more than 15 h of TCD data recorded from 48 stroke and 34 in-hospital control subjects., Results: We assessed the performance of the algorithm in the improvement of estimation of clinically important TCD parameters by comparing them to that of manual beat annotation. The results show that there is a strong correlation between the two, that demonstrates the algorithm has successfully recovered the clinically important features. We obtained significant improvement in estimating the TCD parameters using the algorithm accepted beats compared to using all beats., Significance: Our algorithm provides a valuable tool to clinicians for automated detection of the reliable portion of the data. Moreover, it can be used as a pre-processing tool to improve the data quality for automated diagnosis of pathologic beat waveforms using machine learning.

Cerebral Blood Flow Velocity waveforms acquired via Transcranial Doppler (TCD) can provide evidence for cerebrovascular occlusion and stenosis. Thrombolysis in Brain Ischemia (TIBI) flow grades are widely used for this purpose, but require subjective assessment by expert evaluators to be reliable. In this work we seek to determine whether TCD morphology can be objectively assessed using an unsupervised machine learning approach to waveform categorization. TCD beat waveforms were recorded at multiple depths from the Middle Cerebral Arteries of 106 subjects; 33 with Large Vessel Occlusion (LVO). From each waveform, three morphological features were extracted, quantifying onset of maximal velocity, systolic canopy length, and the number/prominence of peaks/troughs. Spectral clustering identified groups implicit in the resultant three-dimensional feature space, with gap statistic criteria establishing the optimal cluster number. We found that gap statistic disparity was maximized at four clusters, referred to as flow types I, II, III, and IV. Types I and II were primarily composed of control subject waveforms, whereas types III and IV derived mainly from LVO patients. Cluster morphologies for types I and IV aligned clearly with Normal and Blunted TIBI flows, respectively. Types II and III represented commonly observed flow-types not delineated by TIBI, which nonetheless deviate from normal and blunted flows. We conclude that important morphological variability exists beyond that currently quantified by TIBI in populations experiencing or at-risk for acute ischemic stroke, and posit that the observed flow-types provide the foundation for objective methods of real-time automated flow type classification., Competing Interests: At the time this research was conducted, authors ST, CT, KJ, AD, NC, SW, and RH were salaried employees of Neural Analytics, Inc., and TD was a paid consultant. FS is also a former paid consultant. All authors either hold stock or stock options in the company. The funding institution, Neural Analytics, Inc., holds numerous patents related to transcranial doppler technology from which the authors do not directly stand to benefit. Our commercial affiliation does not alter our adherence to PLOS ONE policies on sharing data and materials. This research was also supported in part by NINDS-1R43NS105340.

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

The issue of whether market fish can be involved in the transmission of Toxoplasma gondii in the marine environment is highly debated since toxoplasmosis has been diagnosed frequently in cetaceans stranded along the Mediterranean coastlines in recent times. To support the hypothesis that fishes can harbour and effectively transmit the parasite to top-of-the-food-chain marine organisms and to human consumers of fishery products, a total of 1,293 fishes from 17 species obtained from wholesale and local fish markets were examined for T. gondii DNA. Real-time PCR was performed in samples obtained by separately pooling intestines, gills and skin/muscles collected from each fish species. Thirty-two out of 147 pooled samples from 12 different fish species were found contaminated with T. gondii DNA that was detected in 16 samples of skin/muscle and in 11 samples of both intestine and gills. Quantitative analysis of amplified DNA performed by both real-time PCR and digital PCR (dPCR) confirmed that positive fish samples were contaminated with Toxoplasma genomic DNA to an extent of 6.10 × 10 -2 to 2.77 × 10 4  copies/ml (quantitative PCR) and of 1 to 5.7 × 10 4  copies/ml (dPCR). Fishes are not considered competent biological hosts for T. gondii; nonetheless, they can be contaminated with T. gondii oocysts flowing via freshwater run-offs (untreated sewage discharges, soil flooding) into the marine environment, thus acting as mechanical carriers. Although the detection of viable and infective T. gondii oocysts was not the objective of this investigation, the results here reported suggest that fish species sold for human consumption can be accidentally involved in the transmission route of the parasite in the marine environment and that the risk of foodborne transmission of toxoplasmosis to fish consumers should be further investigated., (© 2019 The Authors. Zoonoses and Public Health Published by Blackwell Verlag GmbH.)

Background and Purpose: Vessel wall imaging can identify intracranial atherosclerotic plaque and give clues about its components. We aimed to investigate whether the plaque hyperintensity in the middle cerebral artery on T2-weighted vessel wall imaging is associated with ischemic stroke., Materials and Methods: We retrospectively reviewed our institutional vessel wall MR imaging data base. Patients with an acute ischemic stroke within 7-day onset in the MCA territory were enrolled. Patients with stroke and stenotic MCA plaque (stenosis degree, ≥50%) were included for analysis. Ipsilateral MCA plaque was defined as symptomatic, and contralateral plaque, as asymptomatic. Plaque was manually delineated on T2-weighted vessel wall imaging. The plaque signal was normalized to the ipsilateral muscle signal. The thresholds and volume of normalized plaque signal were investigated using logistic regression and receiver operating characteristic analysis to determine the association between normalized plaque signal and stroke., Results: One hundred eight stenotic MCAs were analyzed (from 88 patients, 66 men; mean age, 58 ± 15 years), including 72 symptomatic and 36 asymptomatic MCA plaques. Symptomatic MCA plaque showed larger plaque hyperintensity volume compared with asymptomatic MCA plaque. The logistic regression model incorporating stenosis degree, remodeling ratio, and normalized plaque signal 1.3-1.4 (OR, 6.25; 95% CI, 1.90-20.57) had a higher area under curve in differentiating symptomatic/asymptomatic MCA plaque, compared with a model with only stenosis degree and remodeling ratio (area under curve, 0.884 versus 0.806; P =.008)., Conclusions: The MCA plaque hyperintensity on T2-weighted vessel wall imaging is independently associated with ischemic stroke and adds value to symptomatic MCA plaque classification. Measuring the normalized signal intensity may serve as a practical and integrative approach to the analysis of intracranial atherosclerotic plaque., (© 2019 by American Journal of Neuroradiology.)

Transcranial Doppler (TCD) ultrasound has been demonstrated to be a valuable tool for assessing cerebral hemodynamics via measurement of cerebral blood flow velocity (CBFV), with a number of established clinical indications. However, CBFV waveform analysis depends on reliable pulse onset detection, an inherently difficult task for CBFV signals acquired via TCD. We study the application of a new algorithm for CBFV pulse segmentation, which locates pulse onsets in a sequential manner using a moving difference filter and adaptive thresholding. The test data set used in this study consists of 92,012 annotated CBFV pulses, whose quality is representative of real world data. On this test set, the algorithm achieves a true positive rate of 99.998% (2 false negatives), positive predictive value of 99.998% (2 false positives), and mean temporal offset error of 6.10 ± 4.75 ms. We do note that in this context, the way in which true positives, false positives, and false negatives are defined caries some nuance, so care should be taken when drawing comparisons to other algorithms. Additionally, we find that 97.8% and 99.5% of onsets are detected within 10 and 30 ms, respectively, of the true onsets. The algorithm's performance in spite of the large degree of variation in signal quality and waveform morphology present in the test data suggests that it may serve as a valuable tool for the accurate and reliable identification of CBFV pulse onsets in neurocritical care settings., (Copyright © 2019 Canac, Ranjbaran, O'Brien, Asgari, Scalzo, Thorpe, Jalaleddini, Thibeault, Wilk and Hamilton.)

Data regarding the association between subclinical thyroid dysfunction and clinical outcomes in ischemic stroke patients with intravenous thrombolysis (IVT) are limited. We aimed to investigate the predictive value of subclinical thyroid dysfunction in END, functional outcome and mortality at 3 months among IVT patients. We prospectively recruited 563 IVT patients from 5 stroke centers in China. Thyroid function status was classified as subclinical hypothyroidism, subclinical hyperthyroidism (SHyper) and euthyroidism. The primary outcome was END, defined as ≥ 4 point in the NIHSS score within 24 h after IVT. Secondary outcomes included 3-month functional outcome and mortality. Of the 563 participants, END occurred in 14.7%, poor outcome in 50.8%, and mortality in 9.4%. SHyper was an independent predictor of END [odd ratio (OR), 4.35; 95% confidence interval [CI], 1.86-9.68, P = 0.003], 3-month poor outcome (OR, 3.24; 95% CI, 1.43-7.33, P = 0.005) and mortality [hazard ratio, 2.78; 95% CI, 1.55-5.36, P = 0.003]. Subgroup analysis showed that there was no significant relationship between SHyper and clinical outcomes in IVT patients with endovascular therapy. In summary, SHyper is associated with increased risk of END, and poor outcome and mortality at 3 months in IVT patients without endovascular therapy.

Advances in predictive analytics and machine learning supported by an ever-increasing wealth of data and processing power are transforming almost every industry. Accuracy and precision of predictive analytics have significantly increased over the past few years and are evolving at an exponential pace. There have been significant breakthroughs in using Predictive Analytics in healthcare where it is held as the foundation of precision medicine. Yet, although the research in the field is expanding with the profuse volume of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Regardless of the status of its current contribution, the field of predictive analytics is expected to fundamentally change the way we diagnose and treat diseases, as well as the conduct of biomedical science research. In this review, we describe the main tools and techniques in predictive analytics and will analyze the trends in application of these techniques over the recent years. We will also provide examples of its application in medicine and more specifically in stroke and neurovascular research and outline current limitations.

The low cost, simple, noninvasive, and continuous measurement of cerebral blood flow velocity (CBFV) by transcranial Doppler is becoming a common clinical tool for the assessment of cerebral hemodynamics. CBFV monitoring can also help with noninvasive estimation of intracranial pressure and evaluation of mild traumatic brain injury. Reliable CBFV waveform analysis depends heavily on its accurate beat-to-beat delineation. However, CBFV is inherently contaminated with various types of noise/artifacts and has a wide range of possible pathological waveform morphologies. Thus, pulse onset detection is in general a challenging task for CBFV signal. In this paper, we conducted a comprehensive comparative analysis of three popular pulse onset detection methods using a large annotated dataset of 92,794 CBFV pulses-collected from 108 subarachnoid hemorrhage patients admitted to UCLA Medical Center. We compared these methods not only in terms of their accuracy and computational complexity, but also for their sensitivity to the selection of their parameters' values. The results of this comprehensive study revealed that using optimal values of the parameters obtained from sensitivity analysis, one method can achieve the highest accuracy for CBFV pulse onset detection with true positive rate (TPR) of 97.06% and positive predictivity value (PPV) of 96.48%, when error threshold is set to just less than 10 ms. We conclude that the high accuracy and low computational complexity of this method (average running time of 4ms/pulse) makes it a reliable algorithm for CBFV pulse onset detection.

Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning models to classify the TSS. We also propose a deep-learning model to extract hidden representations for the MR perfusion-weighted images and demonstrate classification improvement by incorporating these additional deep features. The cross-validation results show that our best classifier achieved an area under the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value of 0.609, outperforming existing methods. We show that the features generated by our deep-learning algorithm correlate with the MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This paper advances magnetic resonance imaging analysis one-step-closer to an operational decision support tool for stroke treatment guidance.

Purpose: The purpose of the study was to propose a deep transfer learning (DTL)-based model to distinguish indolent from clinically significant prostate cancer (PCa) lesions and to compare the DTL-based model with a deep learning (DL) model without transfer learning and PIRADS v2 score on 3 Tesla multi-parametric MRI (3T mp-MRI) with whole-mount histopathology (WMHP) validation., Methods: With IRB approval, 140 patients with 3T mp-MRI and WMHP comprised the study cohort. The DTL-based model was trained on 169 lesions in 110 arbitrarily selected patients and tested on the remaining 47 lesions in 30 patients. We compared the DTL-based model with the same DL model architecture trained from scratch and the classification based on PIRADS v2 score with a threshold of 4 using accuracy, sensitivity, specificity, and area under curve (AUC). Bootstrapping with 2000 resamples was performed to estimate the 95% confidence interval (CI) for AUC., Results: After training on 169 lesions in 110 patients, the AUC of discriminating indolent from clinically significant PCa lesions of the DTL-based model, DL model without transfer learning and PIRADS v2 score ≥ 4 were 0.726 (CI [0.575, 0.876]), 0.687 (CI [0.532, 0.843]), and 0.711 (CI [0.575, 0.847]), respectively, in the testing set. The DTL-based model achieved higher AUC compared to the DL model without transfer learning and PIRADS v2 score ≥ 4 in discriminating clinically significant lesions in the testing set., Conclusion: The DeLong test indicated that the DTL-based model achieved comparable AUC compared to the classification based on PIRADS v2 score (p = 0.89).

Purpose: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses., Methods: With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution's pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes., Results: We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8-14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0-13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%., Conclusions: The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

Objective: To investigate whether hemodynamic features of symptomatic intracranial atherosclerotic stenosis (sICAS) might correlate with the risk of stroke relapse, using a computational fluid dynamics (CFD) model., Methods: In a cohort study, we recruited patients with acute ischemic stroke attributed to 50 to 99% ICAS confirmed by computed tomographic angiography (CTA). With CTA-based CFD models, translesional pressure ratio (PR = pressure poststenotic /pressure prestenotic ) and translesional wall shear stress ratio (WSSR = WSS stenotic - throat /WSS prestenotic ) were obtained in each sICAS lesion. Translesional PR ≤ median was defined as low PR and WSSR ≥4th quartile as high WSSR. All patients received standard medical treatment. The primary outcome was recurrent ischemic stroke in the same territory (SIT) within 1 year., Results: Overall, 245 patients (median age = 61 years, 63.7% males) were analyzed. Median translesional PR was 0.94 (interquartile range [IQR] = 0.87-0.97); median translesional WSSR was 13.3 (IQR = 7.0-26.7). SIT occurred in 20 (8.2%) patients, mostly with multiple infarcts in the border zone and/or cortical regions. In multivariate Cox regression, low PR (adjusted hazard ratio [HR] = 3.16, p = 0.026) and high WSSR (adjusted HR = 3.05, p = 0.014) were independently associated with SIT. Patients with both low PR and high WSSR had significantly higher risk of SIT than those with normal PR and WSSR (risk = 17.5% vs 3.0%, adjusted HR = 7.52, p = 0.004)., Interpretation: This work represents a step forward in utilizing computational flow simulation techniques in studying intracranial atherosclerotic disease. It reveals a hemodynamic pattern of sICAS that is more prone to stroke relapse, and supports hypoperfusion and artery-to-artery embolism as common mechanisms of ischemic stroke in such patients. Ann Neurol 2019;85:752-764., (© 2019 American Neurological Association.)

Predicting infarct volume from magnetic resonance perfusion-weighted imaging can provide helpful information to clinicians in deciding how aggressively to treat acute stroke patients. Models have been developed to predict tissue fate, yet these models are mostly built using hand-crafted features (e.g., time-to-maximum) derived from perfusion images, which are sensitive to deconvolution methods. We demonstrate the application of deep convolution neural networks (CNNs) on predicting final stroke infarct volume using only the source perfusion images. We propose a deep CNN architecture that improves feature learning and achieves an area under the curve of 0.871 ± 0.024 , outperforming existing tissue fate models. We further validate the proposed deep CNN with existing 2-D and 3-D deep CNNs for images/video classification, showing the importance of the proposed architecture. Our work leverages deep learning techniques in stroke tissue outcome prediction, advancing magnetic resonance imaging perfusion analysis one step closer to an operational decision support tool for stroke treatment guidance.

Background: This study investigated cerebrospinal fluid (CSF) hydrodynamics using cine phase-contrast MRI in the cerebral aqueduct and the prepontine cistern between three distinct groups: pre-shunt normal pressure hydrocephalus (NPH) patients, post-shunt NPH patients, and controls. We hypothesized that the hyperdynamic flow of CSF through the cerebral aqueduct seen in NPH patients was due to a reduction in cisternal CSF volume buffering. Both hydrodynamic (velocity, flow, stroke volume) and peak flow latency (PFL) parameters were investigated., Methods: Scans were conducted on 30 pre-treatment patients ranging in age from 58 to 88 years along with an additional 12 controls. Twelve patients also received scans following either ventriculoatrial (VA) or ventriculoperitoneal (VP) shunt treatment (9 VP, 3 VA), ranging in age from 74 to 89 years with a mean follow up time of 6 months., Results: Significant differences in area, velocity, flow, and stroke volume for the cerebral aqueduct were found between the pre-treatment NPH group and the healthy controls. Shunting caused a significant decrease in both caudal and cranial mean flow and stroke volume in the cerebral aqueduct. No significant changes were found in the prepontine cistern between the pre-treatment group and healthy controls. For the PFL, no significant differences were seen in the cerebral aqueduct between any of the three groups; however, the prepontine cistern PFL was significantly decreased in the pre-treatment NPH group when compared to the control group., Conclusions: Although several studies have quantified the changes in aqueductal flow between hydrocephalic groups and controls, few studies have investigated prepontine cistern flow. Our study was the first to investigate both regions in the same patients for NPH pre- and post- treatment. Following shunt treatment, the aqueductal CSF metrics decreased toward control values, while the prepontine cistern metrics trended up (not significantly) from the normal values established in this study. The opposing trend of the two locations suggests a redistribution of CSF pulsatility in NPH patients. Furthermore, the significantly decreased latency of the prepontine cisternal CSF flow suggests additional evidence for CSF pulsatility dysfunction.

Previous studies have reported that miR-27a-3p is down-regulated in the serum of patients with intracerebral hemorrhage (ICH), but the implication of miR-27a-3p down-regulation in post-ICH complications remains elusive. Here we verified miR-27a-3p levels in the serum of ICH patients by real-time PCR and observed that miR-27a-3p is also significantly reduced in the serum of these patients. We then further investigated the effect of miR-27a-3p on post-ICH complications by intraventricular administration of a miR-27a-3p mimic in rats with collagenase-induced ICH. We found that the hemorrhage markedly reduced miR-27a-3p levels in the hematoma, perihematomal tissue, and serum and that intracerebroventricular administration of the miR-27a-3p mimic alleviated behavioral deficits 24 h after ICH. Moreover, ICH-induced brain edema, vascular leakage, and leukocyte infiltration were also attenuated by this mimic. Of note, miR-27a-3p mimic treatment also inhibited neuronal apoptosis and microglia activation in the perihematomal zone. We further observed that the miR-27a-3p mimic suppressed the up-regulation of aquaporin-11 (AQP11) in the perihematomal area and in rat brain microvascular endothelial cells (BMECs). Moreover, miR-27a-3p down-regulation increased BMEC monolayer permeability and impaired BMEC proliferation and migration. In conclusion, miR-27a-3p down-regulation contributes to brain edema, blood-brain barrier disruption, neuron loss, and neurological deficits following ICH. We conclude that application of exogenous miR-27a-3p may protect against post-ICH complications by targeting AQP11 in the capillary endothelial cells of the brain., (© 2018 Xi et al.)

Cardiac arrest (CA) is the leading cause of death and disability in the United States. Early and accurate prediction of CA outcome can help clinicians and families to make a better-informed decision for the patient's healthcare. Studies have shown that electroencephalography (EEG) may assist in early prognosis of CA outcome. However, visual EEG interpretation is subjective, labor-intensive, and requires interpretation by a medical expert, i.e., neurophysiologists. These limiting factors may hinder the applicability of such testing as the prognostic method in clinical settings. Automatic EEG pattern recognition using quantitative measures can make the EEG analysis more objective and less time consuming. It also allows to detect and display hidden patterns that may be useful for the prognosis over longer time periods of monitoring. Given these potential benefits, there have been an increasing interest over the last few years in the development and employment of EEG quantitative measures to predict CA outcome. This paper extensively reviews the definition and efficacy of various measures that have been employed for the prediction of outcome in CA subjects undergoing hypothermia (a neuroprotection method that has become a standard of care to improve the functional recovery of CA patients after resuscitation). The review details the State-of-the-Art and provides some perspectives on what seems to be promising for the early and accurate prognostication of CA outcome using the quantitative measures of EEG.

Co-occurring substance misuse and borderline personality disorder (BPD) in adults is associated with more severe dysfunction and poorer prognosis than BPD alone. However, it is unknown to what extent substance use in youth with BPD is normative for this age group or pathological. This study compared substance use in 117 help-seeking youth (aged 15-25 years) with their first presentation for treatment of BPD, with an epidemiological general population sample and with healthy, age- and gender-matched controls. Established instruments were used to diagnose BPD and assess substance use. Alcohol dependence, daily tobacco use, and use of illicit substances in the past month were between four and nine times more prevalent in the BPD group than in the general population. Similarly, the prevalence of substance use was disproportionately higher in youth with BPD than in matched controls. The findings indicate a non-normative, alarmingly high rate of substance use among youth with BPD.

Background and Purpose: Anemia is associated with worse outcome in stroke, but the impact of anemia with intravenous thrombolysis or endovascular therapy has hardly been delineated. The aim of this study was to analyze the role of anemia on infarct evolution and outcome after acute stroke treatment., Methods: 1158 patients from Bern and 321 from Los Angeles were included. Baseline data and 3 months outcome assessed with the modified Rankin Scale were recorded prospectively. Baseline DWI lesion volumes were measured in 345 patients and both baseline and final infarct volumes in 180 patients using CT or MRI. Multivariable and linear regression analysis were used to determine predictors of outcome and infarct growth., Results: 712 patients underwent endovascular treatment and 446 intravenous thrombolysis. Lower hemoglobin at baseline, at 24h, and nadir until day 5 predicted poor outcome (OR 1.150-1.279) and higher mortality (OR 1.131-1.237) independently of treatment. Decrease of hemoglobin after hospital arrival, mainly induced by hemodilution, predicted poor outcome and had a linear association with final infarct volumes and the amount and velocity of infarct growth. Infarcts of patients with newly observed anemia were twice as large as infarcts with normal hemoglobin levels., Conclusion: Anemia at hospital admission and any hemoglobin decrease during acute stroke treatment affect outcome negatively, probably by enlarging and accelerating infarct growth. Our results indicate that hemodilution has an adverse effect on penumbral evolution. Whether hemoglobin decrease in acute stroke could be avoided and whether this would improve outcome would need to be studied prospectively., Competing Interests: The authors have declared that no competing interests exist.

Background: Dynamic susceptibility contrast (DSC) MR perfusion is a frequently-used technique for neurovascular imaging. The progress of a bolus of contrast agent through the tissue of the brain is imaged via a series of T2 * -weighted MRI scans. Clinically relevant parameters such as blood flow and Tmax can be calculated by deconvolving the contrast-time curves with the bolus shape (arterial input function). In acute stroke, for instance, these parameters may help distinguish between the likely salvageable tissue and irreversibly damaged infarct core. Deconvolution typically relies on singular value decomposition (SVD): however, studies have shown that these algorithms are very sensitive to noise and artifacts present in the image and therefore may introduce distortions that influence the estimated output parameters. Methods: In this work, we present a machine learning approach to the estimation of perfusion parameters in DSC-MRI. Various machine learning models using as input the raw MR source data were trained to reproduce the output of an FDA approved commercial implementation of the SVD deconvolution algorithm. Experiments were conducted to determine the effect of training set size, optimal patch size, and the effect of using different machine-learning models for regression. Results: Model performance increased with training set size, but after 5,000 samples (voxels) this effect was minimal. Models inferring perfusion maps from a 5 by 5 voxel patch outperformed models able to use the information in a single voxel, but larger patches led to worse performance. Random Forest models produced had the lowest root mean squared error, with neural networks performing second best: however, a phantom study revealed that the random forest was highly susceptible to noise levels, while the neural network was more robust. Conclusion: The machine learning-based approach produces estimates of the perfusion parameters invariant to the noise and artifacts that commonly occur as part of MR acquisition. As a result, better robustness to noise is obtained, when evaluated against the FDA approved software on acute stroke patients and simulated phantom data.

Objective: Hemorrhagic transformation (HT) is the most severe complication of reperfusion therapy in acute ischemic stroke (AIS) patients. Management of AIS patients could benefit from accurate prediction of upcoming HT. While prediction of HT occurrence has recently provided encouraging results, the prediction of the severity and territory of the HT could bring valuable insights that are beyond current methods., Methods: This study tackles these issues and aims to predict the spatial occurrence of HT in AIS from perfusion-weighted magnetic resonance imaging (PWI) combined with diffusion weighted imaging. In all, 165 patients were included in this study and analyzed retrospectively from a cohort of AIS patients treated with reperfusion therapy in a single stroke center., Results: Machine learning models are compared within our framework; support vector machines, linear regression, decision trees, neural networks, and kernel spectral regression were applied to the dataset. Kernel spectral regression performed best with an accuracy of $\text{83.7} \pm \text{2.6}\%$., Conclusion: The key contribution of our framework formalize HT prediction as a machine learning problem. Specifically, the model learns to extract imaging markers of HT directly from source PWI images rather than from pre-established metrics., Significance: Predictions visualized in terms of spatial likelihood of HT in various territories of the brain were evaluated against follow-up gradient recalled echo and provide novel insights for neurointerventionalists prior to endovascular therapy.

Background: The pivotal impact of collateral circulation on outcomes in endovascular therapy has fueled the development of numerous CTA collateral scales, yet synchronized validation with conventional angiography has never occurred. We validated multiphase flat-detector CTA (mpFDCTA) for collateral imaging in patients undergoing endovascular stroke treatment., Materials and Methods: Consecutive acute ischemic stroke patient data, including mpFDCTA shortly followed by digital subtraction angiography (DSA), in the setting of acute ICA- or MCA-occlusions were analyzed. An independent core lab scored mpFDCTA with an established collateral scale and separately graded American Society of Interventional and Therapeutic Neuroradiology (ASITN) collateral score on DSA, blind to all other data., Results: 24 consecutive cases (age 76.7 ± 7.3 years; 58.3% women; baseline NIHSS median 17 (4-23)) of acute ICA- or MCA-occlusion were analyzed. Time from mpFDCTA to intracranial DSA was 23.04 ± 7.6 minutes. Median mpFDCTA collateral score was 3 (0-5) and median DSA ASITN collateral score was 2 (0-3), including the full range of potential collateral grades. mpFDCTA and ASITN collateral score were strongly correlated (r = 0.86, p<0.001). mpFDCTA provided more complete collateral data compared to selective DSA injections in cases of ICA-occlusion. ROC analyses for prediction of clinical outcomes revealed an AUC of 0.76 for mpFDCTA- and 0.70 for DSA ASITN collaterals., Conclusions: mpFDCTA in the angiography suite provides a validated measure of collaterals, offering distinct advantages over conventional angiography. Direct patient transfer to the angiography suite and mpFDCTA collateral grading provides a novel and reliable triage paradigm for acute ischemic stroke., Competing Interests: I have read the journal's policy and the authors of this manuscript have the following competing interests: D.S. Liebeskind: Consultant/Advisory Board; Significant; Consultant to Medtronic (Imaging Core Lab), Consultant to Stryker Neurovascular (Imaging Core Lab). D. Behme: Speakers' Bureau; Modest; Stryker. M. Psychogios: Honoraria; Modest; Siemens Healthineers. The Department of Neuroradiology has a research agreement with Siemens Medical Solutions, Forchheim, Germany. There are no other conflicts of interests to be reported for the other authors. This does not alter our adherence to PLOS ONE policies on sharing data and materials. There are no other conflicts of interests to be reported for the other authors.

Concomitant substance misuse in adults with borderline personality disorder (BPD) is associated with a more severe course of illness and poorer outcomes. Previous research has found an association between the number of borderline personality features and substance misuse in community samples. This study examined the relationship between substance use and severity of BPD in youth presenting for the first time for treatment (first presentation) of BPD. Participants were 117 help-seeking youth aged 15-25 years (93 females; M age = 19.0 years, SD = 2.8) with BPD. Hierarchical logistic regression was used to investigate whether the severity of BPD predicted substance use. After adjusting for demographic factors and concurrent mental state pathology, BPD independently predicted alcohol dependence, amphetamine use in the previous month, or use of 2 or more illicit substances in the previous month but not daily tobacco use or cannabis use in the previous month. BPD might increase the probability that an individual will engage in higher-risk behaviors, such as amphetamine and polysubstance use, or problematic alcohol use. These findings support the need for assessment of youth with BPD for alcohol, amphetamine, and polysubstance use along with the need for routine screening in drug and alcohol services for BPD features. (PsycINFO Database Record, ((c) 2018 APA, all rights reserved).)

The microvasculature is prominently affected by traumatic brain injury (TBI), including mild TBI (concussion). Assessment of cerebral hemodynamics shows promise as biomarkers of TBI, and may help inform development of therapies aimed at promoting neurologic recovery. The objective of this study was to assess the evolution in cerebral hemodynamics observable with transcranial Doppler (TCD) ultrasound in subjects suffering from a concussion at different intervals during recovery. Pediatric subjects between the ages of 14 and 19 years clinically diagnosed with a concussion were observed at different points post-injury. Blood flow velocity in the middle cerebral artery was measured with TCD. After a baseline period, subjects participated in four breath holding challenges. Pulsatility index (PI), resistivity index (RI), the ratio of the first two pulse peaks (P2R), and the mean velocity (MV) were computed from the baseline section. The breath hold index (BHI) was computed from the challenge sections. TCD detected two phases of hemodynamic changes after concussion. Within the first 48 h, PI, RI, and P2R show a significant difference from the controls ( U  = -3.10; P  < 0.01, U  = -2.86; P  < 0.01, and U  = 2.62; P  < 0.01, respectively). In addition, PI and P2R were not correlated ( r p  = -0.36; P  = 0.23). After 48 h, differences in pulsatile features were no longer observable. However, BHI was significantly increased when grouped as 2-3, 4-5, and 6-7 days post-injury ( U  = 2.72; P  < 0.01, U  = 2.46; P  = 0.014, and U  = 2.38; P  = 0.018, respectively). To our knowledge, this is the first longitudinal study of concussions using TCD. In addition, these results are the first to suggest the multiple hemodynamic changes after a concussion are observable with TCD and could ultimately lead to a better understanding of the underlying pathophysiology. In addition, the different hemodynamic responses to a concussion as compared to severe traumatic brain injuries highlight the need for specific diagnostic and therapeutic treatments of mild head injuries in adolescents.

Background and Purpose: Recent studies have strongly associated intracranial aneurysm growth with increased risk of rupture. Identifying aneurysms that are likely to grow would be beneficial to plan more effective monitoring and intervention strategies. Our hypothesis is that for unruptured intracranial aneurysms of similar size, morphologic characteristics differ between aneurysms that continue to grow and those that do not., Materials and Methods: From aneurysms in our medical center with follow-up imaging dates in 2015, ninety-three intracranial aneurysms (23 growing, 70 stable) were selected. All CTA images for the aneurysm diagnosis and follow-up were collected, a total of 348 3D imaging studies. Aneurysm 3D geometry for each imaging study was reconstructed, and morphologic characteristics, including volume, surface area, nonsphericity index, aspect ratio, and size ratio were calculated., Results: Morphologic characteristics were found to differ between growing and stable groups. For aneurysms of <3 mm, nonsphericity index ( P < .001); 3-5 mm, nonsphericity index ( P < .001); 5-7 mm, size ratio ( P = .003); >7 mm, volume ( P < .001); surface area ( P < .001); and nonsphericity index ( P = .002) were significant. Within the anterior communicating artery, the nonsphericity index ( P = .008) and, within the posterior communicating artery, size ratio ( P = .004) were significant. The nonsphericity index receiver operating characteristic area under the curve was 0.721 for discriminating growing and stable cases on the basis of initial images., Conclusions: Among aneurysms with similar sizes, morphologic characteristics appear to differ between those that are growing and those that are stable. The nonsphericity index, in particular, was found to be higher among growing aneurysms. The size ratio was found to be the second most significant parameter associated with growth., (© 2018 by American Journal of Neuroradiology.)

The purpose of this study was to develop and evaluate a scoring system for assessing reperfusion status based on arterial spin labeled (ASL) perfusion MRI in acute ischemic stroke (AIS) patients receiving thrombolysis and/or endovascular treatment. Pseudo-continuous ASL with background suppressed 3D GRASE was acquired along with DWI in 90 patients within 24 h post-treatment. An automatic reperfusion scoring system (auto-RPS) was devised based on the Alberta Stroke Program Early CT Score (ASPECTS) template, and compared with manual RPS and DWI-ASPECTS. TICI (thrombolysis in cerebral infarction) scores were graded in 48 patients who received endovascular treatment. Favorable outcomes were defined by a modified Rankin Scale score of 0-2 at three months. Auto-RPS was positively correlated with DWI-ASPECTS (ρ = 0.6, P < 0.001) and was on average 1 point lower than DWI-ASPECTS ( P < 0.001). The area under the receiver operating characteristic curve for discriminating poor functional outcome (n = 90) was 0.75 (95% CI, 0.64-0.86) for manual RPS, 0.85 (95% CI, 0.76-0.94) for auto-RPS, and 0.81 (95% CI, 0.71-0.90) for DWI-ASPECTS. Multiple logistic regression analysis in the TICI-graded patients (n = 48) showed that auto-RPS is highly associated with functional outcome (OR = 25.2, 95% CI 4.02-496, P < 0.01). Post treatment auto-RPS within 24 h provides a useful tool to predict functional outcome in AIS patients.

Objective: To determine normal ranges for traditional transcranial Doppler (TCD) measurements for two age groups (14-19 and 20-29 years) and compare to existing literature results. The development of a normal range for TCD measurements will be required for the development of diagnostic and prognostic tests in the future., Materials and Methods: We performed TCD on the middle cerebral artery on 147 healthy subjects aged 18.9 years (SD = 2.1) and calculated mean cerebral blood flow velocity (mCBFV) and pulsatility index (PI). The study population was divided into two age populations (14-19 and 20-29 years)., Results: There was a significant decrease in PI (p = 0.015) for the older age group with no difference in mCBFV., Conclusion: Age-related, normal data are a prerequisite for TCD to continue to gain clinical acceptance. Our correlation of age-related TCD findings with previously published results as the generally accepted "gold standard" underlines the validity and sensitivity of this ultrasound method.

Purpose: The accuracy of dose prediction is essential for knowledge-based planning and automated planning techniques. We compare the dose prediction accuracy of 3 prediction methods including statistical voxel dose learning, spectral regression, and support vector regression based on limited patient training data., Methods: Statistical voxel dose learning, spectral regression, and support vector regression were used to predict the dose of noncoplanar intensity-modulated radiation therapy (4π) and volumetric-modulated arc therapy head and neck, 4π lung, and volumetric-modulated arc therapy prostate plans. Twenty cases of each site were used for k-fold cross-validation, with k = 4. Statistical voxel dose learning bins voxels according to their Euclidean distance to the planning target volume and uses the median to predict the dose of new voxels. Distance to the planning target volume, polynomial combinations of the distance components, planning target volume, and organ at risk volume were used as features for spectral regression and support vector regression. A total of 28 features were included. Principal component analysis was performed on the input features to test the effect of dimension reduction. For the coplanar volumetric-modulated arc therapy plans, separate models were trained for voxels within the same axial slice as planning target volume voxels and voxels outside the primary beam. The effect of training separate models for each organ at risk compared to all voxels collectively was also tested. The mean squared error was calculated to evaluate the voxel dose prediction accuracy., Results: Statistical voxel dose learning using separate models for each organ at risk had the lowest root mean squared error for all sites and modalities: 3.91 Gy (head and neck 4π), 3.21 Gy (head and neck volumetric-modulated arc therapy), 2.49 Gy (lung 4π), and 2.35 Gy (prostate volumetric-modulated arc therapy). Compared to using the original features, principal component analysis reduced the 4π prediction error for head and neck spectral regression (-43.9%) and support vector regression (-42.8%) and lung support vector regression (-24.4%) predictions. Principal component analysis was more effective in using all/most of the possible principal components. Separate organ at risk models were more accurate than training on all organ at risk voxels in all cases., Conclusion: Compared with more sophisticated parametric machine learning methods with dimension reduction, statistical voxel dose learning is more robust to patient variability and provides the most accurate dose prediction method.

Co-occurring substance misuse and borderline personality disorder (BPD) in adults is associated with more severe dysfunction and poorer prognosis than BPD alone. However, it is unknown to what extent substance use in youth with BPD is normative for this age group or pathological. This study compared substance use in 117 help-seeking youth (aged 15-25 years) with their first presentation for treatment of BPD, with an epidemiological general population sample and with healthy age- and gender-matched controls. Established instruments were used to diagnose BPD and assess substance use. Alcohol dependence, daily tobacco use, and use of illicit substances in the past month were between four and nine times more prevalent in the BPD group than in the general population. Similarly, the prevalence of substance use was disproportionately higher in youth with BPD than in matched controls. The findings indicate a non-normative, alarmingly high rate of substance use among youth with BPD.

Background and Purpose: Multi-delay arterial spin-labeling (ASL) perfusion imaging has been used as a promising modality to evaluate cerebral perfusion. Our aim was to assess the association of leptomeningeal collateral perfusion scores based on ASL parameters with outcome of endovascular treatment in patients with acute ischemic stroke (AIS) in the middle cerebral artery (MCA) territory., Materials and Methods: ASL data at 4 post-labeling delay (PLD) times (PLD = 1.5, 2, 2.5, 3 s) were acquired during routine clinical magnetic resonance examination on AIS patients prior to endovascular treatment. A 3-point scale of leptomeningeal collateral perfusion grade on 10 anatomic regions was determined based on arterial transit times (ATT), cerebral blood flow (CBF), and arterial cerebral blood volume (CBV), estimated by the multi-delay ASL protocol. Based on a 90-day modified Rankin Scale (mRS), the patients were dichotomized to moderate/good (mRS 0-3) and poor outcome (mRS 4-6) and the regional collateral flow scores were compared., Results: Fifty-five AIS patients with unilateral MCA stroke (mean 73.95±14.82 years) including 23 males were enrolled. Compared with poor outcome patients, patients with moderate to good outcomes had a significantly higher leptomeningeal collateral perfusion scores on CBV (3.01±2.11 vs. 1.82±1.51, p=0.024) but no differences on scores on CBF (2.31±1.61 vs. 1.66±1.32, p=0.231) and ATT (2.67±2.33 vs. 3.42±3.37, p=0.593)., Conclusions: Higher leptomeningeal collateral perfusion scores on CBV images by ASL may be a specific marker of clinical outcome after endovascular treatment in patients with acute MCA ischemic stroke. Further study with larger sample size is warranted.

2D to 3D image registration techniques are useful in the treatment of neurological diseases such as stroke. Image registration can aid physicians and neurosurgeons in the visualization of the brain for treatment planning, provide 3D information during treatment, and enable serial comparisons. In the context of stroke, image registration is challenged by the occluded vessels and deformed anatomy due to the ischemic process. In this paper, we present an algorithm to register 2D digital subtraction angiography (DSA) with 3D magnetic resonance angiography (MRA) based upon local point cloud descriptors. The similarity between these local descriptors is learned using a machine learning algorithm, allowing flexibility in the matching process. In our experiments, the error rate of 2D/3D registration using our machine learning similarity metric (52.29) shows significant improvement when compared to a Euclidean metric (152.54). The proposed similarity metric is versatile and could be applied to a wide range of 2D/3D registration.

Intracranial Hypertension, a disorder characterized by elevated pressure in the brain, is typically monitored in neurointensive care and diagnosed only after elevation has occurred. This reaction-based method of treatment leaves patients at higher risk of additional complications in case of misdetection. The detection of intracranial hypertension has been the subject of many recent studies in an attempt to accurately characterize the causes of hypertension, specifically examining waveform morphology. We investigate the use of Deep Learning, a hierarchical form of machine learning, to model the relationship between hypertension and waveform morphology, giving us the ability to accurately detect presence hypertension. Data from 60 patients, showing intracranial pressure levels over a half hour time span, was used to evaluate the model. We divided each patient's recording into average normalized beats over 30 sec segments, assigning each beat a label of high (i.e. greater than 15 mmHg) or low intracranial pressure. The model was tested to predict the presence of elevated intracranial pressure. The algorithm was found to be 92.05± 2.25% accurate in detecting intracranial hypertension on our dataset.

Background and Purpose: In acute arterial occlusion, fluid-attenuated inversion recovery vascular hyperintensity (FVH) has been linked to slow flow in leptomeningeal collaterals and cerebral hypoperfusion, but the impact on clinical outcome is still controversial. In this study, we aimed to investigate the association between FVH topography or FVH-Alberta Stroke Program Early CT Score (ASPECTS) pattern and outcome in acute M1-middle cerebral artery occlusion patients with endovascular treatment., Methods: We included acute M1-middle cerebral artery occlusion patients treated with endovascular therapy (ET). All patients had diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery before ET. Distal FVH-ASPECTS was evaluated according to distal middle cerebral artery-ASPECT area (M1-M6) and acute DWI lesion was also reviewed. The presence of FVH inside and outside DWI-positive lesions was separately analyzed. Clinical outcome after ET was analyzed with respect to different distal FVH-ASPECTS topography., Results: Among 101 patients who met inclusion criteria for the study, mean age was 66.2±17.8 years and median National Institutes of Health Stroke Scale was 17.0 (interquartile range, 12.0-21.0). FVH-ASPECTS measured outside of the DWI lesion was significantly higher in patients with good outcome (modified Rankin Scale [mRS] score of 0-2; 8.0 versus 4.0, P<0.001). Logistic regression demonstrated that FVH-ASPECTS outside of the DWI lesion was independently associated with clinical outcome of these patients (odds ratio, 1.3; 95% confidence interval, 1.06-1.68; P=0.013). FVH-ASPECTS inside the DWI lesion was associated with hemorrhagic transformation (odds ratio, 1.3; 95% confidence interval, 1.04-1.51; P=0.019)., Conclusions: Higher FVH-ASPECTS measured outside the DWI lesion is associated with good clinical outcomes in patients undergoing ET. FVH-ASPECTS measured inside the DWI lesion was predictive of hemorrhagic transformation. The FVH pattern, not number, can serve as an imaging selection marker for ET in acute middle cerebral artery occlusion., (© 2016 American Heart Association, Inc.)

Deficits of self-control are associated with a number of mental state disorders. The ability to direct attention away from an alluring stimulus appears to aid inhibition of an impulsive response. However, further functional imaging research is required to assess the impact of shifts in attention on self-regulating processes. We varied the level of attentional disengagement in an functional magnetic resonance imaging (fMRI)-based Go/No-go task to probe whether diversion of attention away from alluring stimuli facilitates response inhibition. We used the attention-grabbing characteristic of faces to exogenously direct attention away from stimuli and investigated the relative importance of attention and response inhibition mechanisms under different delayed reward scenarios [i.e., where forgoing an immediate reward ($1) led to a higher ($10) or no payoff in the future]. We found that diverting attention improved response inhibition performance, but only when resistance to an alluring stimulus led to delayed reward. Region of interest analyses indicated significant increased activity in posterior right inferior frontal gyrus during successful No-go trials for delayed reward trials compared to no delayed reward trials, and significant reduction in activity in the superior temporal gyri and left caudate in contexts of high attentional diversion. Our findings imply that strategies that increase the perceived benefits of response inhibition might assist individuals in abstaining from problematic impulsive behaviors.

We present the Iterative/Causal Subspace Tracking framework (I/CST) for reducing noise in continuously monitored quasi-periodic biosignals. Signal reconstruction of the basic segments of the noisy signal (e.g. beats) is achieved by projection to a reduced space on which probabilistic tracking is performed. The attractiveness of the presented method lies in the fact that the subspace, or manifold, is learned by incorporating temporal, morphological, and signal elevation constraints, so that segment samples with similar shapes, and that are close in time and elevation, are also close in the subspace representation. Evaluation of the algorithm's effectiveness on the intracranial pressure (ICP) signal serves as a practical illustration of how it can operate in clinical conditions on routinely acquired biosignals. The reconstruction accuracy of the system is evaluated on an idealized 20-min ICP recording established from the average ICP of patients monitored for various ICP related conditions. The reconstruction accuracy of the ground truth signal is tested in presence of varying levels of additive white Gaussian noise (AWGN) and Poisson noise processes, and measures significant increases of 758% and 396% in the average signal-to-noise ratio (SNR).

Neuroimaging of cerebrovascular status and hemodynamics has vastly improved our understanding of stroke mechanisms and provided information for therapeutic decision-making. CT techniques are the most commonly used techniques due to wide availability, rapid acquisition and acceptable tolerance. Numerous multimodal CT techniques have been developed in the last few years. We summarize and explain the various multimodal CT acquisition techniques within three categories based on the scanning mode, namely static mode (single-phase CTA), multiple static mode (multi-phase CTA) and continuous mode (CT perfusion and dynamic CTA). Post-processing methods based on different acquisition modes are also introduced in an easy manner by focusing on the information extracted and products generated. We also describe the applications for these techniques along with their advantages and disadvantages.