Mathematical Framework of Deconvolution Algorithms for Quantification of Perfusion Parameters.

PurposeMR perfusion weighted imaging (PWI) has been used as sensitive indicator of tissue at risk for infarction. Quantitative perfusion parameters such as cerebral blood flow (CBF), mean transit time (MTT) and cerebral blood volume (CBV) can be obtained from post processing of PWI data using standard singular value decomposition algorithm (SVD). Assumption regarding absence of arterial - tissue delay (ATD) used in SVD algorithm results in underestimation of perfusion parameters. To estimate accurate values for perfusion parameters it is important to understand the mathematical framework behind SVD and improved SVD algorithms (bSVD and rSVD).MethodThis study explains the mathematical framework of SVD and improved SVD algorithms and uses computational techniques that use bSVD algorithm to obtain perfusion parameters maps of CBF, CBV and MTT for acute stroke patient.ResultComputational techniques based on mathematical deconvolution algorithms are used to post process CBV, CBF and MTT maps where decrease in CBF and CBV were seen in left hemisphere.ConclusionThe bSVD algorithm is found to be sensitive to ATD and provides more accurate estimates of perfusion parameters than the SVD algorithm, however CBF estimates from bSVD and rSVD still remain influenced by other artifacts Keywords: PWI = perfusion weighted imaging, CBF= cerebral blood flow, MTT = mean transit time, CBV= cerebral blood volume, SVD = singular value decomposition algorithm

The optimal pulse pressures for healthy adults with different ages and sexes correlate with cardiovascular health metrics

BackgroundPulse pressure (PP) may play a role in the development of cardiovascular disease, and the optimal PP for different ages and sexes is unknown. In a prospective cohort, we studied subjects with favorable cardiovascular health (CVH), proposed the mean PP as the optimal PP values, and demonstrated its relationship with healthy lifestyles.Methods and resultsBetween 1996 and 2016, a total of 162,636 participants (aged 20 years or above; mean age 34.9 years; 26.4% male subjects; meeting criteria for favorable health) were recruited for a medical examination program. PP in male subjects was 45.6 ± 9.4 mmHg and increased after the age of 50 years. PP in female subjects was 41.8 ± 9.5 mmHg and increased after the age of 40 years, exceeding that of male subjects after the age of 50 years. Except for female subjects with a PP of 40–70 mmHg, PP increase correlates with both systolic blood pressure (BP) increase and diastolic BP decrease. Individuals with mean PP values are more likely to meet health metrics, including body mass index (BMI) <25 kg/m2 (chi-squared = 9.35, p<0.01 in male subjects; chi-squared = 208.79, p < 0.001 in female subjects) and BP <120/80 mmHg (chi-squared =1,300, p < 0.001 in male subjects; chi-squared =11,000, p < 0.001 in female subjects). We propose a health score (Hscore) based on the sum of five metrics (BP, BMI, being physically active, non-smoking, and healthy diet), which significantly correlates with the optimal PP.ConclusionThe mean PP (within ±1 standard deviation) could be proposed as the optimal PP in the adult population with favorable CVH. The relationship between health metrics and the optimal PP based on age and sex was further demonstrated to validate the Hscore

Association between plasma levels of hyaluronic acid and functional outcome in acute stroke patients

BACKGROUND: Activation of hyaluronic acid (HA) and associated enzyme synthesis has been demonstrated in experimental stroke animal models. Our study aimed to investigate the plasma levels of HA in acute stroke patients and the associations between HA levels and functional outcome. METHODS: This was a multicenter case–control study. Acute stroke patients and age- and sex-matched non-stroke controls were recruited. Plasma levels of HA in acute stroke patients were determined at <48 hours and at 48 to 72 hours after stroke onset by standard ELISA. Favorable functional outcome was defined as modified Rankin scale ≤2 at 3 months after stroke. RESULTS: The study included 206 acute stroke patients, including 43 who had intracerebral hemorrhage and 163 who had ischemic stroke, and 159 controls. The plasma levels of HA in the acute stroke patients were significantly higher than those in the controls (219.7 ± 203.4 ng/ml for <48 hours and 343.1 ± 710.3 ng/ml for 48 to 72 hours versus 170.4 ± 127.9 ng/ml in the controls; both P < 0.05). For intracerebral hemorrhage patients, HA ≤500 ng/ml (<48 hours) was an independent favorable outcome predictor (P = 0.016). For ischemic stroke patients, an inverted U-shaped association between plasma HA (48 to 72 hours) and outcome was noted, indicating that ischemic stroke patients with too high or too low plasma HA levels tended to have an unfavorable outcome. CONCLUSION: HA plasma level was elevated in patients with acute stroke, and can predict 3-month functional outcome, particularly for patients with intracerebral hemorrhage

Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

Objectives To investigate whether utilizing a convolutional neural network (CNN)-based arterial input function (AIF) improves the volumetric estimation of core and penumbra in association with clinical measures in stroke patients. Methods The study included 160 acute ischemic stroke patients (male = 87, female = 73, median age = 73 years) with approval from the institutional review board. The patients had undergone CTP imaging, NIHSS and ASPECTS grading. convolutional neural network (CNN) model was trained to fit a raw AIF curve to a gamma variate function. CNN AIF was utilized to estimate the core and penumbra volumes which were further validated with clinical scores. Results Penumbra estimated by CNN AIF correlated positively with the NIHSS score (r = 0.69; p  20) and lower ASPECT score ( 10 s, Tmax > 10 s volumes were statistically significantly higher (p < .05). Conclusions With inclusion of the CNN AIF in perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke. Critical relevance statement With CNN AIF perfusion imaging pipeline, penumbra and core estimations are more reliable as they correlate with scores representing neurological deficits in stroke

Effect of the allelic variants of aldehyde dehydrogenase <it>ALDH2*2 </it>and alcohol dehydrogenase <it>ADH1B*2 </it>on blood acetaldehyde concentrations

Abstract Alcoholism is a complex behavioural disorder. Molecular genetics studies have identified numerous candidate genes associated with alcoholism. It is crucial to verify the disease susceptibility genes by correlating the pinpointed allelic variations to the causal phenotypes. Alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH) are the principal enzymes responsible for ethanol metabolism in humans. Both ADH and ALDH exhibit functional polymorphisms among racial populations; these polymorphisms have been shown to be the important genetic determinants in ethanol metabolism and alcoholism. Here, we briefly review recent advances in genomic studies of human ADH/ALDH families and alcoholism, with an emphasis on the pharmacogenetic consequences of venous blood acetaldehyde in the different ALDH2 genotypes following the intake of various doses of ethanol. This paper illustrates a paradigmatic example of phenotypic verifications in a protective disease gene for substance abuse.</p

Case Report - Status epilepticus associated with initiation of theophylline in an elderly patient with diabetic ketoacidosis

An 80-year-old man with a history of Type 2 diabetes mellitus was hospitalized due to generalized convulsive status epilepticus. Initially, hyperglycemia and ketoacidosis were diagnosed, but his seizures were refractory to the medical treatment. Additionally, a high level of serum theophylline (29.1 mg/mL) was detected. Following detoxification of theophylline by oral activated charcoal, the patient regained consciousness and was free from seizures without antiepileptic drug treatment. Brain magnetic resonance imaging revealed subacute subdural hematomas at the bilateral occipital hemispheres. This case suggests that theophylline toxicity may be a predisposing factor for seizures in patients with a history of traumatic brain injury in spite of the presence of diabetic ketoacidosis that may have an anticonvulsant action

Arterial input function segmentation based on a contour geodesic model for tissue at risk identification in ischemic stroke

Purpose: Perfusion parameters such as cerebral blood flow (CBF) and Tmax have been proven to be useful in the diagnosis and prognosis for ischemic stroke. Arterial input function (AIF) is required as an input to estimate perfusion parameters. This makes the AIF selection paradigm of clinical importance. Methods: This study proposes a new technique to address the problem of AIF selection, based on a variational segmentation model that combines geometric constraint in a distance function. The modified model uses discrete total variation in the distance term and via minimizing energy locates the arterial regions. Matrix analysis is utilized to identify the AIF with maximum peak height within the segmented region. Results: Group mean differences indicate that overall the AIF selected by the purposed method has better arterial features of higher peak position (16.7 and 26.1 a.u.) and fast attenuation (1.08 s and 0.9 s) as compared to the other state-of-the-art methods. Utilizing the selected AIF, mean CBF, and Tmax values were estimated higher than the traditional methods. Ischemic regions were precisely located through the perfusion maps. Conclusions: This AIF segmentation framework worked on perfusion images at levels superior to the current clinical state of the art. Consequently, the perfusion parameters derived from AIF selected by the purposed method were more accurate and reliable. The proposed method could potentially be considered as part of the calculation for perfusion imaging in general

Generalized chorea due to delayed encephalopathy after acute carbon monoxide intoxication

Movement disorder due to delayed encephalopathy after carbon monoxide (CO) intoxication is uncommon. Generalized chorea, presenting as an initial symptom of delayed encephalopathy, is extremely rare. We describe a 60-year-old woman, who had completely recovered from acute CO poisoning, developed mental and behavioral changes, urinary incontinence and generalized chorea 2 weeks thereafter. T2-weighted brain magnetic resonance imaging showed extensive hyperintensity of the bilateral periventricular and subcortical white matter and the globus pallidus. Brain single-photon emission computed tomography (SPECT) with technetium-99 ethylene cysteine dimer showed inhomogeneous perfusion in the cerebral cortex, with decreased uptake in bilateral frontal regions. Delayed encephalopathy after acute CO intoxication was diagnosed, and the symptoms gradually improved after hyperbaric oxygen therapy (HBOT). This case report demonstrates that generalized chorea may be one of the initial presenting symptoms of delayed encephalopathy after acute CO intoxication. We hypothesize that the generalized chorea in our patient may have been caused by the subcortical white matter lesions, which most likely interrupted the basal ganglia-thalamocortical circuits and that HBOT may be the treatment of choice for such patients

Development of an intelligent decision support system for ischemic stroke risk assessment in a population-based electronic health record database.

BackgroundIntelligent decision support systems (IDSS) have been applied to tasks of disease management. Deep neural networks (DNNs) are artificial intelligent techniques to achieve high modeling power. The application of DNNs to large-scale data for estimating stroke risk needs to be assessed and validated. This study aims to apply a DNN for deriving a stroke predictive model using a big electronic health record database.Methods and resultsThe Taiwan National Health Insurance Research Database was used to conduct a retrospective population-based study. The database was divided into one development dataset for model training (~70% of total patients for training and ~10% for parameter tuning) and two testing datasets (each ~10%). A total of 11,192,916 claim records from 840,487 patients were used. The primary outcome was defined as any ischemic stroke in inpatient records within 3 years after study enrollment. The DNN was evaluated using the area under the receiver operating characteristic curve (AUC or c-statistic). The development dataset included 672,214 patients (a total of 8,952,000 records) of whom 2,060 patients had stroke events. The mean age of the population was 35.5±20.2 years, with 48.5% men. The model achieved AUC values of 0.920 (95% confidence interval [CI], 0.908-0.932) in testing dataset 1 and 0.925 (95% CI, 0.914-0.937) in testing dataset 2. Under a high sensitivity operating point, the sensitivity and specificity were 92.5% and 79.8% for testing dataset 1; 91.8% and 79.9% for testing dataset 2. Under a high specificity operating point, the sensitivity and specificity were 80.3% and 87.5% for testing dataset 1; 83.7% and 87.5% for testing dataset 2. The DNN model maintained high predictability 5 years after being developed. The model achieved similar performance to other clinical risk assessment scores.ConclusionsUsing a DNN algorithm on this large electronic health record database is capable of obtaining a high performing model for assessment of ischemic stroke risk. Further research is needed to determine whether such a DNN-based IDSS could lead to an improvement in clinical practice

Optimal Scaling Approaches for Perfusion MRI with Distorted Arterial Input Function (AIF) in Patients with Ischemic Stroke

Background: Diagnosis and timely treatment of ischemic stroke depends on the fast and accurate quantification of perfusion parameters. Arterial input function (AIF) describes contrast agent concentration over time as it enters the brain through the brain feeding artery. AIF is the central quantity required to estimate perfusion parameters. Inaccurate and distorted AIF, due to partial volume effects (PVE), would lead to inaccurate quantification of perfusion parameters. Methods: Fifteen patients suffering from stroke underwent perfusion MRI imaging at the Tri-Service General Hospital, Taipei. Various degrees of the PVE were induced on the AIF and subsequently corrected using rescaling methods. Results: Rescaled AIFs match the exact reference AIF curve either at peak height or at tail. Inaccurate estimation of CBF values estimated from non-rescaled AIFs increase with increasing PVE. Rescaling of the AIF using all three approaches resulted in reduced deviation of CBF values from the reference CBF values. In most cases, CBF map generated by rescaled AIF approaches show increased CBF and Tmax values on the slices in the left and right hemispheres. Conclusion: Rescaling AIF by VOF approach seems to be a robust and adaptable approach for correction of the PVE-affected multivoxel AIF. Utilizing an AIF scaling approach leads to more reasonable absolute perfusion parameter values, represented by the increased mean CBF/Tmax values and CBF/Tmax images