Automated Prediction of Ischemic Brain Tissue Fate from Multiphase Computed Tomographic Angiography in Patients with Acute Ischemic Stroke Using Machine Learning

Background and Purpose Multiphase computed tomographic angiography (mCTA) provides time variant images of pial vasculature supplying brain in patients with acute ischemic stroke (AIS). To develop a machine learning (ML) technique to predict tissue perfusion and infarction from mCTA source images.Methods 284 patients with AIS were included from the Precise and Rapid assessment of collaterals using multi-phase CTA in the triage of patients with acute ischemic stroke for Intra-artery Therapy (Prove-IT) study. All patients had non-contrast computed tomography, mCTA, and computed tomographic perfusion (CTP) at baseline and follow-up magnetic resonance imaging/non-contrast-enhanced computed tomography. Of the 284 patient images, 140 patient images were randomly selected to train and validate three ML models to predict a pre-defined Tmax thresholded perfusion abnormality, core and penumbra on CTP. The remaining 144 patient images were used to test the ML models. The predicted perfusion, core and penumbra lesions from ML models were compared to CTP perfusion lesion and to follow-up infarct using Bland-Altman plots, concordance correlation coefficient (CCC), intra-class correlation coefficient (ICC), and Dice similarity coefficient.Results Mean difference between the mCTA predicted perfusion volume and CTP perfusion volume was 4.6 mL (limit of agreement [LoA], –53 to 62.1 mL; <i>P</i>=0.56; CCC 0.63 [95% confidence interval [CI], 0.53 to 0.71; <i>P</i><0.01], ICC 0.68 [95% CI, 0.58 to 0.78; <i>P</i><0.001]). Mean difference between the mCTA predicted infarct and follow-up infarct in the 100 patients with acute reperfusion (modified thrombolysis in cerebral infarction [mTICI] 2b/2c/3) was 21.7 mL, while it was 3.4 mL in the 44 patients not achieving reperfusion (mTICI 0/1). Amongst reperfused subjects, CCC was 0.4 (95% CI, 0.15 to 0.55; <i>P</i><0.01) and ICC was 0.42 (95% CI, 0.18 to 0.50; <i>P</i><0.01); in non-reperfused subjects CCC was 0.52 (95% CI, 0.20 to 0.60; <i>P</i><0.001) and ICC was 0.60 (95% CI, 0.37 to 0.76; <i>P</i><0.001). No difference was observed between the mCTA and CTP predicted infarct volume in the test cohort (<i>P</i>=0.67).Conclusions A ML based mCTA model is able to predict brain tissue perfusion abnormality and follow-up infarction, comparable to CTP.

Effects of aging on coronary flow reserve in patients with no evidence of myocardial perfusion abnormality

Background Coronary flow reserve (CFR) reflects the functional capacity of microcirculation to adapt to blood demand during increased cardiac work. Purpose We tested the hypothesis that aging had impacts on coronary flow velocities and CFR in patients with no evidence of myocardial perfusion abnormality on single photon emission computed tomography (SPECT). Methods and results Seventy-six patients with no evidence of myocardial perfusion abnormality on SPECT undergoing transthoracic Doppler echocardiography were enrolled in this study. Patients were divided into three age groups: 17 patients aged &lt;70 years (Group I), 38 patients aged 70–79 years (Group II), and 21 patients aged ≥80 years (Group III). Compared with Group I, CFR was significantly lower in Group II (P&lt;0.01) and Group III (P&lt;0.01). Multivariate analysis showed that female (P=0.03), cigarette smoking (P=0.004), hemoglobin level (P=0.001) and LV mass index (P=0.03) were determinants for resting coronary flow velocity. On the other hand, age (P=0.008), hemoglobin level (P&lt;0.001) and LV mass index (P=0.04) were determinants for hyperemic coronary flow velocity. Age was only independent determinant for CFR (β=−0.48 P&lt;0.001). Conclusions Our data suggested that aging impaired CFR in patients with no evidence of myocardial perfusion abnormality primarily due to the decrease in hyperemic coronary flow velocity. Comparison of coronary flow reserve amon Funding Acknowledgement Type of funding source: None

COVID-19: What Iodine Maps From Perfusion CT can reveal—A Prospective Cohort Study

Background Subtraction CT angiography (sCTA) is a technique used to evaluate pulmonary perfusion based on iodine distribution maps. The aim of this study is to assess lung perfusion changes with sCTA seen in patients with COVID-19 pneumonia and correlate them with clinical outcomes. Material and methods A prospective cohort study was carried out with 45 RT-PCR-confirmed COVID-19 patients that required hospitalization at three different hospitals, between April and May 2020. In all cases, a basic clinical and demographic profile was obtained. Lung perfusion was assessed using sCTA. Evaluated imaging features included: Pattern predominance of injured lung parenchyma in both lungs (ground-glass opacities, consolidation and mixed pattern) and anatomical extension; predominant type of perfusion abnormality (increased perfusion or hypoperfusion), perfusion abnormality distribution (focal or diffuse), extension of perfusion abnormalities (mild, moderate and severe involvement); presence of vascular dilatation and vascular tortuosity. All participants were followed-up until hospital discharge searching for the development of any of the study endpoints. These endpoints included intensive-care unit (ICU) admission, initiation of invasive mechanical ventilation (IMV) and death. Results Forty-one patients (55.2 ± 16.5 years, 22 men) with RT-PCR-confirmed SARS-CoV-2 infection and an interpretable iodine map were included. Patients with perfusion anomalies on sCTA in morphologically normal lung parenchyma showed lower Pa/Fi values (294 ± 111.3 vs. 397 ± 37.7, p = 0.035), and higher D-dimer levels (1156 ± 1018 vs. 378 ± 60.2, p < 0.01). The main common patterns seen in lung CT scans were ground-glass opacities, mixed pattern with predominant ground-glass opacities and mixed pattern with predominant consolidation in 56.1%, 24.4% and 19.5% respectively. Perfusion abnormalities were common (36 patients, 87.8%), mainly hypoperfusion in areas of apparently healthy lung. Patients with severe hypoperfusion in areas of apparently healthy lung parenchyma had an increased probability of being admitted to ICU and to initiate IMV (HR of 11.9 (95% CI 1.55–91.9) and HR 7.8 (95% CI 1.05–61.1), respectively). Conclusion Perfusion abnormalities evidenced in iodine maps obtained by sCTA are associated with increased admission to ICU and initiation of IMV in COVID-19 patients.

COVID-19: What Can Iodine Maps From Perfusion CT Teach Us? A Prospective Cohort Study.

Background: Subtraction CT angiography (sCTA) is a technique used to evaluate pulmonary perfusion based on iodine distribution maps. The aim of this study is to assess lung perfusion changes with sCTA seen in patients with COVID-19 pneumonia and correlate them with clinical outcomes.Material and Methods: A prospective cohort study was carried out with 45 RT-PCR-confirmed COVID-19 patients that required hospitalization at three different hospitals, between April and May 2020. In all cases, a basic clinical and demographic profile was obtained. Lung perfusion was assessed using sCTA. Evaluated imaging features included: Pattern predominance of injured lung parenchyma in both lungs (ground-glass opacities, consolidation and mixed pattern) and anatomical extension; predominant type of perfusion abnormality (increased perfusion or hypoperfusion), perfusion abnormality distribution (focal or diffuse), extension of perfusion abnormalities (mild, moderate and severe involvement); presence of vascular dilatation and vascular tortuosity. All participants were followed-up until hospital discharge searching for the development of any of the study endpoints. These endpoints included intensive-care unit (ICU) admission, initiation of invasive mechanical ventilation (IMV) and death.Results: Forty-one patients (55.2 +/- 16.5 years, 22 men) with RT-PCR-confirmed SARS-CoV-2 infection and an interpretable iodine map were included. Patients with perfusion anomalies on sCTA in morphologically normal lung parenchyma showed lower Pa/Fi values (294 ± 111.3 vs. 397 ± 37.7, p=0.035), and higher D-dimer levels (1156 ± 1018 vs. 378 ± 60.2, p<0.01). The main common patterns seen in lung CT scans were ground-glass opacities, mixed pattern with predominant ground-glass opacities and mixed pattern with predominant consolidation in 56.1%, 24.4% and 19.5% respectively. Perfusion abnormalities were common (36 patients, 87.8%), mainly hypoperfusion in areas of apparently healthy lung. Patients with severe hypoperfusion in areas of apparently healthy lung parenchyma had an increased probability of being admitted to ICU and to initiate IMV (HR of 11.9 (95%CI 1.55-91.9) and HR 7.8 (95%CI 1.05-61.1), respectively). Conclusion: Perfusion abnormalities evidenced in iodine maps obtained by sCTA are associated with increased admission to ICU and initiation of IMV in COVID-19 patients.

Patterns of Regional Cerebral Blood Flow as a Function of Obesity in Adults

Background: While obesity has been shown to be a risk factor for Alzheimer’s disease, the potential mechanisms underlying this risk may be clarified with better understanding of underlying physiology in obese persons. Objective: To identify patterns of cerebral perfusion abnormality in adults as a function of body mass index (BMI) defined weight categories, including overweight or obese status. Methods: A large psychiatric cohort of 35,442 brain scans across 17,721 adults (mean age 40.8±16.2 years, range 18–94 years) were imaged with SPECT during baseline and concentration scans, the latter done after each participant completed the Connors Continuous Performance Test II. ANOVA was done to identify patterns of perfusion abnormality in this cohort across BMI designations of underweight (BMI < 18.5), normal weight (BMI = 18.5 to 24.9), overweight (BMI 24.9 to 29.9), obesity (BMI≥30), and morbid obesity (BMI≥40). This analysis was done for 128 brain regions quantifying SPECT perfusion using the automated anatomical labeling (AAL) atlas. Results: Across adulthood, higher BMI correlated with decreased perfusion on both resting and concentration brain SPECT scans. These are seen in virtually all brain regions, including those influenced by AD pathology such as the hippocampus. Conclusion: Greater BMI is associated with cerebral perfusion decreases in both resting and concentration SPECT scans across adulthood.

COVID-19: What Can Iodine Maps From Perfusion CT Teach US? A Prospective Cohort Study.

Background: Subtraction CT angiography (sCTA) is a technique used to evaluate pulmonary perfusion based on iodine distribution maps. The aim of this study is to assess lung perfusion changes with sCTA seen in patients with COVID-19 pneumonia, and correlate them with clinical outcomes.Material and Methods: A prospective cohort study was carried out with 45 RT-PCR-confirmed COVID-19 patients that required hospitalization at three different hospitals, between April and May 2020. In all cases, a basic clinical and demographic profile was obtained. Lung perfusion was assessed using sCTA. Evaluated imaging features included: Predominant type of perfusion abnormality (increased perfusion or hypoperfusion), perfusion abnormality distribution (focal or diffuse), extension of perfusion abnormalities (mild, moderate and severe involvement), presence of vascular dilatation and vascular tortuosity. All participants were followed-up until hospital discharge searching for the development of any of the study endpoints. These endpoints included intensive-care unit (ICU) admission, initiation of invasive mechanical ventilation (IMV) and death.Results: Forty-one patients (55.2 +/- 16.5 years, 22 men ) with RT-PCR-confirmed SARS-CoV-2 infection and an interpretable iodine map were included. Patients with perfusion anomalies on sCTA in morphologically normal lung parenchyma showed lower Pa/Fi values (294 ± 111.3 vs. 397 ± 37.7, p=0.035), and higher D-dimer levels (1156 ± 1018 vs. 378 ± 60.2, p<0.01). The patterns seen on lung CT were ground-glass opacities, mixed pattern and alveolar consolidation in 51.2%, 41.6% and 7.3%, respectively. Perfusion abnormalities were common (36 patients, 87.8%), mainly hypoperfusion in areas of apparently healthy lung . Patients with severe hypoperfusion in areas of apparently healthy lung parenchyma had an increased probability of being admitted to ICU and to initiate IMV (HR of 11.9% and HR 7.8%, respectively).Conclusion: Perfusion abnormalities evidenced in iodine maps obtained by sCTA are associated with increased admission to ICU and initiation of IMV in COVID-19 patients.

Increased symmetric dimethylarginine, but not asymmetric dimethylarginine, concentrations are associated with transient myocardial ischemia and predict outcome

Objective Asymmetric and symmetric dimethylarginines (ADMA and SDMA) are endothelial dysfunction markers. ADMA inhibits synthesis of nitric oxide. We aimed to analyze both markers in patients with coronary artery disease (CAD) who were referred for stress/rest myocardial perfusion scintigraphy (MPS). Methods All patients underwent a 2-day dipyridamole (DP) stress/rest protocol. Thereafter, patients with transient myocardial perfusion abnormality were followed up and their coronary blood flow was quantitatively assessed. Venous blood was taken before and after DP stress to measure markers. Results Baseline ADMA and SDMA concentrations were significantly higher in patients with CAD compared with healthy subjects. Pre- and post-stress SDMA concentrations were significantly higher in patients with transient myocardial perfusion abnormality compared with those with negative MPS results. However, ADMA and L-arginine concentrations were not significantly different between the two groups. None of the markers were significantly different between patients with angiographically proven low coronary flow and those with normal coronary flow. Pre-stress SDMA concentrations were an independent predictor of cardiovascular mortality during a 8-year follow-up. Conclusions Elevated serum SDMA concentrations may be helpful for selecting high-risk patients with CAD if there is any doubt in interpreting MPS. SDMA concentrations may also predict cardiovascular outcome.