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Background And Objectives: Hyperacute cardiac CT has shown greater yield for intracardiac thrombus identification compared with transthoracic echocardiography. However, routine use comes with higher cost and additional contrast and radiation exposure. Pretest identification of patients with low probability of thrombus would enable rationalization of its use. Arterial input function (AIF) curves are generated automatically as part of brain perfusion CT. Time from scan onset to the end of AIF (AIF dispersal) is correlated with left ventricular ejection fraction. We hypothesized that there would be an association between AIF dispersal and (1) presence of intracardiac thrombus and (2) 3-month outcome after ischemic stroke/TIA.
Methods: This is a retrospective analysis of prospectively collected patients with a final diagnosis of ischemic stroke or TIA presenting at 3 comprehensive stroke centers between September 2019 and August 2023.
Results: A total of 1,136 patients were included, and the median age was 74 years (interquartile range, IQR [62-81]). The median baseline National Institutes of Health Stroke Scale score was 6 (IQR [3-14]). Intracardiac thrombus was present in 59 patients (5.2%) on hyperacute cardiac CT. The median AIF dispersal was 27 (IQR [22-33]) seconds. Longer AIF dispersal duration was associated with presence of intracardiac thrombus, with an odds ratio (OR) of 1.09 (95% CI 1.05-1.13). AIF dispersal ≥33 seconds was the optimal cutoff point for presence of intracardiac thrombus with a positive association, with OR 6.66 (3.26-13.59). AIF dispersal as a continuous variable was associated with increased risk of poor outcome (modified Rankin Scale scores 5-6) 3 months after stroke in multivariate analysis (OR 1.03 [95% CI 1.00-1.05]). AIF dispersal ≥33 seconds was also associated with worse outcome after stroke in univariate analysis.
Discussion: Prolonged AIF dispersal identifies patients with stroke more likely to have (1) an intracardiac thrombus at the time of presentation and (2) poor outcome 3 months after stroke. These novel findings have significant clinical implications.
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http://dx.doi.org/10.1212/WNL.0000000000210256 | DOI Listing |
AJNR Am J Neuroradiol
May 2025
From the Liverpool Hospital, Liverpool, New South Wales, Australia, Department of Neurology and Neurophysiology (L.S.E., C.CS., D.C., L.L., M.W.P) South Western Sydney Clinical School, UNSW (L.S.E., C.CS., D.C., L.L., C.C., M.W.P) and Ingham Institute for Applied Medical Research, Sydney (L.S.E., C.
Background And Purpose: CTP Software packages utilise various mathematical techniques to transform source data into clinically useful maps. These techniques have not been validated for Posterior circulation infarction (POCI). Studies of anterior circulation stroke have shown that algorithm differences significantly influence the accuracy and best tissue parameters and thresholds of output maps.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
July 2025
Department of Interventional Radiology (N.S., G.A.C.), University of Chicago, Chicago, Illinois.
Background And Purpose: In acute ischemic stroke, the amount of "local" CBF distal to the occlusion, ie, all blood flow, whether supplied antegrade or delayed and dispersed through the collateral network, may contain valuable information regarding infarct growth rate and treatment response. DSC processed with a local arterial input function (AIF) is one method of measuring local CBF (local-qCBF) and has been shown to correlate with collateral supply. Similarly, intravoxel incoherent motion MRI (IVIM) is "local," with excitation and readout in the same plane, and a potential alternative way to measure local-qCBF.
View Article and Find Full Text PDFNeurology
February 2025
Department of Neurology, John Hunter Hospital, Newcastle, Australia.
J Appl Clin Med Phys
February 2025
Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
Purpose: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.
Methods: DCE-MRI data for simulation studies were synthesized using the extended Tofts model and a population-averaged arterial input function (AIF). The ranges of PK parameters for training the RNNs were determined from data of patients with brain tumors.
Cancers (Basel)
August 2024
Department of Radiological Sciences, University of California, Los Angeles, CA 92521, USA.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming.
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