Purpose: To characterize misregistration artifact in arterial input function (AIF) pixels in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) using a two-dimensional non-echo-planar imaging (EPI)-based gradient-recalled echo (GRE) sequence.
Materials And Methods: Dynamic gadopentetate-enhanced MRI was acquired in the rat using a semikeyhole acquisition scheme. The AIF was obtained from abdominal aorta pixels. Different sliding-window reconstruction techniques were applied to determine which lines in a series of the semikeyhole acquisition were associated with the misregistration artifacts.
Results: The misregistration along the phase-encoding direction arose when k-space lines were acquired during the rise-time of the aortic gadolinium concentration. The maximum blood concentration of gadolinium estimated from the phase shift calculation agreed with that estimated from dosage.
Conclusion: AIF misregistration results from a phase shift due to increasing gadolinium concentration in the aorta, and may need to be considered in small animal DCE-MRI studies with a high rate of rise in the AIF in high-field MR applications.
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http://dx.doi.org/10.1002/jmri.20924 | DOI Listing |
Radiology
January 2025
From the Department of Cardiology (T.P., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), MIRACL.ai (Multimodality Imaging for Research and Analysis Core Laboratory: and Artificial Intelligence) (T.P., S.T., K.H., T.G., A.L., E.G., A.U., J.G.D., P.H.), Inserm MASCOT-UMRS 942 (T.P., K.H., T.A.S., T.G., A.L., E.G., A.U., J.G.D., P.H.), and Department of Radiology (T.P., V.B., L.H., T.G.), Université Paris Cité, University Hospital of Lariboisière, Assistance Publique-Hôpitaux de Paris, Paris, France; Cardiovascular Magnetic Resonance Laboratory (T.P., T.H., T.U., F.S., S.C., P.G., J.G.) and Cardiac Computed Tomography Laboratory (T.P., T.H., T.L., B.C., T.U., F.S., S.C., H.B., A.N., M.A., P.G., J.G.), Hôpital Privé Jacques Cartier, Institut Cardiovasculaire Paris Sud, Ramsay Santé, 6 Avenue du Noyer Lambert, 91300 Massy, France; Scientific Partnerships, Siemens Healthcare France, Saint-Denis, France (S.T.); Department of Cardiology, Hôpital Universitaire de Bruxelles-Hôpital Erasme, Brussels, Belgium (A.U.); and Department of Cardiovascular Imaging, American Hospital of Paris, Neuilly, France (O.V., M.S.).
Background Multimodality imaging is essential for personalized prognostic stratification in suspected coronary artery disease (CAD). Machine learning (ML) methods can help address this complexity by incorporating a broader spectrum of variables. Purpose To investigate the performance of an ML model that uses both stress cardiac MRI and coronary CT angiography (CCTA) data to predict major adverse cardiovascular events (MACE) in patients with newly diagnosed CAD.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Institute of Mathematical Sciences Centre for Health Analytics and Modelling (CHaM), Strathmore University, Nairobi, Kenya.
Background: Measures of diagnostic test accuracy provide evidence of how well a test correctly identifies or rules-out disease. Commonly used diagnostic accuracy measures (DAMs) include sensitivity and specificity, predictive values, likelihood ratios, area under the receiver operator characteristic curve (AUROC), area under precision-recall curves (AUPRC), diagnostic effectiveness (accuracy), disease prevalence, and diagnostic odds ratio (DOR) etc. Most available analysis tools perform accuracy testing for a single diagnostic test using summarized data.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Radiation Oncology, Henry Ford Hospital, Detroit, USA.
Best current practice in the analysis of dynamic contrast enhanced (DCE)-MRI is to employ a voxel-by-voxel model selection from a hierarchy of nested models. This nested model selection (NMS) assumes that the observed time-trace of contrast-agent (CA) concentration within a voxel, corresponds to a singular physiologically nested model. However, admixtures of different models may exist within a voxel's CA time-trace.
View Article and Find Full Text PDFAJNR Am J Neuroradiol
January 2025
From the Department of Radiology, Medical Physics (MML, TJC), Department of Interventional Radiology (NS, GAC), Department of Surgery and Large Animal Studies (MAN), and the Department of Statistics (MG), University of Chicago, Chicago, IL, USA; Department of Anesthesiology (SPR), University of Illinois, Chicago, IL, USA; Department of Radiology (MSS), University of Massachusetts Chan Medical School, Worcester, MA, USA; Department of Radiology, Biomedical Engineering and Imaging Institute (Current affiliation MML), Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mount Carmel Health Systems (Current affiliation GAC), Columbus, OH, USA.
Background And Purpose: In acute ischemic stroke, the amount of "local" CBF distal to the occlusion, i.e. all blood flow within a region whether supplied antegrade or delayed and dispersed through the collateral network, may contain valuable information regarding infarct growth rate and treatment response.
View Article and Find Full Text PDFPhysiol Meas
January 2025
Faculty of Sciences, University of Coimbra, Palacio de las Escuelas 3004-531, Coimbra, 3004-504, PORTUGAL.
Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.
Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals.
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