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Motion-Compensated Multishot Pancreatic Diffusion-Weighted Imaging With Deep Learning-Based Denoising.
Invest Radiol
January 2025
From the Department of Radiology, Stanford University, Stanford, CA (K.W., M.J.M., A.M.L., A.B.S., A.J.H., D.B.E., R.L.B.); Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA (K.W.); GE HealthCare, Houston, TX (X.W.); GE HealthCare, Boston, MA (A.G.); and GE HealthCare, Menlo Park, CA (P.L.).
Objectives: Pancreatic diffusion-weighted imaging (DWI) has numerous clinical applications, but conventional single-shot methods suffer from off resonance-induced artifacts like distortion and blurring while cardiovascular motion-induced phase inconsistency leads to quantitative errors and signal loss, limiting its utility. Multishot DWI (msDWI) offers reduced image distortion and blurring relative to single-shot methods but increases sensitivity to motion artifacts. Motion-compensated diffusion-encoding gradients (MCGs) reduce motion artifacts and could improve motion robustness of msDWI but come with the cost of extended echo time, further reducing signal.
View Article and Find Full Text PDFArtificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.
PLoS One
January 2025
Electrical, Mechanical & Computer Engineering School, Federal University of Goias, Goiania, Brazil.
This paper proposes the use of artificial intelligence techniques, specifically the nnU-Net convolutional neural network, to improve the identification of left ventricular walls in images of myocardial perfusion scintigraphy, with the objective of improving the diagnosis and treatment of coronary artery disease. The methodology included data collection in a clinical environment, followed by data preparation and analysis using the 3D Slicer Platform for manual segmentation, and subsequently, the application of artificial intelligence models for automated segmentation, focusing on the efficiency of identifying the walls of the left ventricular. A total of 83 clinical routine exams were collected, each exam containing 50 slices, which is 4,150 images.
View Article and Find Full Text PDFAdult-type ALCAPA: missed at preoperative evaluation.
Indian J Thorac Cardiovasc Surg
February 2025
Ankara City Hospital Cardiovascular Surgery, Ankara, Turkey.
Unlabelled: The Bland-White-Garland syndrome, or Anomalous Origin of the Left Coronary Artery from the Pulmonary Artery (ALCAPA) syndrome, is a rare congenital cardiac anomaly often associated with high mortality, if left untreated. We present a case of a 43-year-old female with undiagnosed ALCAPA who initially underwent mitral valve surgery for severe mitral regurgitation, only to require reoperation due to adult-type ALCAPA. Intraoperatively, the discovery of dilated right coronary artery and its branches and absence of the left coronary ostium prompted further investigation, leading to the diagnosis of adult-type ALCAPA.
View Article and Find Full Text PDFEnhancing Diagnostic Precision: Intracardiac Echocardiography-Guided Endomyocardial Biopsy for Complex Right Atrial Mass.
JACC Case Rep
December 2024
Department of Cardiology, Centro Hospitalar Universitário Lisboa Norte,Centro Cardiovascular da Universidade de Lisboa (CCUL@RISE), Faculdade de Medicina da Universidade de Lisboa, Centro Académico de Medicina de Lisboa, Lisbon, Portugal.
An 80-year-old woman with a history of B-cell non-Hodgkin lymphoma presented to the emergency department with exertional dyspnea and lower limb edema. A transthoracic echocardiogram revealed a large extracardiac mass invading the right atrium. A diagnostic transcatheter endomyocardial biopsy guided by intracardiac echocardiography was performed.
View Article and Find Full Text PDFCorrigendum to A-41 | Percutaneous Coronary Intervention With Procedural Unfractionated Heparin Without Activated Clotting Time Guidance: A Unique Opportunity to Assess Thrombotic and Bleeding Events [, 2(3S), 100705].
J Soc Cardiovasc Angiogr Interv
January 2024
American University of Beirut Medical Center, Beirut, Lebanon.
[This corrects the article DOI: 10.1016/j.jscai.
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