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Your search keyword '"Monica Sigovan"' showing total 5 results
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5 results on '"Monica Sigovan"'

1. MRI hemodynamic markers of progressive bicuspid aortic valve-related aortic disease

Author

S. Jarrett Wrenn, Petter Dyverfeldt, David Saloner, Monica Sigovan, and Michael D. Hope

Subjects
Aortic valve, congenital, hereditary, and neonatal diseases and abnormalities, Aorta, medicine.medical_specialty, medicine.diagnostic_test, business.industry, Disease progression, Hemodynamics, medicine.disease, Aortic disease, Magnetic resonance angiography, medicine.anatomical_structure, Bicuspid aortic valve, medicine.artery, Internal medicine, Cohort, cardiovascular system, Cardiology, Medicine, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Radiology, business
Abstract

Purpose To determine the reproducibility of MRI aortic hemodynamic markers and to assess their relationship to aortic growth in a cohort of patients with bicuspid aortic valves (BAV).

Published
2013
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3. CMR 2007: 1.04:In-vivo USPIO's follow-up and optimized MRI protocol for inflammation imaging in atherosclerotic plaques

Author

C. Desbleds-Mansard, Philippe Douek, Monica Sigovan, Emmanuelle Canet-Soulas, Claire Corot, Jean-Sébastien Raynaud, Loic Boussel, D. Ibarrola, Xavier Violas, D. Gamondes, Eric Lancelot, Dominique Sappey-Marinier, and A. Sulaiman

Subjects
Pathology, medicine.medical_specialty, In vivo, business.industry, Inflammation imaging, medicine, Radiology, Nuclear Medicine and imaging, business
Published
2007
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4. Response

Author

Michael D. Hope, Monica Sigovan, Petter Dyverfeldt, and David Saloner

Subjects
Radiology, Nuclear Medicine and imaging
Published
2012
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5. Deep learning methods for blood flow reconstruction in a vessel with contrast enhanced x-ray computed tomography.

Author

Shusong H, Monica S, and Bruno S

Subjects
Tomography, X-Ray Computed methods, Hemodynamics, X-Rays, Image Processing, Computer-Assisted methods, Algorithms, Deep Learning
Abstract

The reconstruction of blood velocity in a vessel from contrast enhanced x-ray computed tomography projections is a complex inverse problem. It can be formulated as reconstruction problem with a partial differential equation constraint. A solution can be estimated with the a variational adjoint method and proper orthogonal decomposition (POD) basis. In this work, we investigate new inversion approaches based on PODs coupled with deep learning methods. The effectiveness of the reconstruction methods is shown with simulated realistic stationary blood flows in a vessel. The methods outperform the reduced adjoint method and show large speed-up at the online stage., (© 2023 John Wiley & Sons Ltd.)

Published
2024
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