Zobrazeno 1 - 10
of 290
pro vyhledávání: '"Steeden, Jennifer"'
Autor:
Baker, Rebecca R., Muthurangu, Vivek, Rega, Marilena, Walsh, Stephen B., Steeden, Jennifer A.
23Na MRI can be used to quantify in-vivo tissue sodium concentration (TSC), but the low 23Na signal leads to long scan times and/or noisy or low-resolution images. Reconstruction algorithms such as CS have been proposed to mitigate low SNR; although,
Externí odkaz:
http://arxiv.org/abs/2406.02553
Autor:
Yao, Tina, Pajaziti, Endrit, Quail, Michael, Schievano, Silvia, Steeden, Jennifer A, Muthurangu, Vivek
Computational fluid dynamics (CFD) can be used for evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning mode
Externí odkaz:
http://arxiv.org/abs/2402.18236
Autor:
Jaubert, Olivier, Pascale, Michele, Montalt-Tordera, Javier, Akesson, Julius, Virsinskaite, Ruta, Knight, Daniel, Arridge, Simon, Steeden, Jennifer, Muthurangu, Vivek
Purpose: To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Materials and Methods: Learning was performed for a range of DL architectures (VarNet, 3D UNet, Fa
Externí odkaz:
http://arxiv.org/abs/2311.13963
Autor:
Pascale, Michele, Muthurangu, Vivek, Tordera, Javier Montalt, Fitzke, Heather E, Bhatnagar, Gauraang, Taylor, Stuart, Steeden, Jennifer
Three-dimensional (3D) imaging is popular in medical applications, however, anisotropic 3D volumes with thick, low-spatial-resolution slices are often acquired to reduce scan times. Deep learning (DL) offers a solution to recover high-resolution feat
Externí odkaz:
http://arxiv.org/abs/2303.11831
Autor:
Yao, Tina, Clair, Nicole St., Miller, Gabriel F., Dorfman, Adam L., Fogel, Mark A., Ghelani, Sunil, Krishnamurthy, Rajesh, Lam, Christopher Z., Robinson, Joshua D., Schidlow, David, Slesnick, Timothy C., Weigand, Justin, Quail, Michael, Rathod, Rahul, Steeden, Jennifer A., Muthurangu, Vivek
Purpose: To develop and evaluate an end-to-end deep learning pipeline for segmentation and analysis of cardiac magnetic resonance images to provide core-lab processing for a multi-centre registry of Fontan patients. Materials and Methods: This retros
Externí odkaz:
http://arxiv.org/abs/2303.11676
Autor:
Montalt-Tordera, Javier, Pajaziti, Endrit, Jones, Rod, Sauvage, Emilie, Puranik, Rajesh, Singh, Aakansha Ajay Vir, Capelli, Claudio, Steeden, Jennifer, Schievano, Silvia, Muthurangu, Vivek
Background: Computational fluid dynamics (CFD) is increasingly used to assess blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, usually obtained from segmented 3D cardiovascular magnetic re
Externí odkaz:
http://arxiv.org/abs/2209.06117
Autor:
Jaubert, Olivier, Montalt-Tordera, Javier, Brown, James, Knight, Daniel, Arridge, Simon, Steeden, Jennifer, Muthurangu, Vivek
Publikováno v:
Magn Reson Med . 2022 Nov;88(5):2179-2189
Purpose: Real-time monitoring of cardiac output (CO) requires low latency reconstruction and segmentation of real-time phase contrast MR (PCMR), which has previously been difficult to perform. Here we propose a deep learning framework for 'Flow Recon
Externí odkaz:
http://arxiv.org/abs/2203.13729
Purpose: We implemented the Machine Learning (ML) aided k-t SENSE reconstruction to enable high resolution quantitative real-time phase contrast MR (PCMR). Methods: A residual U-net and our U-net M were used to generate the high resolution x-f space
Externí odkaz:
http://arxiv.org/abs/2103.07541
Magnetic Resonance Imaging (MRI) plays a vital role in diagnosis, management and monitoring of many diseases. However, it is an inherently slow imaging technique. Over the last 20 years, parallel imaging, temporal encoding and compressed sensing have
Externí odkaz:
http://arxiv.org/abs/2012.05303
Autor:
Steeden, Jennifer A., Quail, Michael, Gotschy, Alexander, Hauptmann, Andreas, Arridge, Simon, Jones, Rodney, Muthurangu, Vivek
Background: Three-dimensional, whole heart, balanced steady state free precession (WH-bSSFP) sequences provide delineation of intra-cardiac and vascular anatomy. However, they have long acquisition times. Here, we propose significant speed ups using
Externí odkaz:
http://arxiv.org/abs/1912.10503