Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Javier Montalt-Tordera"'
Autor:
Olivier Jaubert, Michele Pascale, Javier Montalt-Tordera, Julius Akesson, Ruta Virsinskaite, Daniel Knight, Simon Arridge, Jennifer Steeden, Vivek Muthurangu
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-10 (2024)
Abstract To develop and assess a deep learning (DL) pipeline to learn dynamic MR image reconstruction from publicly available natural videos (Inter4K). Learning was performed for a range of DL architectures (VarNet, 3D UNet, FastDVDNet) and correspon
Externí odkaz:
https://doaj.org/article/5a7a8d4bd2594e2790bb7d058cc85d40
Autor:
Pedro Osorio, Guillermo Jimenez-Perez, Javier Montalt-Tordera, Jens Hooge, Guillem Duran-Ballester, Shivam Singh, Moritz Radbruch, Ute Bach, Sabrina Schroeder, Krystyna Siudak, Julia Vienenkoetter, Bettina Lawrenz, Sadegh Mohammadi
Publikováno v:
Diagnostics, Vol 14, Iss 13, p 1442 (2024)
Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solu
Externí odkaz:
https://doaj.org/article/54e91f0a90cb42fda1e3c97fecbca72b
Autor:
Javier Montalt-Tordera, Endrit Pajaziti, Rod Jones, Emilie Sauvage, Rajesh Puranik, Aakansha Ajay Vir Singh, Claudio Capelli, Jennifer Steeden, Silvia Schievano, Vivek Muthurangu
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 24, Iss 1, Pp 1-14 (2022)
Abstract Background Computational fluid dynamics (CFD) is increasingly used for the assessment of blood flow conditions in patients with congenital heart disease (CHD). This requires patient-specific anatomy, typically obtained from segmented 3D card
Externí odkaz:
https://doaj.org/article/7f0a60851aa041c4b1457e35d9786eb5
Autor:
Endrit Pajaziti, Javier Montalt-Tordera, Claudio Capelli, Raphaël Sivera, Emilie Sauvage, Michael Quail, Silvia Schievano, Vivek Muthurangu
Publikováno v:
PLoS Computational Biology, Vol 19, Iss 4, p e1011055 (2023)
Computational fluid dynamics (CFD) can be used to simulate vascular haemodynamics and analyse potential treatment options. CFD has shown to be beneficial in improving patient outcomes. However, the implementation of CFD for routine clinical use is ye
Externí odkaz:
https://doaj.org/article/88220f5e6c8f43c681a9f8ed3313e324
Autor:
Jennifer A. Steeden, Grzegorz T Kowalik, Olivier Jaubert, Vivek Muthurangu, Simon R. Arridge, Javier Montalt-Tordera
Publikováno v:
Magnetic Resonance Imaging. 83:125-132
Purpose Real-time spiral phase contrast MR (PCMR) enables rapid free-breathing assessment of flow. Target spatial and temporal resolutions require high acceleration rates often leading to long reconstruction times. Here we propose a deep artifact sup
Autor:
Francisco Borja Belloch, Teresa Diaz-Perdigon, Elisabeth Venzala, Rosa M. Tordera, Javier Montalt-Tordera, Elena Beltran, Philippe Delagrange, Erika Cecon, Elena Puerta
Publikováno v:
European Neuropsychopharmacology. 44:51-65
Circadian rhythms disturbance is widely observable in patients with major depression (MD) and is also associated with depression vulnerability. Of them, disturbed melatonin secretion rhythm is particularly relevant to MD and is strongly phase-locked
Autor:
Javier Montalt-Tordera, Grzegorz T Kowalik, Vivek Muthurangu, Jennifer A. Steeden, Alexander Gotschy
Publikováno v:
Magnetic Resonance Imaging. 72:1-7
Three-dimensional cine imaging provides a wealth of information about cardiac anatomy and function, but its use in the clinical environment is limited because data acquisition is very time consuming. In this work, a free-breathing 3D whole-heart cine
Autor:
Olivier Jaubert, Javier Montalt‐Tordera, James Brown, Daniel Knight, Simon Arridge, Jennifer Steeden, Vivek Muthurangu
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7e3b980b69ee3ff6a8e3b7cc0e5a5515
Publikováno v:
Journal of Magnetic Resonance Imaging. 57:204-205
Publikováno v:
Journal of magnetic resonance imaging : JMRI. 54(3)
BACKGROUND: Contrast-enhanced magnetic resonance angiography (MRA) is used to assess various cardiovascular conditions. However, gadolinium-based contrast agents (GBCAs) carry a risk of dose-related adverse effects. PURPOSE: To develop a deep learnin