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D P O'Regan

Showing results (1-10 of 11) with videos related to

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Clinical Radiology|May 14, 2019
Putting machine learning into motion: applications in cardiovascular imagingD P O'Regan
Clinical Radiology|February 21, 2006
Establishing a clinical cardiac MRI serviceD P O'Regan, S A Schmitz
Clinical Radiology|July 19, 2022
Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validationD H Mallon, C D McNamara, G S Rahmani, et al.
Clinical Radiology|January 17, 2016
Acute myocardial infarction: susceptibility-weighted cardiac MRI for the detection of reperfusion haemorrhage at 1.5 TG Durighel, P F Tokarczuk, A Karsa, et al.
Anaesthesia|November 15, 2018
Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance studyT J W Dawes, A de Marvao, W Shi, et al.
Journal of Biomechanics|July 5, 2017
On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamicsS Pirola, Z Cheng, O A Jarral, et al.
The British Journal of Radiology|September 24, 2005
A comparison of MR cholangiopancreatography at 1.5 and 3.0 TeslaD P O'Regan, J Fitzgerald, J Allsop, et al.
Clinical Radiology|February 21, 2006
Technical report: Magnetic resonance direct thrombus imaging at 3 T field strength in patients with lower limb deep vein thrombosis: a feasibility studyS A Schmitz, D P O'Regan, D Gibson, et al.
Acta Radiologica (Stockholm, Sweden : 1987)|February 27, 2008
White matter brain lesions in midlife familial hypercholesterolemic patients at 3-Tesla magnetic resonance imagingS A Schmitz, D P O'Regan, J Fitzpatrick, et al.
Frontiers in Bioengineering and Biotechnology|July 3, 2025
Patient-specific modelling of pulmonary arterial hypertension: wall shear stress correlates with disease severityC H Armour, D Gopalan, B Statton, et al.
Pageof 2

Showing results (1-10 of 11) with videos related to

Sort By:
Pageof 2
Clinical Radiology|May 14, 2019
Putting machine learning into motion: applications in cardiovascular imagingD P O'Regan
Clinical Radiology|February 21, 2006
Establishing a clinical cardiac MRI serviceD P O'Regan, S A Schmitz
Clinical Radiology|July 19, 2022
Automated detection of enteric tubes misplaced in the respiratory tract on chest radiographs using deep learning with two centre validationD H Mallon, C D McNamara, G S Rahmani, et al.
Clinical Radiology|January 17, 2016
Acute myocardial infarction: susceptibility-weighted cardiac MRI for the detection of reperfusion haemorrhage at 1.5 TG Durighel, P F Tokarczuk, A Karsa, et al.
Anaesthesia|November 15, 2018
Identifying the optimal regional predictor of right ventricular global function: a high-resolution three-dimensional cardiac magnetic resonance studyT J W Dawes, A de Marvao, W Shi, et al.
Journal of Biomechanics|July 5, 2017
On the choice of outlet boundary conditions for patient-specific analysis of aortic flow using computational fluid dynamicsS Pirola, Z Cheng, O A Jarral, et al.
The British Journal of Radiology|September 24, 2005
A comparison of MR cholangiopancreatography at 1.5 and 3.0 TeslaD P O'Regan, J Fitzgerald, J Allsop, et al.
Clinical Radiology|February 21, 2006
Technical report: Magnetic resonance direct thrombus imaging at 3 T field strength in patients with lower limb deep vein thrombosis: a feasibility studyS A Schmitz, D P O'Regan, D Gibson, et al.
Acta Radiologica (Stockholm, Sweden : 1987)|February 27, 2008
White matter brain lesions in midlife familial hypercholesterolemic patients at 3-Tesla magnetic resonance imagingS A Schmitz, D P O'Regan, J Fitzpatrick, et al.
Frontiers in Bioengineering and Biotechnology|July 3, 2025
Patient-specific modelling of pulmonary arterial hypertension: wall shear stress correlates with disease severityC H Armour, D Gopalan, B Statton, et al.
Pageof 2