Asynchronous bubble pinch-off pattern arising in fluidized beds due to jet interaction: A magnetic resonance imaging and computational modeling study

2020 ◽  
Vol 5 (9) ◽  
Author(s):  
A. Penn ◽  
A. Padash ◽  
M. Lehnert ◽  
K. P. Pruessmann ◽  
C. R. Müller ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Computational Modeling ◽  
Fluidized Beds ◽  
Resonance Imaging ◽  
Jet Interaction ◽  
Modeling Study

Wake volume of injected bubbles in fluidized beds: A magnetic resonance imaging velocimetry study

2019 ◽  
Vol 357 ◽  
pp. 428-435 ◽  
Author(s):  
C.M. Boyce ◽  
A. Penn ◽  
M. Lehnert ◽  
K.P. Pruessmann ◽  
C.R. Müller
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fluidized Beds ◽  
Resonance Imaging

scholarly journals Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation

2020 ◽  
Vol 13 (7) ◽  
Author(s):  
Julie K. Shade ◽  
Rheeda L. Ali ◽  
Dante Basile ◽  
Dan Popescu ◽  
Tauseef Akhtar ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Magnetic Resonance ◽  
Computational Modeling ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Area Under The Curve ◽  
Resonance Imaging ◽  
Af Recurrence

Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. Methods: This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. Results: In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). Conclusions: ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.

scholarly journals Real-time magnetic resonance imaging of fluidized beds with internals

2019 ◽  
Vol 198 ◽  
pp. 117-123 ◽  
Author(s):  
Alexander Penn ◽  
Christopher M. Boyce ◽  
Nicholas Conzelmann ◽  
Gaëtan Bezinge ◽  
Klaas P. Pruessmann ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Real Time ◽  
Fluidized Beds ◽  
Resonance Imaging

Magnetic Resonance Imaging of fluidized beds

2008 ◽  
Vol 183 (1) ◽  
pp. 53-62 ◽  
Author(s):  
C.R. Müller ◽  
D.J. Holland ◽  
A.J. Sederman ◽  
M.D. Mantle ◽  
L.F. Gladden ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fluidized Beds ◽  
Resonance Imaging

Oscillations in gas-fluidized beds: Ultra-fast magnetic resonance imaging and pressure sensor measurements

2007 ◽  
Vol 177 (2) ◽  
pp. 87-98 ◽  
Author(s):  
C.R. Müller ◽  
J.F. Davidson ◽  
J.S. Dennis ◽  
P.S. Fennell ◽  
L.F. Gladden ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Pressure Sensor ◽  
Fluidized Beds ◽  
Resonance Imaging ◽  
Gas Fluidized Beds ◽  
Fast Magnetic Resonance Imaging

Real-Time Magnetic Resonance Imaging of Bubble Behavior and Particle Velocity in Fluidized Beds

2018 ◽  
Vol 57 (29) ◽  
pp. 9674-9682 ◽  
Author(s):  
Alexander Penn ◽  
Christopher M. Boyce ◽  
Thomas Kovar ◽  
Takuya Tsuji ◽  
Klaas P. Pruessmann ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Real Time ◽  
Particle Velocity ◽  
Fluidized Beds ◽  
Resonance Imaging ◽  
Bubble Behavior

Magnetic resonance imaging of fluidized beds: Recent advances

2008 ◽  
Vol 42 (5) ◽  
pp. 469-478 ◽  
Author(s):  
D. J. Holland ◽  
C. R. Müller ◽  
A. J. Sederman ◽  
M. D. Mantle ◽  
L. F. Gladden ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Fluidized Beds ◽  
Resonance Imaging ◽  
Recent Advances
Sign in / Sign up

Export Citation Format

Share Document