scholarly journals Cardiovascular magnetic resonance imaging for accurate sizing of the left atrium: Predictability of pulmonary vein isolation success in patients with atrial fibrillation

2011 ◽  
Vol 33 (2) ◽  
pp. 455-463 ◽  
Author(s):  
Cosima Jahnke ◽  
Julia Fischer ◽  
Jesus Gonzalez Mirelis ◽  
Charalampos Kriatselis ◽  
Jin-Hong Gerds-Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Left Atrium ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
Cardiovascular Magnetic Resonance Imaging ◽  
Resonance Imaging

scholarly journals Feasibility and validation of trans-valvular flow derived by four-dimensional flow cardiovascular magnetic resonance imaging in patients with atrial fibrillation

2021 ◽  
Vol 6 ◽  
pp. 73
Author(s):  
Mark T Mills ◽  
Ciaran Grafton-Clarke ◽  
Gareth Williams ◽  
Rebecca C Gosling ◽  
Abdulaziz Al Baraikan ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Atrial Fibrillation ◽  
Cardiovascular Magnetic Resonance ◽  
Magnetic Resonance ◽  
Image Quality ◽  
Cardiovascular Magnetic Resonance Imaging ◽  
Healthy Controls ◽  
Cardiovascular Mri ◽  
Resonance Imaging ◽  
4D Flow

Background: Four-dimensional (4D) flow cardiovascular magnetic resonance imaging (MRI) is an emerging technique used for intra-cardiac blood flow assessment. The role of 4D flow cardiovascular MRI in the assessment of trans-valvular flow in patients with atrial fibrillation (AF) has not previously been assessed. The purpose of this study was to assess the feasibility, image quality, and internal validity of 4D flow cardiovascular MRI in the quantification of trans-valvular flow in patients with AF. Methods: Patients with AF and healthy controls in sinus rhythm underwent cardiovascular MRI, including 4D flow studies. Quality assurance checks were done on the raw data and streamlines. Consistency was investigated by trans-valvular flow assessment between the mitral valve (MV) and the aortic valve (AV). Results: Eight patients with AF (88% male, mean age 62±13 years, mean heart rate (HR) 83±16 beats per minute (bpm)) were included and compared with ten healthy controls (70% male, mean age 41±20 years, mean HR 68.5±9 bpm). All scans were of either good quality with minimal blurring artefacts, or excellent quality with no artefacts. No significant bias was observed between the AV and MV stroke volumes in either healthy controls (–4.8, 95% CI –15.64 to 6.04; P=0.34) or in patients with AF (1.64, 95% CI –4.7 to 7.94; P=0.56). A significant correlation was demonstrated between MV and AV stroke volumes in both healthy controls (r=0.87, 95% CI 0.52 to 0.97; P=0.001) and in AF patients (r=0.82, 95% CI 0.26 to 0.97; P=0.01). Conclusions: In patients with AF, 4D flow cardiovascular MRI is feasible with good image quality, allowing for quantification of trans-valvular flow.

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.

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