16-fold accelerated, single-shot late gadolinium enhancement CMR using GRASP for multi-TI reconstruction

Accelerated In Vivo Cardiac Diffusion Tensor MRI with Residual Deep Learning based Denoising in Lean and Obese Subjects

AI-supported Segmentation of the Whole Left Atrium in Cine MRI Identifies a New Geometrical Predictor of Outcome in Atrial Fibrillation

Applications of Machine Learning in Clinical Cardiovascular MRI

    Automatic quantification of ultra-high resolution quantitative first-pass perfusion imaging using deep-learning based segmentation and MOCO

    Cardiac MR fingerprinting with a short acquisition window in healthy volunteers and 62 consecutive patients referred for clinical CMR

    Cardiac Tag Tracking with Deep Learning Trained with Comprehensive Synthetic Data Generation

    Effects of Accelerated Acquisition of Myocardial Creatine CEST MRI in the Healthy Human Heart at 3T

    Author:Kevin Godines  Wissam AlGhuraibawi  Bonnie Lam  Moriel Vandsburger  

    Institution:University of California Berkeley  

    Session Type:Oral  

    Session Live Q&A Date:Wednesday, 12 August 2020  

    Topic:Machine Learning and Tissue Characterisation in CMR  

    Session Name:CMR Tissue Characterisation  

    Program Number:0792  

    Room Live Q&A Session:Wednesday Parallel 1  

    Free-breathing continuous cine and T1 mapping acquisition using a motion-corrected dual flip angle inversion-recovery spiral technique at 3 T

    Fully Automated Multivendor and Multisite Artificial Intelligence-based 3D Segmentation of the Proximal Arteries from 4D flow MRI

    Improved In Vivo Estimation of the Reynolds Stress Tensor from 4D und 5D Flow MRI Using Cholesky Decomposition-Based Neural Networks

    Improved SMS Reconstruction using ReadOut-Concatenated K-space SPIRiT (ROCK-SPIRiT)

    In-vivo application of a trained neural network using a fusion of computational fluid dynamic and 4D flow MRI data

    A Joint Multi-Scale Variational Neural Network for Accelerating Free-breathing Whole-Heart qBOOST-T2 mapping

    A level-set reformulated as deep recurrent network for left/right ventricle segmentation on cardiac cine MRI

    Leveraging Anatomical Similarity for Unsupervised Model Learning and Synthetic MR Data Generation

    Machine Learning & Future Clinical Practice in Cardiovascular MRI

      A Multi-Scale Variational Neural Network for accelerating bright- and black-blood 3D whole-heart MRI in patients with congenital heart disease

      Myocardial T1, T2, T2* & ECV Mapping: Upcoming Technical Solutions to Practical Problems

      Author:René Botnar  

      Institution:King's College London  

      Session Type:Oral  

      Session Live Q&A Date:Wednesday, 12 August 2020  

      Topic:Machine Learning and Tissue Characterisation in CMR  

      Session Name:CMR Tissue Characterisation  

      Program Number:

      Room Live Q&A Session:Wednesday Parallel 1  

      Quantifying the underestimation of myocardial extra cellular volume fraction measurements due to transcytolemmal water exchange

      Author:Andrew Scott  Peter Gatehouse  David Firmin  

      Institution:The Royal Brompton Hospital  Imperial College London  

      Session Type:Oral  

      Session Live Q&A Date:Wednesday, 12 August 2020  

      Topic:Machine Learning and Tissue Characterisation in CMR  

      Session Name:CMR Tissue Characterisation  

      Program Number:0791  

      Room Live Q&A Session:Wednesday Parallel 1  

      Reproducibility, Repeatability and Preliminary Clinical Results of Free-Breathing Isotropic 3D Whole-Heart T2 Mapping

      Respiratory Motion-compensated High-resolution 3D Whole-heart T1? Mapping

      Myocardial T1, T2, T2* & ECV Mapping: Upcoming Technical Solutions to Practical Problems

      Author:René Botnar  

      Institution:King’s College London  

      Session Type:Oral  

      Session Live Q&A Date:Wednesday, 12 August 2020  

      Topic:Machine Learning and Tissue Characterisation in CMR  

      Session Name:CMR Tissue Characterisation  

      Program Number:

      Room Live Q&A Session:Wednesday Parallel 1