SmartHeart Conference 2022
Friday June 17th, 2022
Join our one-day conference to learn about how SmartHeart researchers have been using machine learning methods to make MR cardiac imaging faster, smarter and more efficient.
Participation is free but registration is required.
Location: Imperial College London, South Kensington
Registration, refreshments and networking
Introduction and overview of the SmartHeart Project
Daniel Rueckert, Imperial College London & TU Munich
MR fingerprinting: towards a virtual biopsy for cardiovascular MRI
Claudia Prieto, King’s College London
3D multi-parametric whole heart MRI: simple, fast and efficient
René Botnar, King’s College London
AI-enabled cardiac imaging quality control
Julia Schnabel, King’s College London & TU Munich
Pathways from MSK to Cardiac MRI Reconstruction
Kerstin Hammernik, Imperial College London & TU Munich
Coffee & Poster Session
KEYNOTE TALK I
Combining Modelling and Imaging of the Heart
Blanca Rodriguez, University of Oxford
KEYNOTE TALK II
An industry perspective on AI in CMR
Speaker TBC, Siemens Healthineers
KEYNOTE TALK III
The Potential of AI in Cardiac MR: a clinical perspective
Rhodri Davies, University College London
AI has the potential to transform clinical practice in cardiac MRI. Programmes such as SmartHeart and the availability of standardised cardiac MR images have made this a fertile area of research. Progress in AI research is propagating into clinical practice and most CMR analysis tools now have AI at their core. I will give a clinical perspective on the use of AI for cardiac MR and explore its potential, focussing on AI for precision measurement, showing the profound effect it has on clinical management. I will explore some of the challenges in translating AI algorithms into clinical use, and look ahead at other potential applications and opportunities for AI in clinical work.
Poster Session & Drinks
Rhodri Davies is an Associate Clinical Professor in Cardiology and Machine Learning at the Institute of Cardiovascular Science, University College London and Barts Heart Centre. Having originally trained in computer science, he obtained a PhD in statistical shape analysis for medical imaging before complete clinical training. He combines his role at UCL with a post as consultant cardiologist at the University Hospital of Wales, specialising in cardiac MRI and echocardiography. His research interests are in applying AI to cardiac imaging to gain insight into health and disease and improve patient care.
Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction Wenjia Bai, Imperial College London
A cooperative training method to improve model cross-domain performance using single-domain data only Chen Chen, Imperial College London
Realistic Adversarial Data Augmentation Chen Chen, Imperial College London
AtrialJSQnet: A New framework for joint segmentation and quantification of left atrium and scars incorporating spatial and shape information Lei Li, University of Oxford
Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework Lei Li, University of Oxford
Multiscale Graph Convolutional Networks for Cardiac Motion Analysis Ping Lu, University of Oxford
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis Ines Machado, Kings College London
Self-Supervision with Superpixels: Training Few-shot Medical Image Segmentation without Annotation Cheng Ouyang, Imperial College London
Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation Cheng Ouyang, Imperial College London
Free-running 3D T1 and T2 myocardial mapping and cine MRI in 3 minutes using low-rank non-rigid motion-corrected reconstruction Andrew Phair, Kings College London
Fairness in Cardiac MR Image Analysis: An Investigation of Bias Due to Data Imbalance in Deep Learning Based Segmentation Esther Puyol-Anton, Kings College London
Interpretable Deep Models for Cardiac Resynchronisation Therapy Response Prediction Esther Puyol-Anton, Kings College London
MR physics-based data augmentation for cardiac MR segmentation in different domains Devran Ugurlu, Kings College London
Joint Motion Correction and Super Resolution for Cardiac Segmentation Shuo Wang, Imperial College London
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation Shuo Wang, Imperial College London
Friday June 17th, 2022
09:00 – 17:00
Room 3.01 (level 3)
Royal School of Mines
Imperial College London
South Kensington, London, SW7 2AZ
Please note the following before registering:
- Participation is free but registration is required.
- In line with COVID-19 safety precautions, if you feel sick or are experiencing COVID-like symptoms prior to the meeting, we ask that you do not participate in-person.
- Spaces are limited, so if you are no longer able to attend, please let us know so that your place can be offered to someone else.