1 citations · 1 across the 3 of their papers we have counts for
3 papers
eess.SP2023★ 1 cited
T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer
Fanwen Wang, Michael Tanzer, Mengyun Qiao +4
Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We pr…
eess.IV2022
Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal
Michael Tanzer, Sea Hee Yook, Guang Yang +2
As diffusion tensor imaging (DTI) gains popularity in cardiac imaging due to its unique ability to non-invasively assess the cardiac microstructure, deep learning-based Artificial…
eess.IV2022
Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction
Michael Tanzer, Pedro Ferreira, Andrew Scott +6
Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) enables us to probe the microstructural arrangement of cardiomyocytes within the myocardium in vivo and non-invasively, which n…