Search Results for author: Michael Tanzer

Found 3 papers, 1 papers with code

T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer

no code implementations28 Sep 2023 Fanwen Wang, Michael Tanzer, Mengyun Qiao, Wenjia Bai, Daniel Rueckert, Guang Yang, Sonia Nielles-Vallespin

Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application.

Decoder

Review of data types and model dimensionality for cardiac DTI SMS-related artefact removal

1 code implementation20 Sep 2022 Michael Tanzer, Sea Hee Yook, Guang Yang, Daniel Rueckert, Sonia Nielles-Vallespin

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 Intelligence is becoming a crucial tool in mitigating some of its drawbacks, such as the long scan times.

Faster Diffusion Cardiac MRI with Deep Learning-based breath hold reduction

no code implementations21 Jun 2022 Michael Tanzer, Pedro Ferreira, Andrew Scott, Zohya Khalique, Maria Dwornik, Dudley Pennell, Guang Yang, Daniel Rueckert, Sonia Nielles-Vallespin

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 no other imaging modality allows.

Ensemble Learning

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