Improving accuracy of myocardial T1 estimation in MyoMapNet


MyoMapNet is a fully connected neural network for T1 estimation of an accelerated cardiac T1 mapping sequence, which collects 4 T1‐weighted images by a single Look‐Locker inversion‐recovery experiment (LL4). MyoMapNet was originally trained using in vivo data from the modified Look‐Locker inversion recovery sequence, which resulted in significant bias and sensitivity to various confounders. This study sought to train MyoMapNet using signals generated from numerical simulations and phantom MR data under multiple simulated confounders. The trained model was then evaluated by phantom data scanned using new phantom vials that differed from those used for training.

In Magnetic Resonance in Medicine
Salah Assana
Salah Assana
Research Assistant ll

I am a Artificial Intelligence and Healthcare enthusiast with a background in machine learning, signal processing and medical imaging.