Accelerated cardiac T1 mapping in four heartbeats with inline MyoMapNet: a deep learning-based T1 estimation approach


Cardiovascular magnetic resonance (CMR) myocardial T1 and extracellular volume (ECV) mapping enable non-invasive quantification of diffuse interstitial fibrosis. Generally, myocardial T1 mapping consists of a preparation pulse and collection of a series of images to sample the recovering longitudinal magnetization at different time points. Based on the evolution of the longitudinal magnetization across the acquired T1-weighted images, T1 at each pixel could be determined. Over the past decade, there have been significant advances in myocardial T1 mapping sequence with different choices of magnetization preparation, number of collected T1-weighted images, and recovery period between different imaging blocks. Trade-offs depend on accuracy and precision. There are also differences in terms of coverage and respiratory motion compensation (free breathing vs. breath-holding). There is also growing interest in using a single sequence to simultaneously measure different tissue relaxation times. These approaches often require a more complicated fitting model with more parameters, resulting in a loss of precision and significantly longer reconstruction time, which reduce their clinical utility.

In Journal of Cardiovascular Magnetic Resonance
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.