Joint cardiac T₁ mapping and cardiac function estimation using a deep manifold framework

2022/05/16 by Qing Zou, Zou, Qing, Mathews Jacob +1
Medicine · #Advanced MRI Techniques and Applications #Cardiac Imaging and Diagnostics #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Medical Imaging Techniques and Applications #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2205.07994

openalex publication_date 2022/05/16 · openalex created_date 2022/05/22 · openalex updated_date 2026/07/28

Abstract

In this work, we proposed a continuous-acquisition strategy using a gradient echo (GRE) inversion recovery sequence based on spiral trajectories to simultaneously obtain the T₁ mapping and CINE imaging. The acquisition is using a free-breathing and ungated fashion. An approach based on variational auto-encoder(VAE) is used for the motion estimation from the centered k-space data. The motion signal is then used to train a deep manifold reconstruction algorithm for image reconstruction. Once the network is trained, we can excite the latent vectors (the estimated motion signals and the contrast signal) in any way as we wanted to generate the image frames in the time series. We can estimate the T₁ mapping using the generated image frames where only contrast is varying. We can also generate the breath-hold CINE in different contrast.

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