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Joint Cardiac T1 Mapping and Cardiac Cine Using Manifold Modeling.

Qing Zou1,2,3, Sarv Priya4, Prashant Nagpal5

  • 1Division of Pediatric Cardiology, Department of Pediatrics, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.

Bioengineering (Basel, Switzerland)
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This study introduces a novel free-breathing MRI protocol for simultaneous cardiac function and T1 mapping. The method uses deep learning to reconstruct images, improving patient comfort and visualization.

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CNNcardiac MRIgenerative modelimage reconstructionmanifold approachunsupervised learningvariational autoencoder

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Area of Science:

  • Cardiovascular Magnetic Resonance Imaging
  • Medical Image Reconstruction
  • Artificial Intelligence in Medicine

Background:

  • Current cardiac MRI protocols often require breath-holding, limiting patient comfort and scan efficiency.
  • Simultaneous acquisition of cardiac function (cine) and myocardial T1 maps is challenging with free-breathing techniques.

Purpose of the Study:

  • To develop a single, free-breathing, and ungated MRI protocol for joint estimation of cardiac function and myocardial T1 maps.
  • To leverage deep learning for efficient image reconstruction and phase estimation from undersampled k-space data.

Main Methods:

  • Reconstruction of a free-breathing, ungated inversion recovery gradient echo sequence using a manifold algorithm.
  • Modeling images as a non-linear function of cardiac/respiratory phases and inversion time using a convolutional neural network (CNN) generator.
  • Utilizing a dense conditional auto-encoder for cardiac and respiratory phase estimation from central k-space samples.

Main Results:

  • Successful joint estimation of cardiac function and myocardial T1 maps from free-breathing, ungated data.
  • Generation of synthetic cine MRI sequences with varying inversion contrasts for improved myocardial visualization.
  • Enabling phase-specific T1 map estimation, overcoming limitations of breath-held methods.

Conclusions:

  • The proposed free-breathing MRI protocol offers a unified approach for cardiac function and T1 mapping, enhancing patient comfort and scan efficiency.
  • Deep learning-based reconstruction significantly improves image quality and enables novel applications like synthetic contrast generation.
  • This framework advances cardiovascular MRI by providing a more comfortable and comprehensive assessment of myocardial tissue characteristics.