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Scanner-Independent MyoMapNet for Accelerated Cardiac MRI T1 Mapping Across Vendors and Field Strengths.

Amine Amyar1, Ahmed S Fahmy1, Rui Guo1

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Summary

This study developed a scanner-independent deep learning model for accelerated cardiac T1 mapping. The new model accurately estimates T1 values across different MRI vendors and field strengths, improving efficiency in cardiac imaging.

Keywords:
deep learninginversion-recovery cardiac T1 mappingmyocardial tissue characterization

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Area of Science:

  • Cardiovascular Magnetic Resonance Imaging
  • Artificial Intelligence in Medical Imaging
  • Quantitative Cardiovascular Assessment

Background:

  • Cardiac T1 mapping estimates T1 values using T1-weighted images and numerical fitting.
  • Reducing scan time by acquiring fewer images compromises precision and accuracy.
  • Existing neural network approaches lack generalizability across different MRI vendors and field strengths.

Purpose of the Study:

  • To develop and evaluate an accelerated cardiac T1 mapping method using a deep learning approach (MyoMapNet).
  • To enhance MyoMapNet's generalizability across vendors and field strengths by incorporating scanner information as inputs.
  • To enable faster and more accurate T1 quantification in cardiac MRI.

Main Methods:

  • Developed Scanner-Independent MyoMapNet (SI-MyoMapNet) by modifying the deep learning architecture to accept vendor and field strength as inputs.
  • Utilized a multicenter dataset of 1423 patients with cardiac disease across two vendors (Siemens, Philips) and two field strengths (1.5T, 3T).
  • Compared SI-MyoMapNet T1 values (from 4 images) against conventional Modified Look-Locker inversion recovery (MOLLI) T1 values (from 8-11 images).

Main Results:

  • SI-MyoMapNet successfully generated T1 maps across different vendors and field strengths.
  • Strong correlations (r > 0.86) were observed between SI-MyoMapNet and MOLLI for native and postcontrast T1 values.
  • Excellent agreement was found for myocardial and blood pool T1 values, with small mean differences and narrow confidence intervals.

Conclusions:

  • Incorporating field strength and vendor information into the deep learning architecture enables MyoMapNet generalizability.
  • SI-MyoMapNet offers an accelerated and robust approach for cardiac T1 mapping across diverse MRI systems.
  • This advancement holds potential for improving the efficiency and consistency of quantitative cardiac MRI.