Open-source cardiac magnetic resonance fingerprinting.

Area of Science:

• Medical Imaging
• Quantitative MRI
• Biophysics

Background:

• Cardiac magnetic resonance fingerprinting (cMRF) offers valuable multi-parametric diagnostic information.
• Quantitative MRI techniques are crucial for precise tissue characterization.
• The need for accessible and reproducible quantitative imaging tools is growing.

Purpose of the Study:

• To introduce an open-source framework for cardiac MRF.
• To provide open-source pulse sequences, reconstruction, and parameter estimation tools.
• To facilitate reproducible quantitative MRI data processing.

Main Methods:

• A 2D cMRF sequence using Pulseq with spiral readout and cardiac triggering was implemented.
• T1 and T2 preparation pulses were incorporated for accurate parameter estimation.
• Phantom and in vivo experiments were conducted on multiple scanners for validation.

Main Results:

• Phantom experiments showed comparable T1 and T2 error (4.50 ± 2.48%) across four scanners.
• In vivo T1 and T2 maps from the open-source cMRF were comparable to vendor-specific implementations.
• The framework demonstrated accurate parameter estimation across different hardware.

Conclusions:

• The developed open-source cMRF framework enables accurate quantitative parameter estimation.
• Reproducible quantitative MRI is achievable with the provided open-source tools.
• This framework promotes wider adoption and validation of cMRF techniques.