Open-source cardiac magnetic resonance fingerprinting.

Purpose: Cardiac magnetic resonance fingerprinting (cMRF) is a powerful quantitative imaging technique that provides multi-parametric diagnostic information. Here, we introduce an open-source framework for cardiac MRF including open-source pulse sequences, image reconstruction, and parameter estimation tools that are needed for the processing of the data.

Methods: A 2D cMRF sequence with a variable-density spiral readout is implemented using the open-source and vendor-agnostic sequence format Pulseq. Cardiac triggering is used to synchronize acquisition with the rest period of the heart. $T_1$ inversion and $T_2$ preparation pulses are added to ensure accurate parameter estimation. Data acquisition is carried out over 15 heartbeats. The images showing the signal changes over time are reconstructed and matched to a pre-calculated signal dictionary. In addition to the cMRF sequence, spin-echo reference sequences for quality control in phantoms are provided. The method is evaluated in phantom experiments using a T1MES phantom on four different scanners. In vivo experiments were performed to compare the open-source cMRF sequence with a vendor-specific cMRF sequence and clinical sequences used for $T_1$ and $T_2$ mapping of the heart. Three volunteers were imaged on two different scanners.

Results: The error of $T_1$ and $T_2$ over all tissue types present in the T1MES phantom was comparable between all four scanners and on average 4.50 ± 2.48%. $T_1$ and $T_2$ maps obtained in vivo were comparable between the open-source and vendor-specific implementation of cMRF.

Conclusion: The proposed open-source cMRF implementation enables accurate parameter estimation across multiple different scanners. Sequence files, image reconstruction, and parameter estimation scripts are available for reproducible quantitative MRI.