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. <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_1$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> inversion and <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_2$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> 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 <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_1$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_2$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> mapping of the heart. Three volunteers were imaged on two different scanners. Results The error of <InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_1$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_2$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> over all tissue types present in the T1MES phantom was comparable between all four scanners and on average 4.50 ± 2.48%. <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq1.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_1$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation> and <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="10334_2025_1269_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="17" /> </InlineMediaObject> <EquationSource Format="TEX">$T_2$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>T</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> 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.

Open-source cardiac magnetic resonance fingerprinting

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2025年6月21日

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Patrick Schuenke,
Catarina Redshaw Kranich,
Max Lutz,
Jakob Schattenfroh,
Matthias Anders,
Philine Reisdorf,
Jeanette Schulz-Menger,
Ingolf Sack,
Jesse Hamilton,
Nicole Seiberlich,
Christoph Kolbitsch

摘要

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. $T_1$ $T_{1}$ inversion and $T_2$ $T_{2}$ 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 $T_1$ $T_{1}$ and $T_2$ $T_{2}$ mapping of the heart. Three volunteers were imaged on two different scanners.

Results

The error of $T_1$ $T_{1}$ and $T_2$ $T_{2}$ over all tissue types present in the T1MES phantom was comparable between all four scanners and on average 4.50 ± 2.48%. $T_1$ $T_{1}$ and $T_2$ $T_{2}$ 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.