Comparison of Myocardial Blood Flow Quantification Models for Double ECG Gating Arterial Spin Labeling MRI: Reproducibility Assessment
Background Arterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart rate variability than single‐ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quan...
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Published in | Journal of magnetic resonance imaging Vol. 60; no. 4; pp. 1577 - 1588 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken, USA
John Wiley & Sons, Inc
01.10.2024
Wiley Subscription Services, Inc |
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ISSN | 1053-1807 1522-2586 1522-2586 |
DOI | 10.1002/jmri.29220 |
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Abstract | Background
Arterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart rate variability than single‐ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency‐offset‐corrected‐inversion (FOCI) pulses provide sharper edge profiles than hyperbolic‐secant (HS), which could benefit myocardial ASL.
Purpose
To assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses.
Study Type
Prospective.
Subjects
Sixteen subjects (27 ± 8 years).
Field Strength/Sequence
1.5 T/DG and SG flow‐sensitive alternating inversion recovery ASL.
Assessment
Three models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal‐to‐noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects.
Statistical Tests
Within‐subject coefficient of variation, analysis of variance. P‐value <0.05 was considered significant.
Results
MBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min).
Data Conclusion
Reproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model.
Level of Evidence
2
Technical Efficacy
Stage 1 |
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AbstractList | BackgroundArterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart rate variability than single‐ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency‐offset‐corrected‐inversion (FOCI) pulses provide sharper edge profiles than hyperbolic‐secant (HS), which could benefit myocardial ASL.PurposeTo assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses.Study TypeProspective.SubjectsSixteen subjects (27 ± 8 years).Field Strength/Sequence1.5 T/DG and SG flow‐sensitive alternating inversion recovery ASL.AssessmentThree models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal‐to‐noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects.Statistical TestsWithin‐subject coefficient of variation, analysis of variance. P‐value <0.05 was considered significant.ResultsMBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min).Data ConclusionReproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model.Level of Evidence2Technical EfficacyStage 1 Background Arterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart rate variability than single‐ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency‐offset‐corrected‐inversion (FOCI) pulses provide sharper edge profiles than hyperbolic‐secant (HS), which could benefit myocardial ASL. Purpose To assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses. Study Type Prospective. Subjects Sixteen subjects (27 ± 8 years). Field Strength/Sequence 1.5 T/DG and SG flow‐sensitive alternating inversion recovery ASL. Assessment Three models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal‐to‐noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects. Statistical Tests Within‐subject coefficient of variation, analysis of variance. P‐value <0.05 was considered significant. Results MBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min). Data Conclusion Reproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model. Level of Evidence 2 Technical Efficacy Stage 1 Arterial spin labeling (ASL) allows non-invasive quantification of myocardial blood flow (MBF). Double-ECG gating (DG) ASL is more robust to heart rate variability than single-ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency-offset-corrected-inversion (FOCI) pulses provide sharper edge profiles than hyperbolic-secant (HS), which could benefit myocardial ASL.BACKGROUNDArterial spin labeling (ASL) allows non-invasive quantification of myocardial blood flow (MBF). Double-ECG gating (DG) ASL is more robust to heart rate variability than single-ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency-offset-corrected-inversion (FOCI) pulses provide sharper edge profiles than hyperbolic-secant (HS), which could benefit myocardial ASL.To assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses.PURPOSETo assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses.Prospective.STUDY TYPEProspective.Sixteen subjects (27 ± 8 years).SUBJECTSSixteen subjects (27 ± 8 years).1.5 T/DG and SG flow-sensitive alternating inversion recovery ASL.FIELD STRENGTH/SEQUENCE1.5 T/DG and SG flow-sensitive alternating inversion recovery ASL.Three models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal-to-noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects.ASSESSMENTThree models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal-to-noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects.Within-subject coefficient of variation, analysis of variance. P-value <0.05 was considered significant.STATISTICAL TESTSWithin-subject coefficient of variation, analysis of variance. P-value <0.05 was considered significant.MBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min).RESULTSMBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min).Reproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model.DATA CONCLUSIONReproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model.2 TECHNICAL EFFICACY: Stage 1.LEVEL OF EVIDENCE2 TECHNICAL EFFICACY: Stage 1. Arterial spin labeling (ASL) allows non-invasive quantification of myocardial blood flow (MBF). Double-ECG gating (DG) ASL is more robust to heart rate variability than single-ECG gating (SG), but its reproducibility requires further investigation. Moreover, the existence of multiple quantification models hinders its application. Frequency-offset-corrected-inversion (FOCI) pulses provide sharper edge profiles than hyperbolic-secant (HS), which could benefit myocardial ASL. To assess the performance of MBF quantification models for DG compared to SG ASL, to evaluate their reproducibility and to compare the effects of HS and FOCI pulses. Prospective. Sixteen subjects (27 ± 8 years). 1.5 T/DG and SG flow-sensitive alternating inversion recovery ASL. Three models for DG MBF quantification were compared using Monte Carlo simulations and in vivo experiments. Two models used a fitting approach (one using only a single label and control image pair per fit, the other using all available image pairs), while the third model used a T1 correction approach. Slice profile simulations were conducted for HS and FOCI pulses with varying B0 and B1. Temporal signal-to-noise ratio (tSNR) was computed for different acquisition/quantification strategies and inversion pulses. The number of images that minimized MBF error was investigated in the model with highest tSNR. Intra and intersession reproducibility were assessed in 10 subjects. Within-subject coefficient of variation, analysis of variance. P-value <0.05 was considered significant. MBF was not different across acquisition/quantification strategies (P = 0.27) nor pulses (P = 0.9). DG MBF quantification models exhibited significantly higher tSNR and superior reproducibility, particularly for the fitting model using multiple images (tSNR was 3.46 ± 2.18 in vivo and 3.32 ± 1.16 in simulations, respectively; wsCV = 16%). Reducing the number of ASL pairs to 13/15 did not increase MBF error (minimum = 0.22 mL/g/min). Reproducibility of MBF was better for DG than SG acquisitions, especially when employing a fitting model. 2 TECHNICAL EFFICACY: Stage 1. |
Author | Bastarrika, Gorka Solís‐Barquero, Sergio M. Aramendía‐Vidaurreta, Verónica Ezponda, Ana Vidorreta, Marta Echeverria‐Chasco, Rebeca Fernández‐Seara, María A. |
Author_xml | – sequence: 1 givenname: Verónica orcidid: 0000-0001-9834-2866 surname: Aramendía‐Vidaurreta fullname: Aramendía‐Vidaurreta, Verónica organization: Instituto de Investigación Sanitaria de Navarra – sequence: 2 givenname: Sergio M. orcidid: 0000-0002-2513-0747 surname: Solís‐Barquero fullname: Solís‐Barquero, Sergio M. organization: Instituto de Investigación Sanitaria de Navarra – sequence: 3 givenname: Marta surname: Vidorreta fullname: Vidorreta, Marta organization: Siemens Healthcare – sequence: 4 givenname: Ana surname: Ezponda fullname: Ezponda, Ana organization: Instituto de Investigación Sanitaria de Navarra – sequence: 5 givenname: Rebeca orcidid: 0000-0003-0199-2593 surname: Echeverria‐Chasco fullname: Echeverria‐Chasco, Rebeca organization: Instituto de Investigación Sanitaria de Navarra – sequence: 6 givenname: Gorka surname: Bastarrika fullname: Bastarrika, Gorka organization: Instituto de Investigación Sanitaria de Navarra – sequence: 7 givenname: María A. orcidid: 0000-0001-8536-6295 surname: Fernández‐Seara fullname: Fernández‐Seara, María A. email: mfseara@unav.es organization: Instituto de Investigación Sanitaria de Navarra |
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CitedBy_id | crossref_primary_10_1002_jmri_29243 |
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Arterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart... Arterial spin labeling (ASL) allows non-invasive quantification of myocardial blood flow (MBF). Double-ECG gating (DG) ASL is more robust to heart rate... BackgroundArterial spin labeling (ASL) allows non‐invasive quantification of myocardial blood flow (MBF). Double‐ECG gating (DG) ASL is more robust to heart... |
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SubjectTerms | adiabatic inversion pulses arterial spin labeling Blood flow Coefficient of variation Data acquisition EKG Error analysis Error correction Field strength Gating Heart rate Image acquisition Image processing In vivo methods and tests Labeling Labels Monte Carlo simulation myocardial perfusion Performance assessment quantification model Reproducibility Simulation Spin labeling Statistical analysis Statistical models Statistical tests Variance analysis |
Title | Comparison of Myocardial Blood Flow Quantification Models for Double ECG Gating Arterial Spin Labeling MRI: Reproducibility Assessment |
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