Lexiaozi Fan1,2, Bradley D Allen1, Austin E Culver3, Li-Yueh Hsu4, Kyungpyo Hong1, Brandon C Benefield3, James C Carr1, Daniel C Lee3, Daniel Kim1,2. 1. Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 2. Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, USA. 3. Division of Cardiology, Internal Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA. 4. Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Maryland, USA.
Abstract
PURPOSE: To develop and evaluate a flexible, Bloch-equation based framework for retrospective T 2 ∗ correction to the arterial input function (AIF) obtained with quantitative cardiac perfusion pulse sequences. METHODS: Our framework initially calculates the gadolinium concentration [Gd] based on T1 measurements alone. Next, T 2 ∗ is estimated from this initial calculation of [Gd] while assuming fast water exchange and using the literature native T2 and static magnetic field variation (ΔB0 ) values. Finally, the [Gd] is recalculated after performing T 2 ∗ correction to the Bloch equation signal model. Using this approach, we performed T 2 ∗ correction to historical phantom and in vivo, dual-imaging perfusion data sets from 3 different patient groups obtained using different pulse sequences and imaging parameters. Images were processed to quantify both the AIF and resting myocardial blood flow (MBF). We also performed a sensitivity analysis of our T 2 ∗ correction to ±20% variations in native T2 and ΔB0 . RESULTS: Compared with the ground truth [Gd] of phantom, the normalized root-means-square-error (NRMSE) in measured [Gd] was 5.1%, 1.3%, and 0.6% for uncorrected, our corrected, and Kellman's corrected, respectively. For in vivo data, both the peak AIF (7.0 ± 3.0 mM vs. 8.6 ± 7.1 mM, 7.2 ± 0.9 mM vs. 8.6 ± 1.7 mM, 7.7 ± 1.8 mM vs. 10.3 ± 5.1 mM, P < .001) and resting MBF (1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.2 ± 0.1 mL/g/min vs. 0.9 ± 0.1 mL/g/min, P < .001) values were significantly different between uncorrected and corrected for all 3 patient groups. Both the peak AIF and resting MBF values varied by <5% over the said variations in native T2 and ΔB0 . CONCLUSION: Our theoretical framework enables retrospective T 2 ∗ correction to the AIF obtained with dual-imaging, cardiac perfusion pulse sequences.
PURPOSE: To develop and evaluate a flexible, Bloch-equation based framework for retrospective T 2 ∗ correction to the arterial input function (AIF) obtained with quantitative cardiac perfusion pulse sequences. METHODS: Our framework initially calculates the gadolinium concentration [Gd] based on T1 measurements alone. Next, T 2 ∗ is estimated from this initial calculation of [Gd] while assuming fast water exchange and using the literature native T2 and static magnetic field variation (ΔB0 ) values. Finally, the [Gd] is recalculated after performing T 2 ∗ correction to the Bloch equation signal model. Using this approach, we performed T 2 ∗ correction to historical phantom and in vivo, dual-imaging perfusion data sets from 3 different patient groups obtained using different pulse sequences and imaging parameters. Images were processed to quantify both the AIF and resting myocardial blood flow (MBF). We also performed a sensitivity analysis of our T 2 ∗ correction to ±20% variations in native T2 and ΔB0 . RESULTS: Compared with the ground truth [Gd] of phantom, the normalized root-means-square-error (NRMSE) in measured [Gd] was 5.1%, 1.3%, and 0.6% for uncorrected, our corrected, and Kellman's corrected, respectively. For in vivo data, both the peak AIF (7.0 ± 3.0 mM vs. 8.6 ± 7.1 mM, 7.2 ± 0.9 mM vs. 8.6 ± 1.7 mM, 7.7 ± 1.8 mM vs. 10.3 ± 5.1 mM, P < .001) and resting MBF (1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.2 ± 0.1 mL/g/min vs. 0.9 ± 0.1 mL/g/min, P < .001) values were significantly different between uncorrected and corrected for all 3 patient groups. Both the peak AIF and resting MBF values varied by <5% over the said variations in native T2 and ΔB0 . CONCLUSION: Our theoretical framework enables retrospective T 2 ∗ correction to the AIF obtained with dual-imaging, cardiac perfusion pulse sequences.
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