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Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification

View Article: PubMed Central - PubMed

ABSTRACT

Background: Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium.

Methods: A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress.

Results: For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively.

Conclusions: A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.

Electronic supplementary material: The online version of this article (doi:10.1186/s12968-017-0355-5) contains supplementary material, which is available to authorized users.

No MeSH data available.


Measured [Gd] vs true [Gd] for phantoms estimated with and without T2* correction for the AIF protocol with the line of identity shown as dotted black line
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Fig7: Measured [Gd] vs true [Gd] for phantoms estimated with and without T2* correction for the AIF protocol with the line of identity shown as dotted black line

Mentions: Measurement of r1 and r2 relaxivities was made for both Gadobutrol (Gadovist®) and Gadoterate meglumine (Dotarem®) doped saline phantoms. For Gadobutrol, the measured values for r1 and r2 were 5.5 L/mmol/s and 6.8 L/mmol/s, respectively and for Gadoterate meglumine the measured values were 4.6 L/mmol/s and 5.7 L/mmol/s, respectively. The measured [Gd] versus actual [Gd] is shown with and without T2* correction (Fig. 7). The linear fit for Gadobutrol was [Gd]estimate = 0.99 [Gd] + 0.0002 with T2* correction and was [Gd]estimate = 0.90 [Gd] + 0.08, without T2* correction. The linear fit for Gadoterate meglumine was [Gd]estimate = 1.02 [Gd] + 0.07 with T2* correction and was [Gd]estimate = 0.94 [Gd] + 0.12, without T2* correction. Measurements of [Gd] for the myocardial signal protocol are shown in Fig. 8 for TS = 95 ms. For TS = 95 ms the fits were 1.004[Gd] + 0.005 and 1.04[Gd] + 0.01 for SSFP protocol with Gadobutrol and Gadoterate meglumine, respectively, and were 0.96[Gd] + 0.01 and 0.96[Gd] + 0.02 for the FLASH protocol with Gadobutrol and Gadoterate meglumine, respectively. The measurements were made for TS = 65 to 125 ms in steps of 10 ms. For SSFP, the slopes of the fits were within 4% of unity slope for all TS values and both agents and for FLASH were within 5%.Fig. 7


Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
Measured [Gd] vs true [Gd] for phantoms estimated with and without T2* correction for the AIF protocol with the line of identity shown as dotted black line
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5383963&req=5

Fig7: Measured [Gd] vs true [Gd] for phantoms estimated with and without T2* correction for the AIF protocol with the line of identity shown as dotted black line
Mentions: Measurement of r1 and r2 relaxivities was made for both Gadobutrol (Gadovist®) and Gadoterate meglumine (Dotarem®) doped saline phantoms. For Gadobutrol, the measured values for r1 and r2 were 5.5 L/mmol/s and 6.8 L/mmol/s, respectively and for Gadoterate meglumine the measured values were 4.6 L/mmol/s and 5.7 L/mmol/s, respectively. The measured [Gd] versus actual [Gd] is shown with and without T2* correction (Fig. 7). The linear fit for Gadobutrol was [Gd]estimate = 0.99 [Gd] + 0.0002 with T2* correction and was [Gd]estimate = 0.90 [Gd] + 0.08, without T2* correction. The linear fit for Gadoterate meglumine was [Gd]estimate = 1.02 [Gd] + 0.07 with T2* correction and was [Gd]estimate = 0.94 [Gd] + 0.12, without T2* correction. Measurements of [Gd] for the myocardial signal protocol are shown in Fig. 8 for TS = 95 ms. For TS = 95 ms the fits were 1.004[Gd] + 0.005 and 1.04[Gd] + 0.01 for SSFP protocol with Gadobutrol and Gadoterate meglumine, respectively, and were 0.96[Gd] + 0.01 and 0.96[Gd] + 0.02 for the FLASH protocol with Gadobutrol and Gadoterate meglumine, respectively. The measurements were made for TS = 65 to 125 ms in steps of 10 ms. For SSFP, the slopes of the fits were within 4% of unity slope for all TS values and both agents and for FLASH were within 5%.Fig. 7

View Article: PubMed Central - PubMed

ABSTRACT

Background: Quantification of myocardial blood flow requires knowledge of the amount of contrast agent in the myocardial tissue and the arterial input function (AIF) driving the delivery of this contrast agent. Accurate quantification is challenged by the lack of linearity between the measured signal and contrast agent concentration. This work characterizes sources of non-linearity and presents a systematic approach to accurate measurements of contrast agent concentration in both blood and myocardium.

Methods: A dual sequence approach with separate pulse sequences for AIF and myocardial tissue allowed separate optimization of parameters for blood and myocardium. A systems approach to the overall design was taken to achieve linearity between signal and contrast agent concentration. Conversion of signal intensity values to contrast agent concentration was achieved through a combination of surface coil sensitivity correction, Bloch simulation based look-up table correction, and in the case of the AIF measurement, correction of T2* losses. Validation of signal correction was performed in phantoms, and values for peak AIF concentration and myocardial flow are provided for 29 normal subjects for rest and adenosine stress.

Results: For phantoms, the measured fits were within 5% for both AIF and myocardium. In healthy volunteers the peak [Gd] was 3.5 ± 1.2 for stress and 4.4 ± 1.2 mmol/L for rest. The T2* in the left ventricle blood pool at peak AIF was approximately 10 ms. The peak-to-valley ratio was 5.6 for the raw signal intensities without correction, and was 8.3 for the look-up-table (LUT) corrected AIF which represents approximately 48% correction. Without T2* correction the myocardial blood flow estimates are overestimated by approximately 10%. The signal-to-noise ratio of the myocardial signal at peak enhancement (1.5 T) was 17.7 ± 6.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. The estimated perfusion flow was 3.9 ± 0.38 and 1.03 ± 0.19 ml/min/g using the BTEX model and 3.4 ± 0.39 and 0.95 ± 0.16 using a Fermi model, for stress and rest, respectively.

Conclusions: A dual sequence for myocardial perfusion cardiovascular magnetic resonance and AIF measurement has been optimized for quantification of myocardial blood flow. A validation in phantoms was performed to confirm that the signal conversion to gadolinium concentration was linear. The proposed sequence was integrated with a fully automatic in-line solution for pixel-wise mapping of myocardial blood flow and evaluated in adenosine stress and rest studies on N = 29 normal healthy subjects. Reliable perfusion mapping was demonstrated and produced estimates with low variability.

Electronic supplementary material: The online version of this article (doi:10.1186/s12968-017-0355-5) contains supplementary material, which is available to authorized users.

No MeSH data available.