Limits...
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.


Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig13: Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction

Mentions: The SNR of the myocardial time intensity was measured on a pixel-wise basis using SNR scaled reconstruction and measured at peak myocardial enhancement. Peak myocardial SNR was 21.8 ± 7.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. Example of myocardial stress perfusion images are shown before and after normalization (Fig. 11) and myocardial blood flow maps are shown in Fig. 12. Images are well saturated as observed at baseline. Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction is shown in Fig. 13. Without T2* correction the myocardial perfusion estimates of blood flow are overestimated by 10%. Estimates of perfusion stress flow using the BTEX model was 3.93 ± 0.38 and rest flow was 1.03 ± 0.19 ml/min/g (N = 29). Estimates for extraction fraction were 0.5 ± 0.04 and 0.85 ± 0.03, at stress and rest, respectively. Estimates of the permeability surface area product (PS) were 1.55 ± 0.2 and 1.33 ± 0.21 (ml/min/g), at stress and rest, respectively. Estimates for the interstitial volume fraction (%) were 27.4 ± 5.9 and 24.8 ± 5.9, at stress and rest, respectively. Estimates for the blood volume fraction (ml/g) were 13.0 ± 0.85 and 9.2 ± 0.76, at stress and rest, respectively. Estimates of perfusion flow using the Fermi model fit over the 1st pass were 3.4 ± 0.39 and 0.95 ± 0.16 ml/min/g, at stress and rest, respectively.Fig. 11


Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification
Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig13: Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction
Mentions: The SNR of the myocardial time intensity was measured on a pixel-wise basis using SNR scaled reconstruction and measured at peak myocardial enhancement. Peak myocardial SNR was 21.8 ± 7.6 at stress and the peak [Gd] was 0.49 ± 0.15 mmol/L. Example of myocardial stress perfusion images are shown before and after normalization (Fig. 11) and myocardial blood flow maps are shown in Fig. 12. Images are well saturated as observed at baseline. Influence of T2* correction on flow comparing myocardial blood flow estimates with and without T2* correction is shown in Fig. 13. Without T2* correction the myocardial perfusion estimates of blood flow are overestimated by 10%. Estimates of perfusion stress flow using the BTEX model was 3.93 ± 0.38 and rest flow was 1.03 ± 0.19 ml/min/g (N = 29). Estimates for extraction fraction were 0.5 ± 0.04 and 0.85 ± 0.03, at stress and rest, respectively. Estimates of the permeability surface area product (PS) were 1.55 ± 0.2 and 1.33 ± 0.21 (ml/min/g), at stress and rest, respectively. Estimates for the interstitial volume fraction (%) were 27.4 ± 5.9 and 24.8 ± 5.9, at stress and rest, respectively. Estimates for the blood volume fraction (ml/g) were 13.0 ± 0.85 and 9.2 ± 0.76, at stress and rest, respectively. Estimates of perfusion flow using the Fermi model fit over the 1st pass were 3.4 ± 0.39 and 0.95 ± 0.16 ml/min/g, at stress and rest, respectively.Fig. 11

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.