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Quantification of myocardial perfusion with self-gated cardiovascular magnetic resonance.

Likhite D, Adluru G, Hu N, McGann C, DiBella E - J Cardiovasc Magn Reson (2015)

Bottom Line: The gated and the self-gated datasets were then quantified with standard methods.Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar.Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Current myocardial perfusion measurements make use of an ECG-gated pulse sequence to track the uptake and washout of a gadolinium-based contrast agent. The use of a gated acquisition is a problem in situations with a poor ECG signal. Recently, an ungated perfusion acquisition was proposed but it is not known how accurately quantitative perfusion estimates can be made from such datasets that are acquired without any triggering signal.

Methods: An undersampled saturation recovery radial turboFLASH pulse sequence was used in 7 subjects to acquire dynamic contrast-enhanced images during free-breathing. A single saturation pulse was followed by acquisition of 4-5 slices after a delay of ~40 msec. This was repeated without pause and without any type of gating. The same pulse sequence, with ECG-gating, was used to acquire gated data as a ground truth. An iterative spatio-temporal constrained reconstruction was used to reconstruct the undersampled images. After reconstruction, the ungated images were retrospectively binned ("self-gated") into two cardiac phases using a region of interest based technique and deformably registered into near-systole and near-diastole. The gated and the self-gated datasets were then quantified with standard methods.

Results: Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar. Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different.

Conclusion: The self-gated technique for quantification of regional myocardial perfusion matched ECG-gated perfusion measurements well in normal subjects at rest. Self-gated systolic perfusion values matched ECG-gated perfusion values better than did diastolic values.

No MeSH data available.


A schematic representing the quantification process. The various steps involved include a) Segmentation of the myocardium b) Extraction of the tissue curves and the AIF and conversion to gadolinium concentration c) Fitting the curves to a 2-compartment model d) Display of the MBF values obtained.
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Fig5: A schematic representing the quantification process. The various steps involved include a) Segmentation of the myocardium b) Extraction of the tissue curves and the AIF and conversion to gadolinium concentration c) Fitting the curves to a 2-compartment model d) Display of the MBF values obtained.

Mentions: The myocardium was segmented out manually using custom software developed in MATLAB® (The Mathworks, Inc., Natick, MA). A single time frame was segmented and its epicardial and endocardial contours were copied to the remaining images in the time series. Figure 5a shows an example of the segmented myocardium from a single self-gated (systolic) slice from the dataset pool. The segmented myocardium was divided into six circumferential regions. Average signal intensity for each region was recorded over time to give the signal intensity (SI) timecurves. The prescribed flip angle (α = 10°-12°) was assumed to be correct and used in the approximation eq. (2) to obtain T1.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ T1=\frac{-SRT}{ \log \left(\frac{M_0 \sin \left(\alpha \right)-SI}{M_0 \sin \left(\alpha \right)}\right)} $$\end{document}T1=−SRTlogM0sinα−SIM0sinα


Quantification of myocardial perfusion with self-gated cardiovascular magnetic resonance.

Likhite D, Adluru G, Hu N, McGann C, DiBella E - J Cardiovasc Magn Reson (2015)

A schematic representing the quantification process. The various steps involved include a) Segmentation of the myocardium b) Extraction of the tissue curves and the AIF and conversion to gadolinium concentration c) Fitting the curves to a 2-compartment model d) Display of the MBF values obtained.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4325943&req=5

Fig5: A schematic representing the quantification process. The various steps involved include a) Segmentation of the myocardium b) Extraction of the tissue curves and the AIF and conversion to gadolinium concentration c) Fitting the curves to a 2-compartment model d) Display of the MBF values obtained.
Mentions: The myocardium was segmented out manually using custom software developed in MATLAB® (The Mathworks, Inc., Natick, MA). A single time frame was segmented and its epicardial and endocardial contours were copied to the remaining images in the time series. Figure 5a shows an example of the segmented myocardium from a single self-gated (systolic) slice from the dataset pool. The segmented myocardium was divided into six circumferential regions. Average signal intensity for each region was recorded over time to give the signal intensity (SI) timecurves. The prescribed flip angle (α = 10°-12°) was assumed to be correct and used in the approximation eq. (2) to obtain T1.2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ T1=\frac{-SRT}{ \log \left(\frac{M_0 \sin \left(\alpha \right)-SI}{M_0 \sin \left(\alpha \right)}\right)} $$\end{document}T1=−SRTlogM0sinα−SIM0sinα

Bottom Line: The gated and the self-gated datasets were then quantified with standard methods.Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar.Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different.

View Article: PubMed Central - PubMed

ABSTRACT

Background: Current myocardial perfusion measurements make use of an ECG-gated pulse sequence to track the uptake and washout of a gadolinium-based contrast agent. The use of a gated acquisition is a problem in situations with a poor ECG signal. Recently, an ungated perfusion acquisition was proposed but it is not known how accurately quantitative perfusion estimates can be made from such datasets that are acquired without any triggering signal.

Methods: An undersampled saturation recovery radial turboFLASH pulse sequence was used in 7 subjects to acquire dynamic contrast-enhanced images during free-breathing. A single saturation pulse was followed by acquisition of 4-5 slices after a delay of ~40 msec. This was repeated without pause and without any type of gating. The same pulse sequence, with ECG-gating, was used to acquire gated data as a ground truth. An iterative spatio-temporal constrained reconstruction was used to reconstruct the undersampled images. After reconstruction, the ungated images were retrospectively binned ("self-gated") into two cardiac phases using a region of interest based technique and deformably registered into near-systole and near-diastole. The gated and the self-gated datasets were then quantified with standard methods.

Results: Regional myocardial blood flow estimates (MBFs) obtained using self-gated systole (0.64 ± 0.26 ml/min/g), self-gated diastole (0.64 ± 0.26 ml/min/g), and ECG-gated scans (0.65 ± 0.28 ml/min/g) were similar. Based on the criteria for interchangeable methods listed in the statistical analysis section, the MBF values estimated from self-gated and gated methods were not significantly different.

Conclusion: The self-gated technique for quantification of regional myocardial perfusion matched ECG-gated perfusion measurements well in normal subjects at rest. Self-gated systolic perfusion values matched ECG-gated perfusion values better than did diastolic values.

No MeSH data available.