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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.


Schematic representation of the self-gating procedure. a) The LV and RV positions detected automatically with the rectangular ROI around the heart for self-gating. b) The 1D signal modulated by cardiac size change with a diastolic (peaks) and systolic (troughs) timeframe.
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Fig3: Schematic representation of the self-gating procedure. a) The LV and RV positions detected automatically with the rectangular ROI around the heart for self-gating. b) The 1D signal modulated by cardiac size change with a diastolic (peaks) and systolic (troughs) timeframe.

Mentions: The cropped region around the heart was selected automatically, by first locating a point in the left ventricle (LV) and in the right ventricle (RV). The RV and LV position were found by analyzing the time to peak of the signal intensity curves from the images automatically. For this, a maximum value image was generated with the maximum value among all time-frames at each pixel. Regional clusters were then created using connected component analysis. The regional cluster with the minimum time to peak was classified as the RV and the next regional cluster to peak was classified as the LV region. Figure 3a shows an example of the LV and RV positions found along with the region around the heart. The sum of all the pixels in the region gave a 1D signal as shown by Figure 3b. The curve represents the uptake and washout of contrast agent modulated by cardiac size changes and somewhat from respiratory motion when breathing changes the content of the cropped area. The high frequency component corresponds to changes in the size of the bright LV and RV blood pools in different cardiac phases. During diastole, the blood pools are larger, and thus give the peaks in Figure 3b. Similarly, systole gives a smaller sum in the region and thus a trough in the 1D signal. An animation is provided as an additional file to make this process clear [see Additional file 1]. The animation shows an ungated dataset and the corresponding 1D signal. The timeframes corresponding to the peaks in the curve are classified as the diastolic timeframes and those corresponding to the troughs are classified as systolic timeframes. The remaining timeframes are classified as near-systolic timeframes or near-diastolic timeframes depending upon their proximity to a systolic or diastolic timeframe. All of the timeframes are used in this process. This process was automatic and resulted in two datasets, one with frames closer to systole and one with frames closer to diastole.Figure 3


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)

Schematic representation of the self-gating procedure. a) The LV and RV positions detected automatically with the rectangular ROI around the heart for self-gating. b) The 1D signal modulated by cardiac size change with a diastolic (peaks) and systolic (troughs) timeframe.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Fig3: Schematic representation of the self-gating procedure. a) The LV and RV positions detected automatically with the rectangular ROI around the heart for self-gating. b) The 1D signal modulated by cardiac size change with a diastolic (peaks) and systolic (troughs) timeframe.
Mentions: The cropped region around the heart was selected automatically, by first locating a point in the left ventricle (LV) and in the right ventricle (RV). The RV and LV position were found by analyzing the time to peak of the signal intensity curves from the images automatically. For this, a maximum value image was generated with the maximum value among all time-frames at each pixel. Regional clusters were then created using connected component analysis. The regional cluster with the minimum time to peak was classified as the RV and the next regional cluster to peak was classified as the LV region. Figure 3a shows an example of the LV and RV positions found along with the region around the heart. The sum of all the pixels in the region gave a 1D signal as shown by Figure 3b. The curve represents the uptake and washout of contrast agent modulated by cardiac size changes and somewhat from respiratory motion when breathing changes the content of the cropped area. The high frequency component corresponds to changes in the size of the bright LV and RV blood pools in different cardiac phases. During diastole, the blood pools are larger, and thus give the peaks in Figure 3b. Similarly, systole gives a smaller sum in the region and thus a trough in the 1D signal. An animation is provided as an additional file to make this process clear [see Additional file 1]. The animation shows an ungated dataset and the corresponding 1D signal. The timeframes corresponding to the peaks in the curve are classified as the diastolic timeframes and those corresponding to the troughs are classified as systolic timeframes. The remaining timeframes are classified as near-systolic timeframes or near-diastolic timeframes depending upon their proximity to a systolic or diastolic timeframe. All of the timeframes are used in this process. This process was automatic and resulted in two datasets, one with frames closer to systole and one with frames closer to diastole.Figure 3

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.